using the lyrics database schema in mysql provided below.
1.)List the artist name of the artists who do not have a webaddress and their leadsource is “Directmail”?
2.)List the names of members in the artist called 'Today'.
3.)Report the total runtime in minutes FOR EACH album in the Titles table.
4.)List the firstname, lastname of members who are represented by the salesperson “Lisa Williams”
5.)List EACH salesperson’s firstname along with the number of Members that EACH SalesPerson represents.
below is the lyric schema used on this assigment for mysql. copy paste it in the mysql console
DROP TABLES IF EXISTS Artists,Genre, Members, Titles,
Tracks,SalesPeople,Studios,XrefArtistsMembers;
DROP TABLES IF EXISTS
Authors,Publishers,Titles,Title_Authors,Royalties;
DROP TABLES IF EXISTS
Products,Customers,Orders,Order_details;
DROP TABLES IF EXISTS Sailors,Boats,Reserves;
CREATE TABLE Artists (
ArtistID int,
ArtistName varchar (50) NOT NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
Country varchar (20) NULL ,
WebAddress varchar (40) NULL ,
EntryDate date NULL ,
LeadSource varchar (10) NULL
);
Insert Into Artists Values(1,'The
Neurotics','Peterson','NC','USA','www.theneurotics.com','2003-05-14','Directmail');
Insert Into Artists Values(2,'Louis Holiday','Clinton','IL','USA'
,NULL,'2003-06-03','Directmail');
Insert Into Artists
Values(3,'Word','Anderson','IN','USA',NULL,'2003-06-08','Email');
Insert Into Artists
Values(5,'Sonata','Alexandria','VA','USA','www.classical.com/sonata','2003-06-08','Ad');
Insert Into Artists Values(10,'The
Bullets','Alverez','TX','USA',NULL,'2003-08-10','Email');
Insert Into Artists Values(14,'Jose MacArthur','Santa
Rosa','CA','USA','www.josemacarthur.com','2003-08-17','Ad');
Insert Into Artists Values(15,'Confused','Tybee
Island','GA','USA',Null,'2003-09-14','Directmail');
Insert Into Artists Values(17,'The Kicks','New
Rochelle','NY','USA',NULL,'2003-12-03','Ad');
Insert Into Artists
Values(16,'Today','London','ONT','Canada','www.today.com','2003-10-07','Email');
Insert Into Artists Values(18,'21 West
Elm','Alamaba','VT','USA','www.21westelm.com','2003-02-05','Ad');
Insert Into Artists
Values(11,'Highlander','Columbus','OH','USA',NULL,'2002-08-10','Email');
CREATE TABLE Genre (
Genre varchar (15)
);
Insert into Genre Values('alternative');
Insert into Genre Values('classical');
Insert into Genre Values('jazz');
Insert into Genre Values('metal');
Insert into Genre Values('R&B');
Insert into Genre Values('rap');
Insert into Genre Values('pop');
CREATE TABLE Members (
MemberID int ,
FirstName varchar (25) NULL ,
LastName varchar (25) NULL ,
Address varchar (60) NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
PostalCode varchar (10) NULL ,
Country varchar (20) NULL ,
HomePhone varchar (16) NULL ,
WorkPhone varchar (16) NULL ,
EMail varchar (40) NULL ,
Gender char (1) NULL ,
Birthday date NULL ,
SalesID smallint NULL
);
Insert Into Members Values(10,'Roberto','Alvarez','Rt
1','Anderson','IN','46019','USA','7651552983','7651628837','[email protected]','M','1968-01-18',2);
Insert Into Members Values(31,'Jose','MacArthur','51444
Vine','Santa
Rosa','CA','99999','USA','6331289393',Null,'[email protected]','M','1978-06-24',1);
Insert Into Members Values(13,'Mary','Chrisman','1772 East
117th','Fishers','IN','46123','USA','3171820387',Null,'[email protected]','F','1973-03-01',1);
Insert Into Members Values(15,'Warren','Boyer','167 Alamo
Dr','Alverez','TX','75601','USA','8221722883',Null,'[email protected]','M','1969-04-19',2);
Insert Into Members Values(32,'Doug','Finney','2020
Dubois','Savannah','GA','30003','USA','9821222929',Null,'[email protected]','M','1963-08-04',3);
Insert Into Members Values(19,'Terry','Irving','18a 7th St','Tybee
Island','GA','30004','USA','5411252093',Null,Null,'M','1959-06-22',3);
Insert Into Members Values(21,'Michelle','Henderson','201
Bonaventure','Savannah','GA','30005','USA','8221928273',Null,Null,'F','1964-03-15',2);
Insert Into Members Values(34,'William','Morrow','PO Box 1882','New
Rochelle','NY','10014','USA','9981722928',Null,'[email protected]','M','1965-03-17',2);
Insert Into Members Values(29,'Frank','Payne','5412 Clinton','New
Rochelle','NY','10014','USA','9981737464',Null,Null,'M','1960-01-17',1);
Insert Into Members Values(35,'Aiden','Franks','167 East
38th','Alverez','TX','75601','USA','8321729283','8321723833','[email protected]','M','1983-09-02',2);
Insert Into Members Values(3,'Bryce','Sanders','PO Box
1292','Peterson','NC','27104','USA','6441824283',Null,'[email protected]','M','1966-06-11',2);
Insert Into Members Values(14,'Carol','Wanner','787 Airport
Rd','Alverez','TX','75601','USA','6831223944',Null,Null,'F','1978-11-08',3);
Insert Into Members Values(33,'Brian','Ranier','23 Gregory
Lane','London','ONT','M6Y 2Y7
','Canada','6231842933',Null,Null,'M','1957-10-19',3);
Insert Into Members Values(7,'Marcellin','Lambert','142 Sample
Rd','Alexandria','VA','20102','USA','8331929302',Null,'[email protected]','M','1959-11-14',3);
Insert Into Members Values(8,'Caroline','Kale','1515 Stone Church
Rd','Allen','VA','20321','USA','7321223742',Null,Null,'F','1956-05-30',3);
Insert Into Members Values(9,'Kerry','Fernandez','15
Midway','Lynchberg','VA','21223','USA','2211229384','2211223939',Null,'M','1962-01-16',1);
Insert Into Members Values(26,'Tony','Wong','115 Maple
St','McKensie','ONT','M8H
3T1','Canada','3311692832','3311692822','[email protected]','M','1955-11-01',2);
Insert Into Members
Values(18,'Bonnie','Taft','RR4','Alamaba','VT','05303','USA','3721223292',Null,'[email protected]','F','1960-09-21',1);
Insert Into Members Values(20,'Louis','Holiday','15 Davis
Ct','Clinton','IL','63882','USA','1451223838',Null,Null,'M','1969-07-27',2);
Insert Into Members
Values(22,'Bobby','Crum','RR2','Pine','VT','05412','USA','1831828211',Null,Null,'M','1965-06-10',3);
Insert Into Members Values(28,'Vic','Cleaver','100
Maple','Reston','VT','05544','USA','8111839292',Null,Null,'M','1957-02-10',2);
Insert Into Members Values(30,'Roberto','Goe','14 Gray
Rd','Columbus','OH','48110','USA','2771123943',Null,Null,'M','1967-09-12',1);
Insert Into Members Values(36,'Davis','Goodman','2020 Country
Rd','Columbus','OH','48318','USA','2771152882','2771128833','[email protected]','M','1980-10-27',2);
CREATE TABLE SalesPeople (
SalesID smallint ,
FirstName varchar (20) NOT NULL ,
LastName varchar (20) NOT NULL ,
Initials varchar (3) NULL ,
Base decimal(5,2) NULL,
Supervisor smallint NUll
);
Insert into SalesPeople
Values(1,'Bob','Bentley','bbb',100,4);
Insert into SalesPeople
Values(2,'Lisa','Williams','lmw',300,4);
Insert into SalesPeople
Values(3,'Clint','Sanchez','cls',100,1);
Insert into SalesPeople Values(4,'Scott','Bull','sjb',Null,
Null);
CREATE TABLE Studios (
StudioID int,
StudioName varchar (40) NULL ,
Address varchar (60) NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
PostalCode varchar (10) NULL ,
Country varchar (20) NULL ,
WebAddress varchar (40) NULL ,
Contact varchar (50) NULL ,
EMail varchar (40) NULL ,
Phone varchar (16) NULL ,
SalesID smallint NULL
);
Insert Into Studios Values(1,'MakeTrax','3000 S St Rd
9','Anderson','IN','46012','USA','www.maketrax.com','Gardner
Roberts','[email protected]','7651223000',3);
Insert Into Studios Values(2,'Lone Star Recording','PO Box
221','Davis','TX','76382','USA','www.lsrecords.com','Manuel
Austin','[email protected]','8821993748',2);
Insert Into Studios Values(3,'Pacific Rim','681 PCH','Santa
Theresa','CA','99320','USA','www.pacrim.org','Harry
Lee','[email protected]','3811110033',2);
CREATE TABLE Titles (
TitleID int ,
ArtistID int NULL ,
Title varchar (50) NULL ,
StudioID int NULL ,
UPC varchar (13) NULL ,
Genre varchar (15) NULL
);
Insert Into Titles Values(1,1,'Meet the
Neurotics',1,'2727366627','alternative');
Insert Into Titles Values(3,15,'Smell the
Glove',2,'1283772282','metal');
Insert Into Titles Values(4,10,'Time
Flies',3,'1882344222','alternative');
Insert Into Titles Values(5,1,'Neurotic
Sequel',1,'2828830202','alternative');
Insert Into Titles
Values(6,5,'Sonatas',2,'3999320021','classical');
Insert Into Titles Values(7,2,'Louis at the
Keys',3,'3838227111','jazz');
CREATE TABLE Tracks (
TitleID int NOT NULL ,
TrackNum smallint NOT NULL ,
TrackTitle varchar (50) NULL ,
LengthSeconds smallint NULL ,
MP3 smallint NULL ,
RealAud smallint NULL
);
Insert Into Tracks Values(1,1,'Hottie',233,1,1);
Insert Into Tracks Values(1,2,'Goodtime March',293,1,1);
Insert Into Tracks Values(1,3,'TV Day',305,1,1);
Insert Into Tracks Values(1,4,'Call Me an Idiot',315,1,1);
Insert Into Tracks Values(1,5,'25',402,1,1);
Insert Into Tracks Values(1,6,'Palm',322,1,1);
Insert Into Tracks Values(1,7,'Front Door',192,1,1);
Insert Into Tracks Values(1,8,'Where''s the Rain',175,1,1);
Insert Into Tracks Values(3,1,'Fat Cheeks',352,1,1);
Insert Into Tracks Values(3,2,'Rocky and Natasha',283,1,1);
Insert Into Tracks Values(3,3,'Dweeb',273,1,1);
Insert Into Tracks Values(3,4,'Funky Town',252,1,1);
Insert Into Tracks Values(3,5,'Shoes',182,1,1);
Insert Into Tracks Values(3,6,'Time In - In Time',129,1,1);
Insert Into Tracks Values(3,7,'Wooden Man',314,0,0);
Insert Into Tracks Values(3,8,'UPS',97,0,0);
Insert Into Tracks Values(3,9,'Empty',182,0,0);
Insert Into Tracks Values(3,10,'Burrito',65,0,0);
Insert Into Tracks Values(4,1,'Bob''s Dream',185,1,1);
Insert Into Tracks Values(4,2,'My Wizard',233,1,1);
Insert Into Tracks Values(4,3,'Third''s Folly',352,1,1);
Insert Into Tracks Values(4,4,'Leather',185,1,1);
Insert Into Tracks Values(4,5,'Hot Cars Cool
Nights',192,1,1);
Insert Into Tracks Values(4,6,'Music in You',204,1,1);
Insert Into Tracks Values(4,7,'Don''t Care About
Time',221,1,1);
Insert Into Tracks Values(4,8,'Kiss',218,1,1);
Insert Into Tracks Values(4,9,'Pizza Box',183,1,1);
Insert Into Tracks Values(4,10,'Goodbye',240,1,1);
Insert Into Tracks Values(5,1,'Song 1',285,1,1);
Insert Into Tracks Values(5,2,'Song 2',272,1,1);
Insert Into Tracks Values(5,3,'Song 3',299,1,1);
Insert Into Tracks Values(5,4,'Song 4',201,1,1);
Insert Into Tracks Values(5,5,'Song 5',198,1,0);
Insert Into Tracks Values(5,6,'Song 6',254,1,0);
Insert Into Tracks Values(5,7,'Song 7',303,1,1);
Insert Into Tracks Values(5,8,'Song 8',230,1,0);
Insert Into Tracks Values(5,9,'Song 8 and 1/2',45,1,0);
Insert Into Tracks Values(6,1,'Violin Sonata No. 1 in D
Major',511,1,1);
Insert Into Tracks Values(6,2,'Violin Sonata No. 2 in A
Major',438,1,1);
Insert Into Tracks Values(6,3,'Violin Sonata No. 4 in E
Minor',821,1,0);
Insert Into Tracks Values(6,4,'Piano Sonata No. 1',493,1,0);
Insert Into Tracks Values(6,5,'Clarinet Sonata in E
Flat',399,1,0);
Insert Into Tracks Values(7,1,'I Don''t Know',201,1,0);
Insert Into Tracks Values(7,2,'What''s the Day',332,1,0);
Insert Into Tracks Values(7,3,'Sirius',287,1,0);
Insert Into Tracks Values(7,4,'Hamburger Blues',292,1,0);
Insert Into Tracks Values(7,5,'Road Trip',314,1,0);
Insert Into Tracks Values(7,6,'Meeting You',321,1,1);
Insert Into Tracks Values(7,7,'Improv 34',441,1,1);
Insert Into Tracks Values(7,8,'Hey',288,1,1);
CREATE TABLE XrefArtistsMembers (
MemberID int NOT NULL ,
ArtistID int NOT NULL ,
RespParty smallint NOT NULL
);
Insert into XrefArtistsMembers Values(20,2,1);
Insert into XrefArtistsMembers Values(31,14,1);
Insert into XrefArtistsMembers Values(3,1,1);
Insert into XrefArtistsMembers Values(10,3,1);
Insert into XrefArtistsMembers Values(13,3,0);
Insert into XrefArtistsMembers Values(7,5,1);
Insert into XrefArtistsMembers Values(8,5,0);
Insert into XrefArtistsMembers Values(9,5,0);
Insert into XrefArtistsMembers Values(32,15,0);
Insert into XrefArtistsMembers Values(19,15,1);
Insert into XrefArtistsMembers Values(21,15,0);
Insert into XrefArtistsMembers Values(34,17,1);
Insert into XrefArtistsMembers Values(29,17,0);
Insert into XrefArtistsMembers Values(15,10,1);
Insert into XrefArtistsMembers Values(35,10,0);
Insert into XrefArtistsMembers Values(14,10,0);
Insert into XrefArtistsMembers Values(33,16,1);
Insert into XrefArtistsMembers Values(26,16,0);
Insert into XrefArtistsMembers Values(18,18,1);
Insert into XrefArtistsMembers Values(28,18,0);
Insert into XrefArtistsMembers Values(22,18,0);
Insert into XrefArtistsMembers Values(30,11,1);
Insert into XrefArtistsMembers Values(36,11,0);
show tables;
In: Computer Science
Harold Blank, Vice President of Manufacturing for Herr Foods, Inc., was contemplating a capital investment that could improve production; however, this operations decision would force several of his recently hired employees into new jobs as positions were eliminated by automation.
Herr Foods took pride in achieving the highest quality in their finished products. Their top product line was potato chips, and the final physical inspection was a critical step. That was when several employees identified and removed around 75% of the discolored or burned chips before packaging. So, when Harold was introduced to the opti-scanner machine, which claimed to do the same step more effectively than humans, he was intrigued, but not convinced.
COMPANY HISTORY
In 1946, Jim Herr purchased Verna’s Potato Chips Company in Lancaster, Pennsylvania, for $1,750. The company’s assets included:
two iron kettles (each holding 100 pounds of lard),
a potato slicer for three potatoes,
a peeler that held 10 pounds of potatoes, and
a 1938 Dodge panel truck.
At 21 years old and with a $1,750 loan, Jim distributed potato chips in a wax paper bag to small grocery stores and other food outlets in southeastern Pennsylvania. He and his wife, Miriam, worked long hours in front of the hot kettles perfecting their recipes and product quality. Building a customer base was equally difficult as there were similar operations selling potato chip products in Pennsylvania and Maryland.
In 1951, a fire destroyed the company. Jim, a religious man, believed God would lead them to a new location. They purchased a small farm in Nottingham—ideally located near the mission church where his family was active and closer to their distribution area. With all of his available resources and help from the local bank, Jim reestablished Herr Foods and expanded the scope of its operations.
Nottingham—located in Chester County about 50 miles southwest of Philadelphia and 50 miles north of Baltimore—was a great location. Even though it was in a rural area, nearly 50% of America’s population lived within a 500-mile radius. The county itself had one of the highest per-capita incomes in the state and was one of the fastest-growing counties in the Philadelphia area. Jim found that the people practiced good moral and ethical standards, were religious, and provided a dependable and stable work force.
Over the years, Jim and Mim’s faith and commitment to fair and ethical business practices paid off. Jim was a man of his word. A simple handshake often closed many complicated agreements between customers and suppliers. Jim always made good on his promises and never forfeited on a debt obligation. If someone was unethical in his business practice with him, Jim was compassionate and not vindictive. This was not a sign of weakness, but an effort to reflect his Christian testimony in all areas of his business.
Herr Foods grew and prospered. Currently, there were 600+ loyal employees and over 150 sku product line classifications. Herr’s operated in a 10-state region between Massachusetts, Ohio, and Virginia, with 20 distribution centers and its own fleet of vehicles to distribute its products to retailers. Revenues were approaching $100 million per year for this family-owned business with potato chips accounting for about 60% of revenues.
The same values that Jim practiced in running the business were still evident as the second generation had assumed ownership and operation of the business. Jim’s three sons, Jim, Ed, and Gene; his daughters, June and Martha; and his son-in-law, Daryl, were all active in the business and espoused the same strong Christian values and beliefs. Son Jim stated, “I’ve agreed with my father’s philosophy of running the company to maintain a culture of integrity, fairness, and opportunity; to stress quality products and service; and to continue the growth of the company.”
OPERATIONS
Given that there were 30 snack-food companies in Pennsylvania alone—along with large national companies like Frito Lay and Nabisco—having any level of success and growing market share was quite an accomplishment for Herr’s. In addition to the extensive potato-chip product line, the company produced pretzels and other snack foods. Each line offered a variety of products with varying sodium, fat, nutrients, and packaging specifications. The company also distributed complementary product lines such as salsas, dips, and meat snacks.
Pretzel production had recently flourished. Three hundred pounds of dough are mixed every 8 minutes and fed onto four different conveyer lines. After the dough—either plain or sour—was blended into 10-pound sections, it was fed through a pretzel die to form its shape. Depending upon the pretzel, 600 to 2,000 pounds per hour were baked in each of four ovens. The operation runs 24 hours per day, Monday through Friday noon. On Friday afternoon, the equipment was cleaned.
The tortilla/corn line produces both corn and tortilla chips. Two lines ran continuously at a rate of about 2,000 pounds of product per hour. The corn was soaked for 8 to 10 hours and then cooked for 2 minutes to form a lumpy, creamed texture. The product was cut on a sheeter and sent to a fryer for 15 seconds. The hot chip runs through a tumbler where seasoning was applied. One day per week, onion rings were produced in this assembly area. Onion rings were actually dehydrated potato flakes that were fried and covered with onion seasoning.
Cheese curls and popcorn represent smaller product lines. For cheese curls, moisture was removed from corn meal, seasoning was applied, and the product was baked for 1 minute to create a puff. About 1,000 pounds were produced per hour. For popcorn, yellow gourmet kernels were air popped, small and unpopped kernels are removed, and the remaining kernels were seasoned.
The potato chip line was the biggest operation. Twenty tractor trailer loads of potatoes arrive every day with 50,000 pounds of potatoes per truck. Each truck was hydraulically lifted to a 45-degree angle to dump the potatoes.
Potatoes were dropped into a washer, scrubbed, and sent by water flume to three slicers. They were sliced at 24 slices per inch in less than 1 second. They then go to the vegetable oil vats where they were cooked between 3 and 5 minutes at about 325° F. Four pounds of potatoes made 1 pound of potato chips. Around 56,000 pounds of potatoes per hour were processed through five fryer ovens 16 hours a day.
After they were dried and salted, a conveyor belt transports the chips past a final inspection point where four employees identify and remove overcooked or green chips. Management estimated that these employees found and removed 75% of the defective chips, and defective chips represented about 0.5% of the entire output. With this inspection, only about 10 to 15 defective chips out of about 10,000 will reach the final package.
Various stages along the conveyer belt sized the chips before they reached a packing machine, with smaller chips going to the smaller packages. The tortilla chips, popcorn, cheese curls, and potato chips were packaged by weight using a bucket process to accumulate the product. The product dropped into a waiting bag. Seven million bags of potato chips of various sizes were produced per month along with a similar quantity of other products. The production process was almost entirely automated until the bags were placed in cartons for shipment.
Cartons of all the product lines were stacked by type in the warehouse. The entire warehouse inventory rotated out on a first-in-first-out basis 3 times per week. The inventory turnover rate was critical for a product sensitive to freshness with about a 10-week shelf life.
Automation
From the time the potatoes were dumped off the truck until the packages of finished product were boxed, there was virtually no human contact during potato chip production. Minimum personal contact with the food was desirable from a health perspective; however, the lack of observation and interaction was a quality control concern. Management had always seen the importance of personal inspection to insure that defective products were identified and removed. Harold was concerned that automation would replace the only stage—a critical stage—where people have an impact on quality.
Herr Foods’ top priority was quality. Ideally, customers should not find even one chip that was defective in any way. Inspectors removed chips that are green, black, and dark brown or have black and gray spots. The green chips resulted when the potatoes did not have the proper sugar content, often found in unripe potatoes. Black or dark brown chips occurred when the chips cook too long in the hot oil. Black, gray, and hard spots were caused in the colder months when potatoes bruise in shipment.
Since Herr Foods prints “Satisfaction Guaranteed or Your Money Back” on each package, the company made every effort to avoid returns. The company’s products have obviously met the consumer satisfaction test as only about 3 out of 100,000 bags of product were returned.
The new system could easily replace the existing system. A 20-foot conveyer belt was used for the inspection process where two employees on both sides of the belt search for defective chips. This section of the production line would be removed and replaced with a 5-foot belt moving at 60 miles per hour, followed by the opti-scanner, which took another 5 feet and, finally, a 10-foot conveyer belt moving at the original speed of 3 miles per hour toward the sorting and packing operations.
The opti-scanner spread out the chips and passed them under an optical sensor that recognized discolored chips. As chips were scanned, a blast of air blew defective chips onto another belt moving at a 90-degree angle where they were disposed.
The opti-scanner manufacturer was convinced that quality will not suffer because of automation, but significantly improved the process of detecting and discarding defective chips from the current rate of 75% to a 95% success level. However, the automated process would also lose about 1 good chip for every 4 bad chips. As the bad chips were blown off the conveyer belt, an occasional good chip would be blown away along with it. The manual inspection system also lost some good chips, but the amount was insignificant. About 2.5 defective chips of every 10,000 chips would be missed through manual inspection; about 12 good chips of every 10,000 would be rejected by the opti-scanner.
While the manufacturer’s claims seemed impressive, Harold still had some significant concerns regarding this new technology and potential risks. He was not aware of any other regional snack food companies that were planning on making this investment and only national companies like Frito Lay seemed to have the means to consider taking a risk of this magnitude on such unproven technology.
Cost
The new opti-scanner machine would cost $75,000; shipping, installation, and testing would be an additional $20,000. It would cost $5,000 to dismantle the existing conveyer belt and prepare the area for the new system. To avoid disrupting production, management wanted to install the new systems—about a 16-hour process—over the next holiday weekend using existing maintenance personnel with technical staff from the manufacturer. The life expectancy of the opti-scanner was five years for capital investment purposes with a zero salvage value. The opti-scanner would probably incur an additional $1,200 per year in maintenance and insurance costs.
With the machine, the company would not need the 4 inspectors employed on each of 2 shifts. These people worked 40 hours per week and received $10 per hour. The company assumed benefit costs of an extra 25%. The evening shift pay differential was an extra $0.50 per hour.
Harold had informed the staff of the company’s policy to not terminate employees due to automation. Affected employees would be reassigned to other jobs within the company. However, Harold believed that these positions could be eliminated within 6 months through attrition and reductions in new hires, which would be a savings to the company.
Inspectors tended to be the most recent hires. While the work can be monotonous, it was critical for ensuring product quality. The position experienced a higher turnover rate than other positions. The average employee stayed about 6 months to 1 year; then, 3 out of 4 transfer to other positions and 1 quits. It costs about $300 per employee in hiring and training.
The inspector position also determined which employees proved capable of more skilled and technical positions. The company had only a limited number of entry-level positions of this nature, and these positions provided a natural training ground.
To justify the acquisition to top management, the machine must give the company a payback of three years or less. Harold believed the labor savings and quality improvement would easily justify and give a satisfactory return on the investment. However, given the tight margins on all the product lines, a capital investment could impact cash flow, which may hurt the company’s credit rating and decrease its working capital.
Since the company was privately held, top management probably needed to borrow money to finance this capital acquisition. Their long-standing association with the area banking community had allowed them to qualify for the lowest rate of 8.25% for this capital project. The company could also finance the equipment purchase from corporate earnings. Last year, company owners earned a rate of return of 14% on book equity. For planning purposes, Harold assumed that 80% of the opti-scanner would be funded by debt with the remaining funds coming from retained earnings. Their current corporate tax rate is 40%.
CORPORATE CULTURE AND PHILOSOPHY
Jim Herr had always been a deeply religious man and believed that the corporate culture and philosophy should be grounded in Christian values and ethics. Maintaining the highest levels of integrity, reputation, and excellence of Herr Foods in the eyes of customers, employees, suppliers, and other stakeholders was critical. Therefore, every significant decision top management made must pass a test.
Required:
Discuss various types of capital budgeting methods available to Harold to help him in his decision. Suggest to Harold what method(s) he should use in making the decision.
Evaluate this capital acquisition proposal and recommend a course of action.
In: Finance
Project Assignment
Construct the Y_bus matrix of a given power network by computer programming, preferably MATLAB. Note that the necessary data are available in the IEEE common data format; and as the working data, you can use the IEEE 14-bus system data.
Due Date: December 26, 2019.
IEEE-Format Data for 14-Bus System
08/19/93 UW ARCHIVE 100.0 1962 W IEEE 14 Bus Test Case BUS DATA FOLLOWS 14 ITEMS 1 Bus 1 HV 1 1 3 1.060 0.0 0.0 0.0 232.4 -16.9 0.0 1.060 0.0 0.0 0.0 0.0 0 2 Bus 2 HV 1 1 2 1.045 -4.98 21.7 12.7 40.0 42.4 0.0 1.045 50.0 -40.0 0.0 0.0 0 3 Bus 3 HV 1 1 2 1.010 -12.72 94.2 19.0 0.0 23.4 0.0 1.010 40.0 0.0 0.0 0.0 0 4 Bus 4 HV 1 1 0 1.019 -10.33 47.8 -3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 5 Bus 5 HV 1 1 0 1.020 -8.78 7.6 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 6 Bus 6 LV 1 1 2 1.070 -14.22 11.2 7.5 0.0 12.2 0.0 1.070 24.0 -6.0 0.0 0.0 0 7 Bus 7 ZV 1 1 0 1.062 -13.37 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 8 Bus 8 TV 1 1 2 1.090 -13.36 0.0 0.0 0.0 17.4 0.0 1.090 24.0 -6.0 0.0 0.0 0 9 Bus 9 LV 1 1 0 1.056 -14.94 29.5 16.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19 0 10 Bus 10 LV 1 1 0 1.051 -15.10 9.0 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 11 Bus 11 LV 1 1 0 1.057 -14.79 3.5 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 12 Bus 12 LV 1 1 0 1.055 -15.07 6.1 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 13 Bus 13 LV 1 1 0 1.050 -15.16 13.5 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 14 Bus 14 LV 1 1 0 1.036 -16.04 14.9 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 -999 BRANCH DATA FOLLOWS 20 ITEMS 1 2 1 1 1 0 0.01938 0.05917 0.0528 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 5 1 1 1 0 0.05403 0.22304 0.0492 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 1 1 1 0 0.04699 0.19797 0.0438 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 4 1 1 1 0 0.05811 0.17632 0.0340 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 5 1 1 1 0 0.05695 0.17388 0.0346 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 1 1 1 0 0.06701 0.17103 0.0128 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 1 1 1 0 0.01335 0.04211 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 7 1 1 1 0 0.0 0.20912 0.0 0 0 0 0 0 0.978 0.0 0.0 0.0 0.0 0.0 0.0 4 9 1 1 1 0 0.0 0.55618 0.0 0 0 0 0 0 0.969 0.0 0.0 0.0 0.0 0.0 0.0 5 6 1 1 1 0 0.0 0.25202 0.0 0 0 0 0 0 0.932 0.0 0.0 0.0 0.0 0.0 0.0 6 11 1 1 1 0 0.09498 0.19890 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 12 1 1 1 0 0.12291 0.25581 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 13 1 1 1 0 0.06615 0.13027 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 8 1 1 1 0 0.0 0.17615 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 9 1 1 1 0 0.0 0.11001 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 10 1 1 1 0 0.03181 0.08450 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 14 1 1 1 0 0.12711 0.27038 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 11 1 1 1 0 0.08205 0.19207 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 13 1 1 1 0 0.22092 0.19988 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 14 1 1 1 0 0.17093 0.34802 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -999 LOSS ZONES FOLLOWS 1 ITEMS 1 IEEE 14 BUS -99 INTERCHANGE DATA FOLLOWS 1 ITEMS 1 2 Bus 2 HV 0.0 999.99 IEEE14 IEEE 14 Bus Test Case -9 TIE LINES FOLLOWS 0 ITEMS -999 END OF DATA
IEEE Common Data Format
Partial Description of the IEEE Common Data Format for the
Exchange of Solved Load Flow Data
The complete description can be found in the paper "Common Data
Format for the Exchange of Solved Load Flow Data", Working Group on a
Common Format for the Exchange of Solved Load Flow Data, _IEEE
Transactions on Power Apparatus and Systems_, Vol. PAS-92, No. 6,
November/December 1973, pp. 1916-1925.
The data file has lines of up to 128 characters. The lines are grouped
into sections with section headers. Data items are entered in specific
columns. No blank items are allowed, enter zeros instead. Floating point
items should have explicit decimal point. No implicit decimal points
are used.
Data type codes: A - Alphanumeric (no special characters)
I - Integer
F - Floating point
* - Mandatory item
Title Data
==========
First card in file.
Columns 2- 9 Date, in format DD/MM/YY with leading zeros. If no date
provided, use 0b/0b/0b where b is blank.
Columns 11-30 Originator's name (A)
Columns 32-37 MVA Base (F*)
Columns 39-42 Year (I)
Column 44 Season (S - Summer, W - Winter)
Column 46-73 Case identification (A)
Bus Data *
==========
Section start card *:
---------------------
Columns 1-16 BUS DATA FOLLOWS (not clear that any more than BUS in
1-3 is significant) *
Columns ?- ? NNNNN ITEMS (column not clear, I would not count on this)
Bus data cards *:
-----------------
Columns 1- 4 Bus number (I) *
Columns 7-17 Name (A) (left justify) *
Columns 19-20 Load flow area number (I) Don't use zero! *
Columns 21-23 Loss zone number (I)
Columns 25-26 Type (I) *
0 - Unregulated (load, PQ)
1 - Hold MVAR generation within voltage limits, (PQ)
2 - Hold voltage within VAR limits (gen, PV)
3 - Hold voltage and angle (swing, V-Theta) (must always
have one)
Columns 28-33 Final voltage, p.u. (F) *
Columns 34-40 Final angle, degrees (F) *
Columns 41-49 Load MW (F) *
Columns 50-59 Load MVAR (F) *
Columns 60-67 Generation MW (F) *
Columns 68-75 Generation MVAR (F) *
Columns 77-83 Base KV (F)
Columns 85-90 Desired volts (pu) (F) (This is desired remote voltage if
this bus is controlling another bus.
Columns 91-98 Maximum MVAR or voltage limit (F)
Columns 99-106 Minimum MVAR or voltage limit (F)
Columns 107-114 Shunt conductance G (per unit) (F) *
Columns 115-122 Shunt susceptance B (per unit) (F) *
Columns 124-127 Remote controlled bus number
Section end card:
-----------------
Columns 1- 4 -999
Branch Data *
=============
Section start card *:
---------------------
Columns 1-16 BRANCH DATA FOLLOWS (not clear that any more than BRANCH
is significant) *
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Branch data cards *:
--------------------
Columns 1- 4 Tap bus number (I) *
For transformers or phase shifters, the side of the model
the non-unity tap is on
Columns 6- 9 Z bus number (I) *
For transformers and phase shifters, the side of the model
the device impedance is on.
Columns 11-12 Load flow area (I)
Columns 13-14 Loss zone (I)
Column 17 Circuit (I) * (Use 1 for single lines)
Column 19 Type (I) *
0 - Transmission line
1 - Fixed tap
2 - Variable tap for voltage control (TCUL, LTC)
3 - Variable tap (turns ratio) for MVAR control
4 - Variable phase angle for MW control (phase shifter)
Columns 20-29 Branch resistance R, per unit (F) *
Columns 30-40 Branch reactance X, per unit (F) * No zero impedance lines
Columns 41-50 Line charging B, per unit (F) * (total line charging, +B)
Columns 51-55 Line MVA rating No 1 (I) Left justify!
Columns 57-61 Line MVA rating No 2 (I) Left justify!
Columns 63-67 Line MVA rating No 3 (I) Left justify!
Columns 69-72 Control bus number
Column 74 Side (I)
0 - Controlled bus is one of the terminals
1 - Controlled bus is near the tap side
2 - Controlled bus is near the impedance side (Z bus)
Columns 77-82 Transformer final turns ratio (F)
Columns 84-90 Transformer (phase shifter) final angle (F)
Columns 91-97 Minimum tap or phase shift (F)
Columns 98-104 Maximum tap or phase shift (F)
Columns 106-111 Step size (F)
Columns 113-119 Minimum voltage, MVAR or MW limit (F)
Columns 120-126 Maximum voltage, MVAR or MW limit (F)
Section end card:
-----------------
Columns 1- 4 -999
Loss Zone Data
==============
Section start card
------------------
Columns 1-16 LOSS ZONES FOLLOWS (not clear that any more than LOSS
is significant)
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Loss Zone Cards:
----------------
Columns 1- 3 Loss zone number (I)
Columns 5-16 Loss zone name (A)
Section end card:
-----------------
Columns 1- 3 -99
Interchange Data *
==================
Section start card
------------------
Columns 1-16 INTERCHANGE DATA FOLLOWS (not clear that any more than
first word is significant).
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Interchange Data Cards *:
-------------------------
Columns 1- 2 Area number (I) no zeros! *
Columns 4- 7 Interchange slack bus number (I) *
Columns 9-20 Alternate swing bus name (A)
Columns 21-28 Area interchange export, MW (F) (+ = out) *
Columns 30-35 Area interchange tolerance, MW (F) *
Columns 38-43 Area code (abbreviated name) (A) *
Columns 46-75 Area name (A)
Section end card:
-----------------
Columns 1- 2 -9
Tie Line Data
=============
Section start card
------------------
Columns 1-16 TIE LINES FOLLOW (not clear that any more than TIE
is significant)
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Tie Line Cards:
---------------
Columns 1- 4 Metered bus number (I)
Columns 7-8 Metered area number (I)
Columns 11-14 Non-metered bus number (I)
Columns 17-18 Non-metered area number (I)
Column 21 Circuit number
Section end card:
-----------------
Columns 1- 3 -999
In: Electrical Engineering
Project Assignment
Construct the Y_bus matrix of a given power network by computer programming, preferably MATLAB. Note that the necessary data are available in the IEEE common data format; and as the working data, you can use the IEEE 14-bus system data.
Due Date: December 26, 2019.
08/19/93 UW ARCHIVE 100.0 1962 W IEEE 14 Bus Test Case BUS DATA FOLLOWS 14 ITEMS 1 Bus 1 HV 1 1 3 1.060 0.0 0.0 0.0 232.4 -16.9 0.0 1.060 0.0 0.0 0.0 0.0 0 2 Bus 2 HV 1 1 2 1.045 -4.98 21.7 12.7 40.0 42.4 0.0 1.045 50.0 -40.0 0.0 0.0 0 3 Bus 3 HV 1 1 2 1.010 -12.72 94.2 19.0 0.0 23.4 0.0 1.010 40.0 0.0 0.0 0.0 0 4 Bus 4 HV 1 1 0 1.019 -10.33 47.8 -3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 5 Bus 5 HV 1 1 0 1.020 -8.78 7.6 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 6 Bus 6 LV 1 1 2 1.070 -14.22 11.2 7.5 0.0 12.2 0.0 1.070 24.0 -6.0 0.0 0.0 0 7 Bus 7 ZV 1 1 0 1.062 -13.37 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 8 Bus 8 TV 1 1 2 1.090 -13.36 0.0 0.0 0.0 17.4 0.0 1.090 24.0 -6.0 0.0 0.0 0 9 Bus 9 LV 1 1 0 1.056 -14.94 29.5 16.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19 0 10 Bus 10 LV 1 1 0 1.051 -15.10 9.0 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 11 Bus 11 LV 1 1 0 1.057 -14.79 3.5 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 12 Bus 12 LV 1 1 0 1.055 -15.07 6.1 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 13 Bus 13 LV 1 1 0 1.050 -15.16 13.5 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 14 Bus 14 LV 1 1 0 1.036 -16.04 14.9 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 -999 BRANCH DATA FOLLOWS 20 ITEMS 1 2 1 1 1 0 0.01938 0.05917 0.0528 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 5 1 1 1 0 0.05403 0.22304 0.0492 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 1 1 1 0 0.04699 0.19797 0.0438 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 4 1 1 1 0 0.05811 0.17632 0.0340 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 5 1 1 1 0 0.05695 0.17388 0.0346 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 1 1 1 0 0.06701 0.17103 0.0128 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 1 1 1 0 0.01335 0.04211 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 7 1 1 1 0 0.0 0.20912 0.0 0 0 0 0 0 0.978 0.0 0.0 0.0 0.0 0.0 0.0 4 9 1 1 1 0 0.0 0.55618 0.0 0 0 0 0 0 0.969 0.0 0.0 0.0 0.0 0.0 0.0 5 6 1 1 1 0 0.0 0.25202 0.0 0 0 0 0 0 0.932 0.0 0.0 0.0 0.0 0.0 0.0 6 11 1 1 1 0 0.09498 0.19890 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 12 1 1 1 0 0.12291 0.25581 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 13 1 1 1 0 0.06615 0.13027 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 8 1 1 1 0 0.0 0.17615 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 9 1 1 1 0 0.0 0.11001 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 10 1 1 1 0 0.03181 0.08450 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 14 1 1 1 0 0.12711 0.27038 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 11 1 1 1 0 0.08205 0.19207 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 13 1 1 1 0 0.22092 0.19988 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 14 1 1 1 0 0.17093 0.34802 0.0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -999 LOSS ZONES FOLLOWS 1 ITEMS 1 IEEE 14 BUS -99 INTERCHANGE DATA FOLLOWS 1 ITEMS 1 2 Bus 2 HV 0.0 999.99 IEEE14 IEEE 14 Bus Test Case -9 TIE LINES FOLLOWS 0 ITEMS -999 END OF DATA
Partial Description of the IEEE Common Data Format for the
Exchange of Solved Load Flow Data
The complete description can be found in the paper "Common Data
Format for the Exchange of Solved Load Flow Data", Working Group on a
Common Format for the Exchange of Solved Load Flow Data, _IEEE
Transactions on Power Apparatus and Systems_, Vol. PAS-92, No. 6,
November/December 1973, pp. 1916-1925.
The data file has lines of up to 128 characters. The lines are grouped
into sections with section headers. Data items are entered in specific
columns. No blank items are allowed, enter zeros instead. Floating point
items should have explicit decimal point. No implicit decimal points
are used.
Data type codes: A - Alphanumeric (no special characters)
I - Integer
F - Floating point
* - Mandatory item
Title Data
==========
First card in file.
Columns 2- 9 Date, in format DD/MM/YY with leading zeros. If no date
provided, use 0b/0b/0b where b is blank.
Columns 11-30 Originator's name (A)
Columns 32-37 MVA Base (F*)
Columns 39-42 Year (I)
Column 44 Season (S - Summer, W - Winter)
Column 46-73 Case identification (A)
Bus Data *
==========
Section start card *:
---------------------
Columns 1-16 BUS DATA FOLLOWS (not clear that any more than BUS in
1-3 is significant) *
Columns ?- ? NNNNN ITEMS (column not clear, I would not count on this)
Bus data cards *:
-----------------
Columns 1- 4 Bus number (I) *
Columns 7-17 Name (A) (left justify) *
Columns 19-20 Load flow area number (I) Don't use zero! *
Columns 21-23 Loss zone number (I)
Columns 25-26 Type (I) *
0 - Unregulated (load, PQ)
1 - Hold MVAR generation within voltage limits, (PQ)
2 - Hold voltage within VAR limits (gen, PV)
3 - Hold voltage and angle (swing, V-Theta) (must always
have one)
Columns 28-33 Final voltage, p.u. (F) *
Columns 34-40 Final angle, degrees (F) *
Columns 41-49 Load MW (F) *
Columns 50-59 Load MVAR (F) *
Columns 60-67 Generation MW (F) *
Columns 68-75 Generation MVAR (F) *
Columns 77-83 Base KV (F)
Columns 85-90 Desired volts (pu) (F) (This is desired remote voltage if
this bus is controlling another bus.
Columns 91-98 Maximum MVAR or voltage limit (F)
Columns 99-106 Minimum MVAR or voltage limit (F)
Columns 107-114 Shunt conductance G (per unit) (F) *
Columns 115-122 Shunt susceptance B (per unit) (F) *
Columns 124-127 Remote controlled bus number
Section end card:
-----------------
Columns 1- 4 -999
Branch Data *
=============
Section start card *:
---------------------
Columns 1-16 BRANCH DATA FOLLOWS (not clear that any more than BRANCH
is significant) *
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Branch data cards *:
--------------------
Columns 1- 4 Tap bus number (I) *
For transformers or phase shifters, the side of the model
the non-unity tap is on
Columns 6- 9 Z bus number (I) *
For transformers and phase shifters, the side of the model
the device impedance is on.
Columns 11-12 Load flow area (I)
Columns 13-14 Loss zone (I)
Column 17 Circuit (I) * (Use 1 for single lines)
Column 19 Type (I) *
0 - Transmission line
1 - Fixed tap
2 - Variable tap for voltage control (TCUL, LTC)
3 - Variable tap (turns ratio) for MVAR control
4 - Variable phase angle for MW control (phase shifter)
Columns 20-29 Branch resistance R, per unit (F) *
Columns 30-40 Branch reactance X, per unit (F) * No zero impedance lines
Columns 41-50 Line charging B, per unit (F) * (total line charging, +B)
Columns 51-55 Line MVA rating No 1 (I) Left justify!
Columns 57-61 Line MVA rating No 2 (I) Left justify!
Columns 63-67 Line MVA rating No 3 (I) Left justify!
Columns 69-72 Control bus number
Column 74 Side (I)
0 - Controlled bus is one of the terminals
1 - Controlled bus is near the tap side
2 - Controlled bus is near the impedance side (Z bus)
Columns 77-82 Transformer final turns ratio (F)
Columns 84-90 Transformer (phase shifter) final angle (F)
Columns 91-97 Minimum tap or phase shift (F)
Columns 98-104 Maximum tap or phase shift (F)
Columns 106-111 Step size (F)
Columns 113-119 Minimum voltage, MVAR or MW limit (F)
Columns 120-126 Maximum voltage, MVAR or MW limit (F)
Section end card:
-----------------
Columns 1- 4 -999
Loss Zone Data
==============
Section start card
------------------
Columns 1-16 LOSS ZONES FOLLOWS (not clear that any more than LOSS
is significant)
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Loss Zone Cards:
----------------
Columns 1- 3 Loss zone number (I)
Columns 5-16 Loss zone name (A)
Section end card:
-----------------
Columns 1- 3 -99
Interchange Data *
==================
Section start card
------------------
Columns 1-16 INTERCHANGE DATA FOLLOWS (not clear that any more than
first word is significant).
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Interchange Data Cards *:
-------------------------
Columns 1- 2 Area number (I) no zeros! *
Columns 4- 7 Interchange slack bus number (I) *
Columns 9-20 Alternate swing bus name (A)
Columns 21-28 Area interchange export, MW (F) (+ = out) *
Columns 30-35 Area interchange tolerance, MW (F) *
Columns 38-43 Area code (abbreviated name) (A) *
Columns 46-75 Area name (A)
Section end card:
-----------------
Columns 1- 2 -9
Tie Line Data
=============
Section start card
------------------
Columns 1-16 TIE LINES FOLLOW (not clear that any more than TIE
is significant)
Columns 40?- ? NNNNN ITEMS (column not clear, I would not count on this)
Tie Line Cards:
---------------
Columns 1- 4 Metered bus number (I)
Columns 7-8 Metered area number (I)
Columns 11-14 Non-metered bus number (I)
Columns 17-18 Non-metered area number (I)
Column 21 Circuit number
Section end card:
-----------------
Columns 1- 3 -999
In: Electrical Engineering
c++
I cannot get my operator+ to pass my test, it is failing on the first test, how would i convert my operator+ to use pointers and dynamic memory?
Requirements:
============================================================================
string.hpp:
#ifndef CS23001_STRING_INTERFACE_HPP
#define CS23001_STRING_INTERFACE_HPP
#include <iostream>
////////////////////////////////////////////////////
// CLASS INV: str[length()] == 0 &&
// length() == capacity() &&
// capacity() == stringSize - 1
//
class String {
public:
String (); //Empty string
String (char); //String('x')
String (const char[]); //String("abc")
String (const String&); //Copy Constructor
~String (); //Destructor
void swap (String&); //Constant time swap
String& operator= (String); //Assignment Copy
char& operator[] (int); //Accessor/Modifier
char operator[] (int) const; //Accessor
int capacity () const; //Max chars that can be stored (not including null)
int length () const; //Actual number of chars in string
String operator+ (const String&) const;
String& operator+= (const String&);
bool operator== (const String&) const;
bool operator< (const String&) const;
String substr (int, int) const; //The sub-string from staring position to ending position
int findch (int, char) const; //Find location of charater starting at a position
int findstr (int, const String&) const; //Find location of str starting at a position
void test_String ();
friend std::ostream& operator<<(std::ostream&, const String&);
friend std::istream& operator>>(std::istream&, String&);
private:
String (int n ); //String(10) - capacity 10, empty string
String (int , const char []); //String(10, "abc") - capacity 10 with "abc"
void resetCapacity (int); //Resets capacity to N, keeps string intact
char *str; //Pointer to char[]
int stringSize; //Size includes NULL terminator
};
String operator+ (const char[], const String&);
String operator+ (char, const String&);
bool operator== (const char[], const String&);
bool operator== (char, const String&);
bool operator< (const char[], const String&);
bool operator< (char, const String&);
bool operator<= (const String&, const String&);
bool operator!= (const String&, const String&);
bool operator>= (const String&, const String&);
bool operator> (const String&, const String&);
#endif
============================================================================
string.cpp:
#include <iostream>
#include "string.hpp"
#include <cassert>
String::String() { // default constructor - empty string
stringSize = 1;
str = new char [stringSize];
str[0] = 0;
}
String::String(char ch) {
stringSize = 2;
str = new char [stringSize];
str[0] = ch;
str[1] = '\0';
}
//REQUIRES: str.length() < capacity()
//String a("abc")
//Takes character array and turns into string array
String::String(const char X[]) {
int i = 0;
while (X[i] != '\0') ++i;
stringSize = i + 1;
str = new char [stringSize];
for(int j = 0; j < capacity(); ++j)
str[j] = X[j];
str[capacity()] = 0;
}
String::String(const String& rhs) { //copy constructor
stringSize = rhs.stringSize;
str = new char [stringSize];
for(int i = 0; i < capacity(); ++i)
str[i] = rhs.str[i];
}
String::~String() { //destructor
delete[] str;
}
void String::swap (String& rhs) { //Constant time swap
char * temporary = str;
str = rhs.str;
rhs.str = temporary;
int hold = stringSize;
stringSize = rhs.stringSize;
rhs.stringSize = hold;
}
String& String::operator= ( String rhs) { // Assignment copy
if (str == rhs.str) return *this; //check to see if they are already pointing to the same address
delete [] str;
stringSize = rhs.stringSize;
str = new char [stringSize];
for (int i = 0; i < capacity(); ++i)
str[i] = rhs.str[i];
return *this;
}
//REQUIRES: 0 <= i < length()
// operator[] const --- allows access to const objects
char String::operator[](int i) const {
assert( (i > 0) && (i < length()) );
return str[i];
}
//REQUIRES: 0 <= i < length()
// operator[] --- allows access / modification to non-const objects
char& String::operator[] (int i) {
assert( (i >= 0) && (i < length() ) );
return str[i];
}
int String::capacity() const { //capacity = stringSize -1;
return (stringSize - 1);
}
//ENSURES: Retval == i where str[i] = 0
int String::length() const {
int result = 0;
while (str[result] != '\0')
++result;
return result;
}
// retval == "xyzabc" where "xyx" + "abc"
String String::operator+(const String& rhs) const {
String result;
int offset = length();
int i = 0;
while(rhs.str[i] != 0) {
result.str[offset + i] = rhs.str[i];
++i;
if (offset + i == capacity()) break;
}
return result;
}
String operator+(char lhs, const String& rhs) {
return String(lhs) + rhs;
}
String operator+(const char lhs[], const String& rhs) {
return String(lhs) + rhs;
}
String& String::operator+=(const String& rhs) {
*this = operator+(rhs);
return *this;
}
bool String::operator==(const String& rhs) const {
int i = 0;
while ((str[i] != '\0') && (str[i] == rhs.str[i])) ++i;
return str[i] == rhs.str[i];
}
bool operator==(char lhs, const String& rhs) {
return String(lhs) == rhs;
}
bool operator==(char lhs[], const String& rhs) {
return String(lhs) == rhs;
}
bool String::operator<(const String& rhs) const {
int i = 0;
while ((str[i] != 0) && (rhs.str[i] != 0) && (str[i] == rhs.str[i])) ++i;
return str[i] < rhs.str[i];
}
bool operator<(char lhs, const String& rhs) {
return String(lhs) < rhs;
}
bool operator<(const char lhs[], const String& rhs) {
return String(lhs) < rhs;
}
bool operator!=(const String& lhs, const String& rhs) {
return !(lhs == rhs) || (lhs == rhs);
}
bool operator<=(const String& lhs, const String& rhs) {
return (lhs < rhs) || (lhs == rhs);
}
bool operator>(const String& lhs, const String& rhs) {
return (rhs < lhs);
}
bool operator>=(const String& lhs, const String& rhs) {
return !(lhs < rhs);
}
std::ostream& operator<<(std::ostream& out, const String& rhs) {
out << rhs.str;
return out;
}
std::istream& operator>>(std::istream& in, String& rhs) {
char placehold[540000];
in >> placehold;
rhs = String(placehold);
return in;
}
//REQUIRES: 0 <= start < length()
//ENSURES: retval == i where str[i] == ch && i >= start
// or retval == -1 if ch != s[start.length()-1]
int String::findch(int start, char ch) const {
if ( (start < 0) || (start >= length()) ) return -1;
int i = start;
while (str[i] != 0) {
if (str[i] == ch) return i;
++i;
}
return -1;
}
int String::findstr(int pos, const String& rhs) const {
int i = pos;
if ((pos < 0) || (pos >= length() - rhs.length()))
return -1;
if (length() < rhs.length())
return -1;
while ((str[pos] != 0) && (rhs.length() + pos - 1 <= length())) {
if (rhs == substr(i, i + rhs.length() - 1))
return pos;
++i;
}
return -1;
}
//REQUIRES: 0 <= start <= end < length()
//ENSURES: retval == s[start, ..., end]
String String::substr(int start, int end) const {
if (start < 0) return String();
if (start > end) return String();
if (end >= length()) return String();
String result;
int i = start;
while (i <= end) {
result += str[i];
++i;
}
return result;
}
String::String (int n) { //String(10) - capacity 10, empty string
stringSize = n;
str = new char [stringSize];
str[0] = 0;
}
String::String (int n, const char ch[]) { //String(10, "abc") - capacity 10 with "abc"
stringSize = n;
str = new char [n];
for (int i = 0; i < n; ++i)
str[i] = ch[i];
}
void String::resetCapacity (int n ) { //Resets capacity to N, keeps string intact
int smaller = stringSize;
if (smaller > n) smaller = n;
stringSize = n;
char * tmp = new char [stringSize];
for (int i = 0; i < smaller; ++i)
tmp[i] = str[i];
delete [] str;
str = tmp;
}
void String::test_String() {
String testing(5);
assert(testing.length() == 0);
assert(testing.capacity() == 5);
String test(15);
assert(test.length() == 0);
assert(test.capacity() == 15);
String CharArray(10, "abc");
assert(CharArray.length() == 3);
assert(CharArray.capacity() == 10);
}
============================================================================
concat test .cpp:
#include "string.hpp"
#include <cassert>
#include <iostream>
int main ()
{
{
// TEST
String str = "x";
String str2 = "y";
String result;
result = str+str2;
std::cout<< str << " " << str2 << " " <<result<<std::endl;
// VERIFY
assert(result == "xy");
}
{
// TEST
String str = "xyz";
String str2 = "abc";
String result;
result = str+str2;
// VERIFY
assert(result == "xyzabc");
}
{
// TEST
String str = "qlW3KSqbFk";
String str2 = "f6iSmJhRTl";
String result;
result = str+str2;
// VERIFY
assert(result == "qlW3KSqbFkf6iSmJhRTl");
}
{
//------------------------------------------------------
// SETUP FIXTURE
// TEST
String str = "lZ8kmGDuKeqzqPOmvthx94jQQg46C8";
String str2 = "SgiwD";
String result;
result = str+str2;
// VERIFY
assert(result == "lZ8kmGDuKeqzqPOmvthx94jQQg46C8SgiwD");
}
std::cout << "Done testing Concatination Constructor." << std::endl; }
In: Computer Science
Please create the SQL queries using the lryics database under question 4 and use "select * from..." after each query to show the effects of your data manipulation query. Thanks for help
1. The title 'Time Flies' now has a new track, the 11th track 'Spring', which is 150 seconds long and has only a MP3 file. Insert the new track into Tracks table (Don’t hand-code any data for insert that can be looked up from the Titles table).
2. Create a new table called Members2 with the same fields as the Members table. (Use DESCRIBE to check if Members2 table is created).
3. Populate Members2 with the content of the Members table.
4. The area code for Columbus, Ohio (OH) has been changed from 277 to 899. Update the homephone and workphone numbers of all members in Members2 table accordingly.
DROP TABLES IF EXISTS Artists,Genre, Members, Titles, Tracks,SalesPeople,Studios,XrefArtistsMembers;
DROP TABLES IF EXISTS Authors,Publishers,Titles,Title_Authors,Royalties;
DROP TABLES IF EXISTS Products,Customers,Orders,Order_details;
DROP TABLES IF EXISTS Sailors,Boats,Reserves;
CREATE TABLE Artists (
ArtistID int,
ArtistName varchar (50) NOT NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
Country varchar (20) NULL ,
WebAddress varchar (40) NULL ,
EntryDate date NULL ,
LeadSource varchar (10) NULL
);
Insert Into Artists Values(1,'The Neurotics','Peterson','NC','USA','www.theneurotics.com','2003-05-14','Directmail');
Insert Into Artists Values(2,'Louis Holiday','Clinton','IL','USA' ,NULL,'2003-06-03','Directmail');
Insert Into Artists Values(3,'Word','Anderson','IN','USA',NULL,'2003-06-08','Email');
Insert Into Artists Values(5,'Sonata','Alexandria','VA','USA','www.classical.com/sonata','2003-06-08','Ad');
Insert Into Artists Values(10,'The Bullets','Alverez','TX','USA',NULL,'2003-08-10','Email');
Insert Into Artists Values(14,'Jose MacArthur','Santa Rosa','CA','USA','www.josemacarthur.com','2003-08-17','Ad');
Insert Into Artists Values(15,'Confused','Tybee Island','GA','USA',Null,'2003-09-14','Directmail');
Insert Into Artists Values(17,'The Kicks','New Rochelle','NY','USA',NULL,'2003-12-03','Ad');
Insert Into Artists Values(16,'Today','London','ONT','Canada','www.today.com','2003-10-07','Email');
Insert Into Artists Values(18,'21 West Elm','Alamaba','VT','USA','www.21westelm.com','2003-02-05','Ad');
Insert Into Artists Values(11,'Highlander','Columbus','OH','USA',NULL,'2002-08-10','Email');
CREATE TABLE Genre (
Genre varchar (15)
);
Insert into Genre Values('alternative');
Insert into Genre Values('classical');
Insert into Genre Values('jazz');
Insert into Genre Values('metal');
Insert into Genre Values('R&B');
Insert into Genre Values('rap');
Insert into Genre Values('pop');
CREATE TABLE Members (
MemberID int ,
FirstName varchar (25) NULL ,
LastName varchar (25) NULL ,
Address varchar (60) NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
PostalCode varchar (10) NULL ,
Country varchar (20) NULL ,
HomePhone varchar (16) NULL ,
WorkPhone varchar (16) NULL ,
EMail varchar (40) NULL ,
Gender char (1) NULL ,
Birthday date NULL ,
SalesID smallint NULL
);
Insert Into Members Values(10,'Roberto','Alvarez','Rt 1','Anderson','IN','46019','USA','7651552983','7651628837','[email protected]','M','1968-01-18',2);
Insert Into Members Values(31,'Jose','MacArthur','51444 Vine','Santa Rosa','CA','99999','USA','6331289393',Null,'[email protected]','M','1978-06-24',1);
Insert Into Members Values(13,'Mary','Chrisman','1772 East 117th','Fishers','IN','46123','USA','3171820387',Null,'[email protected]','F','1973-03-01',1);
Insert Into Members Values(15,'Warren','Boyer','167 Alamo Dr','Alverez','TX','75601','USA','8221722883',Null,'[email protected]','M','1969-04-19',2);
Insert Into Members Values(32,'Doug','Finney','2020 Dubois','Savannah','GA','30003','USA','9821222929',Null,'[email protected]','M','1963-08-04',3);
Insert Into Members Values(19,'Terry','Irving','18a 7th St','Tybee Island','GA','30004','USA','5411252093',Null,Null,'M','1959-06-22',3);
Insert Into Members Values(21,'Michelle','Henderson','201 Bonaventure','Savannah','GA','30005','USA','8221928273',Null,Null,'F','1964-03-15',2);
Insert Into Members Values(34,'William','Morrow','PO Box 1882','New Rochelle','NY','10014','USA','9981722928',Null,'[email protected]','M','1965-03-17',2);
Insert Into Members Values(29,'Frank','Payne','5412 Clinton','New Rochelle','NY','10014','USA','9981737464',Null,Null,'M','1960-01-17',1);
Insert Into Members Values(35,'Aiden','Franks','167 East 38th','Alverez','TX','75601','USA','8321729283','8321723833','[email protected]','M','1983-09-02',2);
Insert Into Members Values(3,'Bryce','Sanders','PO Box 1292','Peterson','NC','27104','USA','6441824283',Null,'[email protected]','M','1966-06-11',2);
Insert Into Members Values(14,'Carol','Wanner','787 Airport Rd','Alverez','TX','75601','USA','6831223944',Null,Null,'F','1978-11-08',3);
Insert Into Members Values(33,'Brian','Ranier','23 Gregory Lane','London','ONT','M6Y 2Y7 ','Canada','6231842933',Null,Null,'M','1957-10-19',3);
Insert Into Members Values(7,'Marcellin','Lambert','142 Sample Rd','Alexandria','VA','20102','USA','8331929302',Null,'[email protected]','M','1959-11-14',3);
Insert Into Members Values(8,'Caroline','Kale','1515 Stone Church Rd','Allen','VA','20321','USA','7321223742',Null,Null,'F','1956-05-30',3);
Insert Into Members Values(9,'Kerry','Fernandez','15 Midway','Lynchberg','VA','21223','USA','2211229384','2211223939',Null,'M','1962-01-16',1);
Insert Into Members Values(26,'Tony','Wong','115 Maple St','McKensie','ONT','M8H 3T1','Canada','3311692832','3311692822','[email protected]','M','1955-11-01',2);
Insert Into Members Values(18,'Bonnie','Taft','RR4','Alamaba','VT','05303','USA','3721223292',Null,'[email protected]','F','1960-09-21',1);
Insert Into Members Values(20,'Louis','Holiday','15 Davis Ct','Clinton','IL','63882','USA','1451223838',Null,Null,'M','1969-07-27',2);
Insert Into Members Values(22,'Bobby','Crum','RR2','Pine','VT','05412','USA','1831828211',Null,Null,'M','1965-06-10',3);
Insert Into Members Values(28,'Vic','Cleaver','100 Maple','Reston','VT','05544','USA','8111839292',Null,Null,'M','1957-02-10',2);
Insert Into Members Values(30,'Roberto','Goe','14 Gray Rd','Columbus','OH','48110','USA','2771123943',Null,Null,'M','1967-09-12',1);
Insert Into Members Values(36,'Davis','Goodman','2020 Country Rd','Columbus','OH','48318','USA','2771152882','2771128833','[email protected]','M','1980-10-27',2);
CREATE TABLE SalesPeople (
SalesID smallint ,
FirstName varchar (20) NOT NULL ,
LastName varchar (20) NOT NULL ,
Initials varchar (3) NULL ,
Base decimal(5,2) NULL,
Supervisor smallint NUll
);
Insert into SalesPeople Values(1,'Bob','Bentley','bbb',100,4);
Insert into SalesPeople Values(2,'Lisa','Williams','lmw',300,4);
Insert into SalesPeople Values(3,'Clint','Sanchez','cls',100,1);
Insert into SalesPeople Values(4,'Scott','Bull','sjb',Null, Null);
CREATE TABLE Studios (
StudioID int,
StudioName varchar (40) NULL ,
Address varchar (60) NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
PostalCode varchar (10) NULL ,
Country varchar (20) NULL ,
WebAddress varchar (40) NULL ,
Contact varchar (50) NULL ,
EMail varchar (40) NULL ,
Phone varchar (16) NULL ,
SalesID smallint NULL
);
Insert Into Studios Values(1,'MakeTrax','3000 S St Rd 9','Anderson','IN','46012','USA','www.maketrax.com','Gardner Roberts','[email protected]','7651223000',3);
Insert Into Studios Values(2,'Lone Star Recording','PO Box 221','Davis','TX','76382','USA','www.lsrecords.com','Manuel Austin','[email protected]','8821993748',2);
Insert Into Studios Values(3,'Pacific Rim','681 PCH','Santa Theresa','CA','99320','USA','www.pacrim.org','Harry Lee','[email protected]','3811110033',2);
CREATE TABLE Titles (
TitleID int ,
ArtistID int NULL ,
Title varchar (50) NULL ,
StudioID int NULL ,
UPC varchar (13) NULL ,
Genre varchar (15) NULL
);
Insert Into Titles Values(1,1,'Meet the Neurotics',1,'2727366627','alternative');
Insert Into Titles Values(3,15,'Smell the Glove',2,'1283772282','metal');
Insert Into Titles Values(4,10,'Time Flies',3,'1882344222','alternative');
Insert Into Titles Values(5,1,'Neurotic Sequel',1,'2828830202','alternative');
Insert Into Titles Values(6,5,'Sonatas',2,'3999320021','classical');
Insert Into Titles Values(7,2,'Louis at the Keys',3,'3838227111','jazz');
CREATE TABLE Tracks (
TitleID int NOT NULL ,
TrackNum smallint NOT NULL ,
TrackTitle varchar (50) NULL ,
LengthSeconds smallint NULL ,
MP3 smallint NULL ,
RealAud smallint NULL
);
Insert Into Tracks Values(1,1,'Hottie',233,1,1);
Insert Into Tracks Values(1,2,'Goodtime March',293,1,1);
Insert Into Tracks Values(1,3,'TV Day',305,1,1);
Insert Into Tracks Values(1,4,'Call Me an Idiot',315,1,1);
Insert Into Tracks Values(1,5,'25',402,1,1);
Insert Into Tracks Values(1,6,'Palm',322,1,1);
Insert Into Tracks Values(1,7,'Front Door',192,1,1);
Insert Into Tracks Values(1,8,'Where''s the Rain',175,1,1);
Insert Into Tracks Values(3,1,'Fat Cheeks',352,1,1);
Insert Into Tracks Values(3,2,'Rocky and Natasha',283,1,1);
Insert Into Tracks Values(3,3,'Dweeb',273,1,1);
Insert Into Tracks Values(3,4,'Funky Town',252,1,1);
Insert Into Tracks Values(3,5,'Shoes',182,1,1);
Insert Into Tracks Values(3,6,'Time In - In Time',129,1,1);
Insert Into Tracks Values(3,7,'Wooden Man',314,0,0);
Insert Into Tracks Values(3,8,'UPS',97,0,0);
Insert Into Tracks Values(3,9,'Empty',182,0,0);
Insert Into Tracks Values(3,10,'Burrito',65,0,0);
Insert Into Tracks Values(4,1,'Bob''s Dream',185,1,1);
Insert Into Tracks Values(4,2,'My Wizard',233,1,1);
Insert Into Tracks Values(4,3,'Third''s Folly',352,1,1);
Insert Into Tracks Values(4,4,'Leather',185,1,1);
Insert Into Tracks Values(4,5,'Hot Cars Cool Nights',192,1,1);
Insert Into Tracks Values(4,6,'Music in You',204,1,1);
Insert Into Tracks Values(4,7,'Don''t Care About Time',221,1,1);
Insert Into Tracks Values(4,8,'Kiss',218,1,1);
Insert Into Tracks Values(4,9,'Pizza Box',183,1,1);
Insert Into Tracks Values(4,10,'Goodbye',240,1,1);
Insert Into Tracks Values(5,1,'Song 1',285,1,1);
Insert Into Tracks Values(5,2,'Song 2',272,1,1);
Insert Into Tracks Values(5,3,'Song 3',299,1,1);
Insert Into Tracks Values(5,4,'Song 4',201,1,1);
Insert Into Tracks Values(5,5,'Song 5',198,1,0);
Insert Into Tracks Values(5,6,'Song 6',254,1,0);
Insert Into Tracks Values(5,7,'Song 7',303,1,1);
Insert Into Tracks Values(5,8,'Song 8',230,1,0);
Insert Into Tracks Values(5,9,'Song 8 and 1/2',45,1,0);
Insert Into Tracks Values(6,1,'Violin Sonata No. 1 in D Major',511,1,1);
Insert Into Tracks Values(6,2,'Violin Sonata No. 2 in A Major',438,1,1);
Insert Into Tracks Values(6,3,'Violin Sonata No. 4 in E Minor',821,1,0);
Insert Into Tracks Values(6,4,'Piano Sonata No. 1',493,1,0);
Insert Into Tracks Values(6,5,'Clarinet Sonata in E Flat',399,1,0);
Insert Into Tracks Values(7,1,'I Don''t Know',201,1,0);
Insert Into Tracks Values(7,2,'What''s the Day',332,1,0);
Insert Into Tracks Values(7,3,'Sirius',287,1,0);
Insert Into Tracks Values(7,4,'Hamburger Blues',292,1,0);
Insert Into Tracks Values(7,5,'Road Trip',314,1,0);
Insert Into Tracks Values(7,6,'Meeting You',321,1,1);
Insert Into Tracks Values(7,7,'Improv 34',441,1,1);
Insert Into Tracks Values(7,8,'Hey',288,1,1);
CREATE TABLE XrefArtistsMembers (
MemberID int NOT NULL ,
ArtistID int NOT NULL ,
RespParty smallint NOT NULL
);
Insert into XrefArtistsMembers Values(20,2,1);
Insert into XrefArtistsMembers Values(31,14,1);
Insert into XrefArtistsMembers Values(3,1,1);
Insert into XrefArtistsMembers Values(10,3,1);
Insert into XrefArtistsMembers Values(13,3,0);
Insert into XrefArtistsMembers Values(7,5,1);
Insert into XrefArtistsMembers Values(8,5,0);
Insert into XrefArtistsMembers Values(9,5,0);
Insert into XrefArtistsMembers Values(32,15,0);
Insert into XrefArtistsMembers Values(19,15,1);
Insert into XrefArtistsMembers Values(21,15,0);
Insert into XrefArtistsMembers Values(34,17,1);
Insert into XrefArtistsMembers Values(29,17,0);
Insert into XrefArtistsMembers Values(15,10,1);
Insert into XrefArtistsMembers Values(35,10,0);
Insert into XrefArtistsMembers Values(14,10,0);
Insert into XrefArtistsMembers Values(33,16,1);
Insert into XrefArtistsMembers Values(26,16,0);
Insert into XrefArtistsMembers Values(18,18,1);
Insert into XrefArtistsMembers Values(28,18,0);
Insert into XrefArtistsMembers Values(22,18,0);
Insert into XrefArtistsMembers Values(30,11,1);
Insert into XrefArtistsMembers Values(36,11,0);
show tables;In: Computer Science
Write a Python 3 program called “parse.py” using the template for a Python program that we covered in this module. Note: Use this mod7.txt input file.
Name your output file “output.txt”.
Build your program using a main function and at least one other function.
Give your input and output file names as command line arguments.
Your program will read the input file, and will output the following information to the output file as well as printing it to the screen:
This is mod7.txt
I do not come here as an advocate, because whatever position the suffrage movement may occupy in the United States of America, in England it has passed beyond the realm of advocacy and it has entered into the sphere of practical politics. It has become the subject of revolution and civil war, and so tonight I am not here to advocate woman suffrage. American suffragists can do that very well for themselves. I am here as a soldier who has temporarily left the field of battle in order to explain - it seems strange it should have to be explained, what civil war is like when civil war is waged by women. I am not only here as a soldier temporarily absent from the field at battle; I am here, and that, I think, is the strangest part of my coming, I am here as a person who, according to the law courts of my country, it has been decided, is of no value to the community at all; and I am adjudged because of my life to be a dangerous person, under sentence of penal servitude in a convict prison. It is not at all difficult if revolutionaries come to you from Russia, if they come to you from China, or from any other part of the world, if they are men. But since I am a woman it is necessary to explain why women have adopted revolutionary methods in order to win the rights of citizenship. We women, in trying to make our case clear, always have to make as part of our argument, and urge upon men in our audience the fact, a very simple fact, that women are human beings. Suppose the men of Hartford had a grievance, and they laid that grievance before their legislature, and the legislature obstinately refused to listen to them, or to remove their grievance, what would be the proper and the constitutional and the practical way of getting their grievance removed? Well, it is perfectly obvious at the next general election the men of Hartford would turn out that legislature and elect a new one. But let the men of Hartford imagine that they were not in the position of being voters at all, that they were governed without their consent being obtained, that the legislature turned an absolutely deaf ear to their demands, what would the men of Hartford do then? They couldn't vote the legislature out. They would have to choose; they would have to make a choice of two evils: they would either have to submit indefinitely to an unjust state of affairs, or they would have to rise up and adopt some of the antiquated means by which men in the past got their grievances remedied. Your forefathers decided that they must have representation for taxation, many, many years ago. When they felt they couldn't wait any longer, when they laid all the arguments before an obstinate British government that they could think of, and when their arguments were absolutely disregarded, when every other means had failed, they began by the tea party at Boston, and they went on until they had won the independence of the United States of America. It is about eight years since the word militant was first used to describe what we were doing. It was not militant at all, except that it provoked militancy on the part of those who were opposed to it. When women asked questions in political meetings and failed to get answers, they were not doing anything militant. In Great Britain it is a custom, a time-honoured one, to ask questions of candidates for parliament and ask questions of members of the government. No man was ever put out of a public meeting for asking a question. The first people who were put out of a political meeting for asking questions, were women; they were brutally ill-used; they found themselves in jail before 24 hours had expired. We were called militant, and we were quite willing to accept the name. We were determined to press this question of the enfranchisement of women to the point where we were no longer to be ignored by the politicians. You have two babies very hungry and wanting to be fed. One baby is a patient baby, and waits indefinitely until its mother is ready to feed it. The other baby is an impatient baby and cries lustily, screams and kicks and makes everybody unpleasant until it is fed. Well, we know perfectly well which baby is attended to first. That is the whole history of politics. You have to make more noise than anybody else, you have to make yourself more obtrusive than anybody else, you have to fill all the papers more than anybody else, in fact you have to be there all the time and see that they do not snow you under. When you have warfare things happen; people suffer; the noncombatants suffer as well as the combatants. And so it happens in civil war. When your forefathers threw the tea into Boston Harbour, a good many women had to go without their tea. It has always seemed to me an extraordinary thing that you did not follow it up by throwing the whiskey overboard; you sacrificed the women; and there is a good deal of warfare for which men take a great deal of glorification which has involved more practical sacrifice on women than it has on any man. It always has been so. The grievances of those who have got power, the influence of those who have got power commands a great deal of attention; but the wrongs and the grievances of those people who have no power at all are apt to be absolutely ignored. That is the history of humanity right from the beginning. Well, in our civil war people have suffered, but you cannot make omelettes without breaking eggs; you cannot have civil war without damage to something. The great thing is to see that no more damage is done than is absolutely necessary, that you do just as much as will arouse enough feeling to bring about peace, to bring about an honourable peace for the combatants; and that is what we have been doing. We entirely prevented stockbrokers in London from telegraphing to stockbrokers in Glasgow and vice versa: for one whole day telegraphic communication was entirely stopped. I am not going to tell you how it was done. I am not going to tell you how the women got to the mains and cut the wires; but it was done. It was done, and it was proved to the authorities that weak women, suffrage women, as we are supposed to be, had enough ingenuity to create a situation of that kind. Now, I ask you, if women can do that, is there any limit to what we can do except the limit we put upon ourselves? If you are dealing with an industrial revolution, if you get the men and women of one class rising up against the men and women of another class, you can locate the difficulty; if there is a great industrial strike, you know exactly where the violence is and how the warfare is going to be waged; but in our war against the government you can't locate it. We wear no mark; we belong to every class; we permeate every class of the community from the highest to the lowest; and so you see in the woman's civil war the dear men of my country are discovering it is absolutely impossible to deal with it: you cannot locate it, and you cannot stop it. "Put them in prison," they said, "that will stop it." But it didn't stop it at all: instead of the women giving it up, more women did it, and more and more and more women did it until there were 300 women at a time, who had not broken a single law, only "made a nuisance of themselves" as the politicians say. Then they began to legislate. The British government has passed more stringent laws to deal with this agitation than it ever found necessary during all the history of political agitation in my country. They were able to deal with the revolutionaries of the Chartists' time; they were able to deal with the trades union agitation; they were able to deal with the revolutionaries later on when the Reform Acts were passed: but the ordinary law has not sufficed to curb insurgent women. They had to dip back into the middle ages to find a means of repressing the women in revolt. They have said to us, government rests upon force, the women haven't force, so they must submit. Well, we are showing them that government does not rest upon force at all: it rests upon consent. As long as women consent to be unjustly governed, they can be, but directly women say: "We withhold our consent, we will not be governed any longer so long as that government is unjust." Not by the forces of civil war can you govern the very weakest woman. You can kill that woman, but she escapes you then; you cannot govern her. No power on earth can govern a human being, however feeble, who withholds his or her consent. When they put us in prison at first, simply for taking petitions, we submitted; we allowed them to dress us in prison clothes; we allowed them to put us in solitary confinement; we allowed them to put us amongst the most degraded of criminals; we learned of some of the appalling evils of our so-called civilisation that we could not have learned in any other way. It was valuable experience, and we were glad to get it. I have seen men smile when they heard the words "hunger strike", and yet I think there are very few men today who would be prepared to adopt a "hunger strike" for any cause. It is only people who feel an intolerable sense of oppression who would adopt a means of that kind. It means you refuse food until you are at death's door, and then the authorities have to choose between letting you die, and letting you go; and then they let the women go. Now, that went on so long that the government felt that they were unable to cope. It was [then] that, to the shame of the British government, they set the example to authorities all over the world of feeding sane, resisting human beings by force. There may be doctors in this meeting: if so, they know it is one thing to feed by force an insane person; but it is quite another thing to feed a sane, resisting human being who resists with every nerve and with every fibre of her body the indignity and the outrage of forcible feeding. Now, that was done in England, and the government thought they had crushed us. But they found that it did not quell the agitation, that more and more women came in and even passed that terrible ordeal, and they were obliged to let them go. Then came the legislation - the "Cat and Mouse Act". The home secretary said: "Give me the power to let these women go when they are at death's door, and leave them at liberty under license until they have recovered their health again and then bring them back." It was passed to repress the agitation, to make the women yield - because that is what it has really come to, ladies and gentlemen. It has come to a battle between the women and the government as to who shall yield first, whether they will yield and give us the vote, or whether we will give up our agitation. Well, they little know what women are. Women are very slow to rouse, but once they are aroused, once they are determined, nothing on earth and nothing in heaven will make women give way; it is impossible. And so this "Cat and Mouse Act" which is being used against women today has failed. There are women lying at death's door, recovering enough strength to undergo operations who have not given in and won't give in, and who will be prepared, as soon as they get up from their sick beds, to go on as before. There are women who are being carried from their sick beds on stretchers into meetings. They are too weak to speak, but they go amongst their fellow workers just to show that their spirits are unquenched, and that their spirit is alive, and they mean to go on as long as life lasts. Now, I want to say to you who think women cannot succeed, we have brought the government of England to this position, that it has to face this alternative: either women are to be killed or women are to have the vote. I ask American men in this meeting, what would you say if in your state you were faced with that alternative, that you must either kill them or give them their citizenship? Well, there is only one answer to that alternative, there is only one way out - you must give those women the vote. You won your freedom in America when you had the revolution, by bloodshed, by sacrificing human life. You won the civil war by the sacrifice of human life when you decided to emancipate the negro. You have left it to women in your land, the men of all civilised countries have left it to women, to work out their own salvation. That is the way in which we women of England are doing. Human life for us is sacred, but we say if any life is to be sacrificed it shall be ours; we won't do it ourselves, but we will put the enemy in the position where they will have to choose between giving us freedom or giving us death. So here am I. I come in the intervals of prison appearance. I come after having been four times imprisoned under the "Cat and Mouse Act", probably going back to be rearrested as soon as I set my foot on British soil. I come to ask you to help to win this fight. If we win it, this hardest of all fights, then, to be sure, in the future it is going to be made easier for women all over the world to win their fight when their time comes.
In: Computer Science
A Positive Revolution in Change: Appreciative Inquiry
David L. Cooperrider Case Western Reserve University and Diana
Whitney The Taos Institute
After reading the journal article assigned for the week, write and post a 1-pg review (350 words) include a link that was of interest
Appreciative Inquiry (AI) begins an adventure. The urge and call
to adventure has been sounded by many people and many
organizations, and it will take many more to fully explore the vast
vistas that are now appearing on the horizon. But even in the first
steps, what is being sensed is an exciting direction in our
language and theories of change—an invitation, as some have
declared, to “a positive revolution”.
The words just quoted are strong and, unfortunately, they are not
ours. But the more we replay, for example, the high-wire moments of
our several years of work at GTE, the more we find ourselves asking
the very same kinds of questions the people of GTE asked their
senior executives: “Are you really ready for the momentum that is
being generated? This is igniting a grassroots movement…it is
creating an organization in full voice, a center stage for the
positive revolutionaries!”
Tom White, President of what was then called GTE Telops (making up
80% of GTE’s 67,000 employees) replies back, with no hesitation:
“Yes, and what I see in this meeting are zealots, people with a
mission and passion for creating the new GTE. Count me in, I’m your
number one recruit, number one zealot”. People cheer.
Enthusiasms continue, and they echo over subsequent months as lots
of hard work pays off. Fourteen months later --based on significant
and measurable changes in stock prices, morale survey measures,
quality/customer relations, union-management relations, etc.--
GTE’s whole system change initiative is given professional
recognition by the American Society for Training and Development.
It wins the 1997 ASTD award for best organization change program in
the country. Appreciative inquiry is cited as the “backbone”.
How Did They Do It?
This paper provides a broad update and overview of AI. The GTE
story mentioned at the outset is, in many ways, just beginning but
it is scarcely alone. In the ten years since the
1with its emphasis on metaphor and narrative, relational ways of
knowing, on language, and on its potential as a source of
generative theory (Gergen, 1994); as the most important advance in
action research in the past decade (Bushe, 1995); as offspring and
“heir” to Maslow’s vision of a positive social science (Chin, 1998;
Curran, 1991); as a powerful second generation OD practice (French
and Bell, 1995; Porras, 1991; Mirvis, 1988/89); as model of a much
needed participatory science, a “new yoga of inquiry” (Harman,
1990); as a radically affirmative approach to change which
completely lets go of problem-based management and in so doing
vitally transforms strategic planning, survey methods, culture
change, merger integration methods, approaches to TQM, measurement
systems, sociotechnical systems, etc. (White, 1996); and lastly, as
OD’s philosopher’s stone (Head & Sorenson, et. al 1996). Indeed
it is difficult to sum up the whole of AI—as a philosophy of
knowing, a normative stance, a methodology for managing change, and
as an approach to leadership and human development. However, for
purposes here, it might be most useful to begin with a
practice-oriented definition of AI, one that is more descriptive
than theoretical and one that provides a compass for the examples
to follow:
Appreciative Inquiry is about the co-evolutionary search for the
best in people, their organizations, and the relevant world around
them. In its broadest focus, it involves systematic discovery of
what gives “life” to a living system when it is most alive, most
effective, and most constructively capable in economic, ecological,
and human terms. AI involves, in a central way, the art and
practice of asking questions that strengthen a system’s capacity to
apprehend, anticipate, and heighten positive potential. It
centrally involves the mobilization of inquiry through the crafting
of the “unconditional positive question” often-involving hundreds
or sometimes thousands of people. In AI, the arduous task of
intervention gives way to the speed of imagination and innovation;
instead of negation, criticism, and spiraling diagnosis, there is
discovery, dream, and design. AI seeks, fundamentally, to build a
constructive union between a whole people and the massive entirety
of what people talk about as past and present capacities:
achievements, assets, unexplored potentials, innovations,
strengths, elevated thoughts, opportunities, benchmarks, high point
moments, lived values, traditions, strategic competencies, stories,
expressions of wisdom, insights into the deeper corporate spirit or
soul, and visions of valued and possible futures. Taking all of
these together as a gestalt, AI deliberately, in everything it
does, seeks to work from accounts of this “positive change
core”—and it assumes that every living system has many untapped and
rich and inspiring accounts of the positive. Link the energy of
this core directly to any change agenda and changes never thought
possible are suddenly and democratically mobilized.
The positive core of organizational life, we submit, is one of the
greatest and largely unrecognized resources in the field of change
management today. As said earlier, we are clearly in our infancy
when it comes to tools for working with it, talking about it, and
designing our systems in synergistic alignment with it. But one
thing is evident and clear as we reflect on the most important
things we have learned with AI: human systems grow in the direction
of what they persistently ask questions about and this propensity
is strongest and most sustainable when the means and ends of
inquiry are positively
3correlated. The single most prolific thing a group can do if its
aims are to liberate the human spirit and consciously construct a
better future is to make the positive change core the common and
explicit property of all.
Let’s Illustrate:
The Appreciative Inquiry “4-D” Cycle
(insert 4-D cycle here—see page 28)
You have just received the following unsettling phone call:
My name is Rita Simmel; I am President of a New York consulting
partnership. Our firm specializes in dealing with difficult
conflict in organizations: labor-management issues, gender
conflict, issues of diversity. We have been retained by a fortune
500 corporation for the past several years. The contract is around
sexual harassment, an issue that is deeper and more severe than
virtually any corporation realizes. The issues are about power, the
glass ceiling, and many things. As you know, millions of dollars
are being expended on the issues. Our firm has specialized in this
area for some years and now I’m beginning to ask myself the
Hippocratic oath. Are we really helping? Here is the bottom line
with our client. We have been working on the issues for two years,
and by every measure-- numbers of complaints, lawsuits, evaluations
from sexual harassment training programs, word of mouth—the problem
continues in its growth. Furthermore people are now voting with
their feet. They are not coming to the workshops. Those that do
seem to leave with doubts: our post-workshop interviews show people
feel less able to communicate with those of the opposite gender,
they report feeling more distance and less trust, and the glass
ceiling remains. So here is my question. How would you take an
appreciative inquiry approach to sexual harassment?
This was a tough one. We requested time to think about it, asking
if we could talk again in a day or two. We can do the same for you
right now (give you a bit of time) as we invite you to think about
things you might seriously propose in the callback.
So before going further with the story lets pause and look at a
typical flow for AI, a cycle that can be as rapid and informal as
in a conversation with a friend or colleague, or as formal as an
organization-wide analysis involving every stakeholder, including
customers, suppliers, partners, and the like.
4Figure one shows (page 28), on the outside, four key stages in AI:
Discovery—mobilizing a whole system inquiry into the positive
change core; Dream—creating a clear results-oriented vision in
relation to discovered potential and in relation to questions of
higher purpose, i.e., “What is the world calling us to become?”
Design—creating possibility propositions of the ideal organization,
an organization design which people feel is capable of magnifying
or eclipsing the positive core and realizing the articulated new
dream; and Destiny—strengthening the affirmative capability of the
whole system enabling it to build hope and momentum around a deep
purpose and creating processes for learning, adjustment, and
improvisation, like a jazz group over time (see the excellent
article by Barrett, 1998).
At the core of the cycle, is Affirmative Topic Choice. It is the
most important part of any AI. If, in fact, knowledge and
organizational destiny are as intricately interwoven as we think,
then isn’t it possible that the seeds of change are implicit in the
very first questions we ask? AI theory says yes and takes the idea
quite seriously: it says that the way we know people, groups, and
organizations is fateful. It further asserts the time is overdue to
recognize that symbols and conversations, emerging from all our
analytic modes, are among the world’s paramount resources.
Topic Choice
So back to our phone call. If inquiry and change are a simultaneous
moment; if the questions we ask set the stage for what we “find”;
and if what we “discover” (the data) creates the material out of
which the future is conceived, conversed about, and
constructed—then how shall we proceed with an appreciative approach
to sexual harassment? Here is an excerpt from the response:
D.C.: Hello Rita. Before we get into our proposal we have an
important question. What is it that you want to learn about and
achieve with this whole intervention, and by when?
Rita: We want to dramatically cut the incidence of sexual
harassment. We want to solve this huge problem, or at least make a
significant dent in it.
D.C.: O.K. Rita… But is that all?
Rita: You mean what do I really want to see? (Long pauses…then she
blurts out). What we really want to see is the development of the
new century organization—a model of high quality cross-gender
relationships in the workplace!
DC: Great topic. What would happen if we put an invitation out in
the company newsletter, asking people in pairs to step forward to
nominate themselves as candidates to study and share their stories
of what it means to create and sustain high quality cross-gender
relationships in the workplace? It might be interesting to do a
large conference, and really put a magnifying lens to the stages of
development, contextual factors, toughquestions of adult
attraction, breakthroughs in terms of power relations, and so on.
What do you think?
To move fastforward, a relatively small pilot project was created
which surpassed everyone’s expectations. Hundreds, not dozens, of
pairs nominated themselves. That was surprise number one. Then
other organizations got word of the pilot and a truly major effort,
moving through the 4-D framework, was conceptualized by another
consulting firm, Marge Schiller and Associates. The pioneering
organization she worked with, which now can happily be named, was
the Avon Corporation in Mexico. Again there were similar
issues—including the glass ceiling at senior management levels—but
again there was interest in framing the whole thing in terms of an
inquiry.
To begin, a hundred people were trained in the basics of AI
interviewing. They in turn went out into every part of the
organization and over the next several weeks completed many more
interviews, about 300 in all. At the end of each interview, the
interviewers asked the person interviewed if they too could help do
some interviewing. A waterfall was experienced. Stories poured
in—stories of achievement, trust building, authentic joint
leadership, practices of effective conflict management, ways of
dealing with sex stereotypes, stages of development and methods of
career advancement.
The second two “Ds”-- articulating the new century dream and
creating designs for an organization that maximally supported the
development of high quality cross-gender relationships-- came next.
These were combined in a large group format much like a future
search. Using stories from the interviews as a basis for imagining
the future, expansive and practical propositions were created, for
example, “Every task force or committee at Avon, whenever possible,
is co-chaired by a cross-gender pairing”. The significance of even
this simple proposal proved to be big. Likewise, propositions in
other areas of organization design were also carefully crafted.
Soon, literally everything in the organization was opened to
discussion: corporate structures, systems, work processes,
communications, career opportunities, governance, compensation
practices, leadership patterns, learning opportunities, customer
connections, and more.
In the end, some 30 visionary propositions were created. Subsequent
changes in system structures and behaviors were reported to be
dramatic (Schiller, 1998). As it turns out, the story, like GTE’s,
gets even better. Avon Mexico was just recently singled out,
several years later, by the Catalyst organization. They were given
the 1997 Catalyst Award for best place in the country for women to
work.
It is a classic example of the power of topic choice. Affirmative
topics, always homegrown, can be on anything the people of an
organization feel gives life to the system. As a rule of thumb most
projects have between 3-5 topics. Words like empowerment,
innovation, sense of ownership, commitment, integrity, ecological
consciousness, and pride are often articulated as worthy of study.
Topics can be on anything an organization feels to be strategically
and humanly important. AI topics can be on technical processes,
financial efficiencies, human issues, market opportunities, social
responsibilities, or anything else. In each case of topic choice,
the same premise isfirmly posited: human systems grow in the
direction of their deepest and most frequent inquiries.
The Phase of Discovery
The inquiry we are talking about is anything but wishful. If we
were to underline one of the two words-- appreciative or
inquiry—our pen would immediately move to the latter. In Vital
Speeches of the Day (1996), Tom White, President of what was then
called GTE Telephone Operations, puts his interpretation of AI in
executive language, months before GTE’s change effort was
recognized by ASTD:
Appreciative Inquiry can get you much better results than seeking
out and solving problems. That’s an interesting concept for me—and
I imagine most of you—because telephone companies are among the
best problem solvers in the world. We troubleshoot everything. We
concentrate enormous resources on correcting problems that have
relatively minor impact on our overall service and performance (and
which)…when used continually and over a long period of time, this
approach can lead to a negative culture. If you combine a negative
culture with all the challenges we face today, it could be easy to
convince ourselves that we have too many problem to overcome—to
slip into a paralyzing sense of hopelessness….Don’t get me wrong.
I’m not advocating mindless happy talk. Appreciative Inquiry is a
complex science designed to make things better. We can’t ignore
problems—we just need to approach them from the other side”.
What Tom White calls “the other side”, we are describing as the
positive change core. AI, most simply, is a tool for connecting to
the transformational power of this core. Willis Harman (1990) talks
about AI as a participatory science, a yoga of inquiry, where the
term yoga comes from the Sanskrit root yug which means link or
bond. In that sense if we remember something or someone, it can be
said that there is a form of yoga happening. AI helps make the
memory link by concentrating systematic inquiry into all aspects of
the appreciable world, into an organization’s infinite and surplus
capacity—past, present and future. By concentrating on the atom,
human beings have unleashed its power. AI says we can do the same
in every living system once we open this ever emergent positive
core—every strength, innovation, achievement, resource, living
value, imaginative story, benchmark, hope, positive tradition,
passion, high point experience, internal genius, dream-- to
systematic inquiry.
The core task of the discovery phase is to discover and disclose
positive capacity, at least until an organization’s understanding
of this “surplus” is exhausted (which has never happened once in
our experience). AI provides a practical way to ignite this “spirit
of inquiry” on an organization-wide basis. Consider this
example:
At Leadshare in Canada, AI was used to help this big eight
accounting firm make the tough transition in the executive
succession of a “legendary” managing partner. The managing partner
seized the moment as an incredible leadership development
opportunity for all 400 partners. Everyone was interviewed with AI.
An extensive interview protocol was designed (it ended up taking
about 2 hours per interview)focusing on affirmative topics like
innovation, equality, partnership, speed to market, and valuing
diversity (in Canada between francophone and anglophone). And not
one outside consultant did the interviews. All were done
internally, by 30 junior partners as part of a leadership
development program. A powerful and instant intergenerational
connection was made, and organizational history came alive in
face-to-face stories. Instead of amnesia, or a
problem-to-be-solved, people began to relate to their history in a
whole new way. Like a good piece of poetry filled with endless
interpretive meaning, people at Leadshare ascended into their
history as a reservoir of positive possibility. At the next annual
partners meeting, with over 400 people in the conference hall, the
material was showcased and coupled to the future, as the strategic
planning became one of the “best” the partners could ever remember
(Rainey, 1996)
Perhaps it is obvious, but the process of doing the interviews is
as important as the data collected. When managers ask us how many
people should be interviewed or, who should do the interviews, we
increasingly find ourselves saying “everyone”. It is not uncommon
in AI work to talk about doing thousands of interviews. A hospital
in Seattle recently did three thousand interviews in preparation
for an organization-wide Appreciative Inquiry Summit (Whitney and
Cooperrider, 1998). People themselves, not consultants, generate
the system-wide organization analysis using questions like this: “
Obviously you have had ups and downs in your career here at XYZ.
But for the moment I would like you to focus on a high point, a
time in your work experience here where you felt most alive, most
engaged, or most successful. Can you tell me the story? How did it
unfold? What was it organizationally that made it stand out? What
was it about you that made it a high point? What key insights do
you have for all of us at XYZ?”
In Chicago, in one of the most exciting AI’s we have seen, there is
talk of over a million interviews. And guess whose interviews have
produced the best data—the most inspiring, vision-generating
stories? It is the children. It is happening through
inter-generational inquiry where the elders are valued and share
hopes in settings with the young. One of our favorite papers is
about the Imagine Chicago story and the leadership of Bliss Browne.
It is titled “The Child as the Agent of Inquiry” (Cooperrider,
1996). It argues that the spirit of inquiry is something all of us
in change work need to reclaim and aspire to: openness,
availability, epistemological humility, the ability to admire, to
be surprised, to be inspired, to inquire into our valued and
possible worlds.
What distinguishes AI, especially in this phase of work, is that
every carefully crafted question is positive. Knowing and changing
are a simultaneous moment. The thrill of discovery becomes the
thrill of creating. As people throughout a system connect in
serious study into qualities, examples, and analysis of the
positive core --each appreciating and everyone being appreciated--
hope grows and community expands.
From Discovery to Dream
When an artist sits in front of a landscape the imagination is
kindled not by searching for “what is wrong with this landscape”,
but by a special ability to be inspired by those things of value
worth valuing. Appreciation, it appears, draws our eye toward life,
but stirs our feelings, sets in motion our curiosity, and provides
inspiration to the envisioning
8mind. In his analysis of esthetics and the origins of creative
images, Nietzsche once asked of the power of appreciation: “ Does
it not praise? Does it not glorify? Does it not select? Does it not
bring {that which is appreciated} to prominence?” (In Rader, 1973,
p. 12). Then in the same passage he takes a next step, linking
valuing (discovery) and imagination (dream). He elaborates: “
valuing is creating: hear it, ye creating ones! Valuation is itself
the treasure and jewel of valued things”.
During the dream phase, the interview stories and insights get put
to constructive use. As people are brought together to listen
carefully to the innovations and moments of organizational “life”,
sometimes in storytelling modes, sometimes in interpretive and
analytic modes, a convergence zone is created where the future
begins to be discerned in the form of visible patterns interwoven
into the texture of the actual. The amplified interaction among
innovators and innovations makes something important happen: very
rapidly we start seeing outlines of the New World. Some
organizations turn the data into a special commemorative report
celebrating the successes and exceptional moment in the life of the
organization (Liebler, 1997). Others have created a thematic
analysis—careful to document rich stories and not succumb to
“narrative thin” one line quotes (Ludema, 1996). In all cases the
data onto the positive change core serves as an essential resource
for the visioning stages of the appreciative inquiry 4-D
model.
Before their strategic planning session in 1997, Nutrimental Foods
of Brazil closed down the plant for a full day to bring all 700
employees together for a day of Discovery into the factors and
forces that have given life to the system when it had been most
effective, most alive, and most successful as a producer of high
quality health foods. With cheers and good wishes a “smaller” group
of 150 stakeholders—employees from all levels, suppliers,
distributors, community leaders, financiers, and customers—then
went into a four day strategy session to articulate a new and bold
corporate dream. The stories from the day before were used just as
an artist uses a palette of colors—before painting a picture the
artist assembles the red paints, blue, green, yellow and so on.
With these “materials” in hand people were asked to dream: “What is
the world calling us to become? What are those things about us that
no matter how much we change, we want to continue into our new and
different future? Lets assume that tonight while we were all asleep
a miracle occurred where Nutrimental became exactly as we would
like it to be—all of its best qualities are magnified, extended,
multiplied the way we would like to see…in fact we wake up and it
is now 2005…as you come into Nutrimental today what do you see that
is different, and how do you know?”After four days of appreciative
analysis, planning, and articulation of three new strategic
business directions, the organization launches into the future with
focus, solidarity, and confidence. Six months later, record bottom
line figures of millions of dollars are recorded—profits are up
300%. The co-CEOs Rodrigo Loures and Arthur Lemme Netto attribute
the dramatic results to two things: bringing the whole system into
the planning process, and realizing that organizations are in fact
“centers of human relatedness”(Loures and Lemme Netto, 1998) which
thrive when there is an appreciative eye—when people see the best
in one another, when they can dialogue their dreams and ultimate
concerns in affirming ways, and when they are connected in full
voice to create not just new worlds but better worlds.
9Design
Once the strategic focus or dream is articulated (usually
consisting of three things in our model-- a vision of a better
world, a powerful purpose, and a compelling statement of strategic
intent) attention turns to the creation of the ideal organization,
the social architecture or actual design of the system in relation
to the world of which it is part. What we have found is that the
sequencing is crucial, moving first through in-depth work on Dream
before Design, followed with back and forth iterations. In Zimbabwe
we recently worked with a partner organization of Save the
Children. It was fascinating to observe how easy it was to
re-design the organization in terms of structures and systems once
broad agreement was reached on a powerful Dream. The articulation
of the image of the future was simple: “Every person in Zimbabwe
shall have access to clean water within five years”. The critical
design shift, demanded by the large dream, was to a new form of
organization based on a network of alliances or partnerships, not
bureaucracy’s self-sufficient hierarchy.
One aspect that differentiates Appreciative Inquiry from other
visioning or planning methodologies is that images of the future
emerge out of grounded examples from an organization’s positive
past. Sometimes this “data” is complimented with benchmark studies
of other organizations creating a “generative metaphor” for
circumventing common resistances to change (Barrett and
Cooperrider, 1990). In both cases, the good news stories are used
to craft possibility propositions that bridge the best of “what is”
with collective speculation or aspiration of “what might be”. In
the working of the material people are invited to challenge the
status quo as well as common assumptions underlying the design of
the organization. People are encouraged to “wander beyond” the data
with the essential question being: “What would our organization
look like if it were designed in every way possible to maximize the
qualities of the positive core and enable the accelerated
realization of our dreams?”
When inspired by a great dream we have yet to find an organization
that did not feel compelled to design something very new and very
necessary. Here is an example of a possibility proposition, one of
about twenty organization design visions that were created at DIA
Corporation, a rapidly growing distributor of consumer products.
Today this proposition is modus operandi at the corporation:
DIA has become a learning organization that fosters the cross
fertilization of ideas, minimizes the building of empires,
harnesses the synergy of group cooperation, and cultivates the
pride of being a valued member of one outstanding corporation. DIA
accelerates its learning through an annual strategic planning
conference that involves all five hundred people in the firm as
well as key partners and stakeholders. As a setting for “strategic
learning”, teams present their benchmarking studies of the best
five other organizations, deemed leaders in their class. Other
teams present an annual appreciative analysis of DIA, and together
these data-bases of success stories (internal and external) help
set the stage for DIA’s strategic, future search planning.
Recently we have had the opportunity to team up with Dee Hock, one
of the greatest visionary CEOs we have ever worked with. Dee was
the founder of VISA, abreakthrough organization that has over
20,000 offices, and since 1970 has grown something like 10,000%;
this year annual sales expected to pass $1 trillion. The whole Visa
system, from Calcutta to Chicago, in over 200 countries is
completely unmanageable from the perspective of using centralized,
command-and-control design principles.
If General Motors once defined the shape of the old model, perhaps
Dee’s “chaordic organization” –combining chaos and order in ways
which interweave (like nature’s designs) infinite variety and
self-organizing order—is a foreshadowing of an emerging prototype.
What we have learned by working with Dee is how to move
pragmatically and substantively from appreciative Discovery and
Dream to truly post-bureaucratic Design that distributes power and
liberates human energy in a way we have never seen. Most recently
we have collaborated on a re-constitution of the United Way of
America as well as an initiative to design something akin to a
United Nations among the world’s great religions and spiritual
traditions (it is called United Religions). In each case helping
people agree on a set of design principles is crucial. That is
“principles” as in “We hold these truths to be self evident: that
all people are created equal…” Again, this is not a set of
platitudes but a manifesto, what people believe in and care about
in their gut.
Destiny
Of all the creatures of earth, said William James in 1902, only
human beings can change their pattern. “Man alone is the architect
of his destiny”.
In our early years of AI work we called the 4th “D” Delivery. We
emphasized planning for continuous learning, adjustment, and
improvisation in the service of shared ideals. It was a time for
action planning, developing implementation strategies, and dealing
with conventional challenges of sustainability. But the word
delivery simply did not go far enough. It did not convey the sense
of liberation we were seeing, like the well documented hotel case,
where the system tranformed itself from a one-star to four-star
hotel by using AI and literally putting a moratorium on all the
traditional problem solving efforts that it had going (Barret and
Cooperrider, 1990).
Executives like Jane Watkins (former Chair of the Board at NTL) and
Jane Pratt (executive at the World Bank and now CEO of the Mountain
Institute) argued that AI engenders a repatterning of our
relationships not only with each other but also our relationship to
reality itself. Reminiscent of Paulo Friere’s concept of pedagogy
of the oppressed—where people move in their relationship to reality
from “submergence” to “reflexive awareness” to
“co-participation”—these leaders insisted that AI’s gift is at the
paradigmatic level. AI is not so much about new knowledge but new
knowing. Indeed people frequently talk, as they move through the
pedagogy of life-giving Discovery, Dream, and Design, that
something suddenly hits home: that interpretation matters—that the
manner in which they/we read the world filters to the level of our
imaginations, our relationships, and ultimately to the direction
and meaning of our action. We create the organizational worlds in
which we live.
What we discovered quite honestly was that momentum for change
and long-term sustainability increased the more we abandoned
“delivery” ideas of action planning, monitoring progress, and
building implementation strategies. What was done instead, in
several of the most exciting cases, was to focus only on giving AI
away, to everyone, and then stepping back. The GTE story, still
unfolding but already attracting national recognition, is
suggestive. It is a story that says organizational change needs to
look a lot more like an inspired movement than a neatly packaged or
engineered product. Dan Young, the head of OD at GTE, and his
colleagues Maureen Garrison and Jean Moore, call it “organizing for
change from the grassroots to the frontline”. Call it the path of
positive protest, or a strategy for positive subversion—whatever it
is called it is virtually unstoppable once “it” is up and running.
Its structure is called the Positive Change Network (PCN). One
especially dramatic moment gives the sense:
The headline article in GTE Together described what was spreading
as a grassroots movement to build the new GTE. Initiated as a pilot
training to see what would happen if the tools and theories of
appreciative inquiry were made available to frontline employees,
things started taking off. All of a sudden, without any permission,
frontline employees are launching interview studies into positive
topics like innovation, inspired leadership, revolutionary customer
responsiveness, labor-management partnerships, and “fun”. Fresh out
of a training session on AI, one employee, for example, did 200
interviews into the positive core of a major call center. Who is
going say “no” to a complementary request like—“would you help me
out…I’m really trying to find out more about the best innovations
developing in your area and I see you as someone who could really
give me new insight into creating settings where innovation can
happen… It is part of my leadership development. Do you have time
for an interview…I would be glad to share my learning’s with you
later!” Soon the topics are finding their way into meetings,
corridor conversations, and senior planning sessions—in other words
the questions, enthusiastically received, are changing corporate
attention, language, agendas, and learnings. Many start
brainstorming applications for AI. Lists are endless. Have we ever
done focus groups with the 100% satisfied customer? How about
changing call center measures? What would happen if we replaced the
entire deficit measures with equally powerful measures of the
positive? How can we revitalize the TQM groups, demoralized by one
fishbone analysis after another? What would happen if we augmented
variance analysis with depth studies that help people to dream and
define the very visions of quality standards? How about a star
stories program to generate a narrative rich environment—where
customers are asked to share stories of encounters with exceptional
employees? How about a gathering with senior executives so we can
celebrate our learning’s with them, share with them how seeing the
positive has changed our work and family lives, and even recruit
them to join the PCN?
The pilot now had a momentum all its own. The immediate response—an
avalanche of requests for participation—confirmed that there were
large numbers at GTE ready to be called to the task of positive
change. To grow the network by the 100s, even thousands, it was
decided to do a ten region training session, all linked and
downloaded by satellite conferencing. A successful pilot of three
sites—Seattle, Indianapolis, and Dallas—confirmed the same kind of
energy and response could happen through distancetechnologies.
Quite suddenly the power of a 1000 person network caught people’s
attention. Just imagine the 1000 “students” of organization life
coming together in a year at an AI Summit to share learning from
10,000 innovations discovered at GTE. Very rapidly, by connecting
and consistently noticing breakthroughs, new patterns of organizing
would become commonplace knowledge. Changes would happen not by
organized confrontation, diagnosis, burning platforms, or piecemeal
reform but through irresistibly vibrant and real visions. And when
everyone’s awareness grows at the same time—that basic change is
taking place in this area and that area, it is easier to coalesce a
new consensus that fundamental change is possible. PCN was becoming
a lightning rod for energy and enthusiasm we all greatly
underestimated. Then the unions raised questions. There were
serious concerns, including the fact that they were not consulted
in the early stages. We were told the initiative was over. There
was to be a meeting of the unions and GTE at the Federal Mediation
Offices in Washington D.C. to put the whole thing to rest.
But at the meeting with the IBEW and the CWA, leaders from both
groups said they saw something fresh and unique about AI. They
agreed to bring 200 union leaders together for a 2-day
introduction. Their purpose: “to evaluate AI…to see if it should
have any place in the future at GTE”. A month later, the session
takes place. It looks like it is going pretty well and then the
moment of decision. Tables of eight were instructed to evaluate the
ideas and cast a vote as a group: “yes, we endorse moving forward
with AI” or “No, we withhold endorsement”. For thirty minutes the
30 groups deliberated. Dan Young calls the vote. Tensions are felt.
“Table one, how do you vote?” The response was ready: “we vote 100%
for moving forward with AI and feel this is an historic opportunity
for the whole system”. Then the next table: “We vote 100% with a
caveat—that every person at GTE have the opportunity to get the AI
training, and that all projects going forward be done in
partnership, the unions and the company”. On and on the vote goes.
30 tables speak. 30 tables vote. Every single one votes to move
forward. It was stunning. Eight months later AI is combined with
the “conflictive partnership” model of John Calhoun Wells of the
Federal Mediation Services at the kickoff session and announcement
of a new era of partnership. The historic statement of Partnership
states: “The company and the Unions realize that traditional
adversarial labor-management relations must change in order to
adapt to the new global telecommunications marketplace. It is
difficult to move to cooperation in one quantum leap. However the
company and the Unions have agreed to move in a new direction. This
new direction emphasizes partnership…”
AI accelerates the nonlinear interaction of organization
breakthroughs, putting them together with historic, positive
traditions and strengths to create a “convergence zone”
facilitating the collective repatterning of human systems. At some
point, apparently minor positive discoveries connect in
accelerating manner and quantum change, a jump from one state to
the next that cannot be achieved through incremental change alone,
becomes possible. What is needed, as the Destiny Phase of AI
suggests, are the network-like structures that liberate not only
the daily search into qualities and elements of an organization’s
positive core but the establishment of a convergence zone for
people to empower one another—to connect, cooperate, and co-create.
Changes never thoughtpossible are suddenly and democratically
mobilized when people constructively appropriate the power of the
positive core and simply… let go of accounts of the negative.
But then the question is always voiced: “What do we do with the
real problems?”
Basic Principles of Appreciative Inquiry
To address this question in anything other than Pollyannaish terms
we need to at least comment on the generative-theoretical work that
has inspired and given strength too much of AI in practice. Here
are five principles and scholarly streams we consider as central to
AI’s theory-base of change.
The Constructionist Principle: Simply stated— human knowledge and
organizational destiny are interwoven. To be effective as
executives, leaders, change agents, etc., we must be adept in the
art of understanding, reading, and analyzing organizations as
living, human constructions. Knowing (organizations) stands at the
center of any and virtually every attempt at change. Thus, the way
we know is fateful.
At first blush this statement appears simple and obvious enough. We
are, as leaders and change agents, constantly involved in
knowing/inquiring/reading the people and world around us—doing
strategic planning analysis, environmental scans, needs analysis,
assessments and audits, surveys, focus groups, performance
appraisals, and so on. Certainly success hinges on such modes of
knowing. And this is precisely where things get more interesting
because throughout the academy a revolution is afoot, alive with
tremendous ferment and implication, in regards to modernist views
of knowledge. In particular, what is confronted is the Western
conception of objective, individualistic, historic knowledge—“a
conception that has insinuated itself into virtually all aspects of
modern institutional life” (Gergen, 1985, P. 272). At stake are
questions that pertain to the deepest dimensions of our being and
humanity: how we know what we know, whose voices and
interpretations matter, whether the world is governed by external
laws independent of human choices and consciousness, and where is
knowledge to be located (in the individual “mind”, or out there
“externally” in nature or impersonal structures)? At stake are
issues that are profoundly fundamental, not just for the future of
social science but for the trajectory of all our lives.
In our view, the finest work in this area, indeed a huge extension
of the most radical ideas in Lewinian thought, can be found in Ken
Gergen’s Toward Transformation in Social Knowledge (1982) and
Realities and Relationships: Soundings In Social Construction
(1994). What Gergen does, in both of these, is synthesize the
essential whole of the post modern ferment and crucially takes it
beyond disenchantment with the old and offers alternative
conceptions of knowledge, fresh discourses on human functioning,
new vistas for human science, and exciting directions for
approaching change. Constuctionism is an approach to human science
and practice which replaces the individual with the relationship as
the locus of knowledge, and thus is built around a keen
appreciation of thepower of language and discourse of all types
(from words to metaphors to narrative forms, etc.) to create our
sense of reality—our sense of the true, the good, the
possible.
Philosophically it involves a decisive shift in western
intellectual tradition from cogito ergo sum, to communicamus ergo
sum and in practice constructionism replaces absolutist claims or
the final word with the never ending collaborative quest to
understand and construct options for better living. The purpose of
inquiry, which is talked about as totally inseparable and
intertwined with action, is the creation of “generative theory”,
not so much mappings or explanations of yesterday’s world but
anticipatory articulations of tomorrow’s possibilities.
Constructionism, because of its emphasis on the communal basis of
knowledge and its radical questioning of everything that is
taken-for-granted as “objective” or seemingly immutable, invites us
to find ways to increase the generative capacity of knowledge.
However there are warnings: “Few are prepared”, says Gergen (1985,
p. 271) “for such a wrenching, conceptual dislocation. However, for
the innovative, adventurous and resilient, the horizons are
exciting indeed.” This is precisely the call AI has responded to.
Principle number two takes it deeper.
The Principle of Simultaneity: Here it is recognized that inquiry
and change are not truly separate moments, but are simultaneous.
Inquiry is intervention. The seeds of change—that is, the things
people think and talk about, the things people discover and learn,
and the things that inform dialogue and inspire images of the
future—are implicit in the very first questions we ask. The
questions we ask set the stage for what we “find”, and what we
“discover” (the data) becomes the linguistic material, the stories,
out of which the future is conceived, conversed about, and
constructed.
One of the most impactful things a change agent or practitioner
does is to articulate questions. Instinctively, intuitively and
tacitly we all know that research of any kind can, in a flash,
profoundly alters the way we see ourselves, view reality, and
conduct our lives. Consider the economic poll, or the questions
that led to the discovery of the atom bomb, or the surveys that,
once leaked, created a riot at a unionized automobile plant in
London (see Cooperrider and Srivastva, 1987). If we accept the
proposition that patterns of social-organizational action are not
fixed by nature in any direct biological or physical way, that
human systems are made and imagined in relational settings by human
beings (socially constructed), then attention turns to the source
of our ideas, our discourses, our researches—that is our questions.
Alterations in linguistic practices—including the linguistic
practice of crafting questions—hold profound implications for
changes in social practice.
One great myth that continues to dampen the potential here is the
understanding that first we do an analysis, and then we decide on
change. Not so says the constructionist view. Even the most
innocent question evokes change—even if reactions are simply
changes in awareness, dialogue, feelings of boredom, or even
laughter. When we consider the possibilities in these terms, that
inquiry and change are a simultaneous moment, we begin reflecting
anew. It is not so much “Is my question leading to right or wrong
answers?” but rather “What impact is my question having on our
lives together…is it helping togenerate conversations about the
good, the better, the possible… is it strengthening our
relationships?”
The Poetic Principle: A metaphor here is that human organizations
are a lot more like an open book than, say, a machine. An
organization’s story is constantly being co-authored. Moreover,
pasts, presents, or futures are endless sources of learning,
inspiration, or interpretation—precisely like, for example, the
endless interpretive possibilities in a good piece of poetry or a
biblical text. The important implication is that we can study
virtually any topic related to human experience in any human system
or organization. We can inquire into the nature of alienation or
joy, enthusiasm or low morale, efficiency or excess, in any human
organization. There is not a single topic related to organizational
life that we could not study in any organization.
What constuctionism does is remind us that it is not the “world out
there” dictating or driving our topics of inquiry but again the
topics are themselves social artifacts, products of social
processes (cultural habits, typifying discourses, rhetoric,
professional ways, power relations). It is in this vein that AI
says let us make sure we are not just reproducing the same worlds
over and over again because of the simple and boring repetition of
our questions (not “one more” morale survey which everybody can
predict the results ahead of time). AI also says, with a sense of
excitement and potential, that there can be great gains made in a
better linking of the means and ends of inquiry. Options now begin
to multiply. For example, informally, in many talks with great
leaders in the NGO world (Save the Children, World Vision), we have
begun to appreciate the profound joy that CEO’s feel as “servant
leaders”-- and the role this positive affect potentially plays in
creating healthy organizations. But then one questions: is there a
book on the Harvard Business book-list, or anywhere for that
matter, on Executive Joy ? And even if there isn’t… does this mean
that joy has nothing to do with good leadership, or healthy human
systems? Why aren’t we including this topic in our change efforts?
What might happen if we did?
What the poetic principle invites is re-consideration of aims and
focus of any inquiry in the domain of change management. For it is
becoming clearer that our topics, like windsocks, continue to blow
steadily onward in the direction of our conventional gaze. As we
shall soon explore, seeing the world as a problem has become “very
much a way of organizational life”.
The Anticipatory Principle: The infinite human resource we have for
generating constructive organizational change is our collective
imagination and discourse about the future. One of the basic
theorems of the anticipatory view of organizational life is that it
is the image of the future, which in fact guides what might be
called the current behavior of any organism or organization. Much
like a movie projector on a screen, human systems are forever
projecting ahead of themselves a horizon of expectation (in their
talk in the hallways, in the metaphors and language they use) that
brings the future powerfully into the present as a mobilizing
agent. To inquire in ways that serves to refashion anticipatory
reality—especially the artful creation of positive imagery on
acollective basis--may be the most prolific thing any inquiry can
do.Our positive images of the future lead our positive actions—this
is the increasingly energizing basis and presupposition of
Appreciative Inquiry.
Whether we are talking about placebo studies in medicine (Ornstein
and Sobel, 1987); reviews of a myriad of studies of the Pygmalion
dynamic in the classroom (Jussim, 1986); studies of the rise and
fall of cultures (Boulding,1966; Polak, 1973); research into the
relationships between optimism and health (Seligman, 1990 );
studies of positive self-monitoring and ways for accelerating
learning (Kirschenbaum, 1984 ); analysis of the importance of
imbalanced, positive inner dialogue to personal and relational
well-being (Schwartz, 1986 ); research on positive mood states and
effective decision making (Isen, 1983); studies from the domain of
“conscious evolution" (Hubbard, 1998 ); or theories on how positive
noticing of even “small wins” can reverberate throughout a system
and change the world (Weick, 1984 )—the conclusions are converging
on something Aristotle said many years ago. “A vivid imagination”,
he said “ compels the whole body to obey it”. In the context of
more popular writing, Dan Goleman (1987), in a well-written New
York Times headline-article declares “Research Affirms the Power of
Positive Thinking”.
The Positive Principle. This last principle is not so abstract. It
grows out of years of experience with appreciative inquiry. Put
most simply, it has been our experience that building and
sustaining momentum for change requires large amounts of positive
affect and social bonding—things like hope, excitement,
inspiration, caring, camaraderie, sense of urgent purpose, and
sheer joy in creating something meaningful together. What we have
found is that the more positive the question we ask in our work the
more long lasting and successful the change effort. It does not
help, we have found, to begin our inquiries from the standpoint of
the world as a problem to be solved. We are more effective the
longer we can retain the spirit of inquiry of the everlasting
beginner. The major thing we do that makes the difference is to
craft and seed, in better and more catalytic ways, the
unconditional positive question.
Although the positive has not been paraded as a central concept in
most approaches to organization analysis and change, it is clear we
need no longer be shy about bringing this language more carefully
and prominently into our work. And personally speaking, it is so
much healthier. We love letting go of “fixing” the world. We love
doing interviews, hundreds of them, into moments of organizational
“life”. And we are, quite frankly, more effective the more we are
able to learn, to admire, to be surprised, to be inspired alongside
the people with whom we are working. Perhaps it is not just
organizations—we too become what we study. So suggested, over and
over again, is the life-promoting impact of inquiry into the good,
the better, and the possible. A theory of affirmative basis of
human action and organizing is emerging from many quarters—social
contructionism, image theory, conscious evolution and the like. And
the whole thing is beginning, we believe, to make a number of our
change-management traditions look obsolete.
Appreciative Inquiry and Power in Organizations
We could have easily called this section “Eulogy for Problem
Solving”. In our view, the problem solving paradigm, while once
perhaps quite effective, is simply out of sync with the realities
of today’s virtual worlds (Cooperrider, 1996). Problem solving
approaches to change are painfully slow (always asking people to
look backward to yesterday’s causes); they rarely result in new
vision (by definition we can describe something as a problem
because we already, perhaps implicitly, assume an ideal, so we are
not searching to expansive new knowledge of better ideals but
searching how to close “gaps”); and in human terms problem
approaches are notorious for generating defensiveness (it is not my
problem but yours). But our real concern, from a social
constructionist perspective, has to do with relations of power and
control. It is the most speculative part of this chapter; and
hopefully, it better illuminates the potentials advocated by AI. In
particular is the more conscious linking of language, including the
language of our own profession, to change. Words do create
worlds—even in unintended ways.
It was an unforgettable moment in a conference on AI for inner city
change agents, mostly community mobilizers from the Saul Alinsky
school of thought (Rules for Radicals), in Chicago. After two days
a participant challenges: “This is naïve…have you ever worked in
the depths of the inner city, like the Cabrini Green public housing
projects? You’re asking me to go in and ‘appreciate’ it…just
yesterday I’m there and the impoverished children are playing
soccer, not with a ball, no money for that, but with a dead rat.
Tell me about appreciative inquiry in the housing projects!”
It was a powerful question. It was one that made us go deeper
theoretically. At one level we were arguing typical approaches to
problem diagnosis, including the Alinsky confrontation methods,
would work, but at about half the speed of AI. But then as we
explored the subject of the cultural consequences of deficit
discourse we began seeing a disconcerting relationship between the
society-wide escalation of deficit-based change methods and the
erosion of people power. The analysis, from here, could proceed
from virtually any “professional” discipline—the diagnostic
vocabularies of social work, medicine, organization development,
management, law, accounting, community development, editing—but
lets begin with psychology and the social sciences (ample linkage
will be made to our own field). Ken Gergen’s (1994) work, again, is
at the forefront for anyone wanting something more than a
suggestive summary.
Consider the following characterizations of the self: impulsive
personality, narcissism, anti-social personality, reactive
depressive, codependent, self-alienated, type-A, paranoid,
stressed, repressed, authoritarian, midlife crisis. These are all
terms commonly used by the mental-health professions and are now
common among people in the culture itself. But importantly, these
terms, and several thousand others (Gergen 1994), have come into
conventional usage only within the present century, many in only
the last decade. But something else is noteworthy: the
terminology’s discredit, draw attention to problems, shortcomings,
and incapacity’s. Interestingly, the trajectory of the
“professional” development of vocabularies of human deficit is
rising at geometric rates, correlated as might be expected with the
sheer growth in numbers of the profession. In1892 when the American
Psychological association was founded there were 31 members. By
1906 there were 181. The next thirty-one years witnessed an
expansion of almost a hundredfold, to over 3000. In the next
twenty-two years the figure grew again by twenty times, over
63,000. Add to this similar growth figures in social work,
psychiatry, community development, and organization development and
one realizes that the spiraling production of languages of deficit
have become quite a growth industry. By 1980 mental illness was the
third most expensive category of health disorder in the United
States at more than $20 billion annually. By 1983, the costs for
mental illness, exclusive of alcoholism and drug abuse, were
estimated to be almost $73 billion. We have no figures for the
consulting industry, but we can guess. While intentions are good,
argues Gergen, some of the unintended consequences may not
be.
From a constructionist perspective one realizes that words do not
so much innocently “mirror” a world out there as they become
vehicles for coordinating our actions with one another. Words in
any profession function a bit like tools of the trade. When I used
to give my son Matt a hammer, inevitably everything in the house
soon became a nail. What happens when the “scientifically”
legitimated vocabularies of human deficit become the common and
explicit tool kit of all? Gergen suggests not everything about it
is healthy. Such deficit discourse, when chronically used,
“generates a network of increasing entanglements for the culture at
large. Such entanglements are not only self serving for the
professions, they also add exponentially to the sense of human
misery” (1994 p. 142).
In particular, deficit based change approaches have an unfortunate
propensity to reinforce hierarchy, wherein “less than ideal”
individuals, who learn to accept what sometimes becomes a lifelong
label, are encouraged to enter “treatment programs” under expert
supervision; to erode community, wherein the mental health
professions appropriate the process of interpersonal realignment
that might otherwise (in other eras) have happened in a
nonprofessional contexts like the family or community; to instill a
sense of self-enfeeblement,wherein deficit terms essentialize the
person and like a birthmark or fingerprint, the deficit is expected
to inevitably manifest itself into many aspects of their lives (it
is a “thing”); to stimulate endless vocabulary expansion wherein
people increasingly construct their problems in the professional
languages (diagnosing each other) and seek more help which in turn
increased the numbers in the profession who are rewarded when they
expand the vocabulary—“to explore a new disorder within the mental
health sciences is not unlike discovering a new star in astronomy
(Gergen p.159)”. Gergen sums up: “As I am proposing, when the
culture is furnished with a professionally rationalized language of
mental deficit and people are increasingly understood according to
this language, the population of “patients” expands. This
population, in turn, forces the profession to extend its
vocabulary, and thus the array of mental deficit terms available
for cultural use (Gergen p.161). Is there no exit from such
progressive infirmity?
After talking this over with the people in the inner city Chicago
conference—and tracing the vocabularies of human deficit not only
to the rise of the professions but also to the rise of bureaucracy,
skeptical science, original sin theological accounts, the cynical
media—the Alinsky trained activist sat down in a gasp. He said: “in
the name of entertainment my people are being fed negative views of
human violence—and they aresurrounded by endless description of
their negative “needs” their “problem lives”. Even in my methods,
the same. And what do I see? I see people asleep in front of their
TVs. Unable to move, like sleeping dogs. Yes they have voice in the
housing project assessments. But it is a certain kind of voice…it
is visionless voice. They get to confirm the deficit analysis; all
the reports are the same. “Yes” they say, “The reports are true”.
What is hitting me right now is how radical the AI message might
be. Marx could have said it better: perhaps the vocabularies of
human deficit are the opiates of the masses. People have voice in
the analyses—this involvement is what we fought for. But people are
not mobilized by it anymore. No, they are asleep. Visionless voice
is probably worse than no voice.
Elsewhere we have cautioned, in our own discipline, that it is not
so much the problem solving methodologies per se that are of
central concern, but the growing sense that we all, throughout the
culture, have taken the tools a step further. It is not so much
that organizations have problems, they are problems (see figure two
on page 28). Somewhere a shift of this kind has taken place. Once
accepted as fundamental truth about organizations, virtually
everything in change-management becomes infused with a deficit
consciousness. For example, as French and Bell (1995) define it,
“Action-research is both an approach to problem solving—a model or
paradigm, and a problem solving process—a series of activities and
events” (p. 88). Levinson, in the classic on Organizational
Diagnosis (1972) likens it to therapy—“like a therapeutic or
teaching relationship it should be an alliance of both parties to
discover and resolve these problems…looking for experiences which
appear stressful to people. What kinds of occurrences disrupt or
disorganize people? (p. 37). Chris Argyris, again in another
classic, asserts: One condition that seems so basic as to be
defined as axiomatic is the generation of valid information…Valid
information is that which describes the factors, plus their
interrelationships, that create the problem (1970, pp.16-17).
Tough questions remain about power and deficit discourse. And of
course there are an array of new innovations in the field, many in
this volume, that are signaling significant departures. So at this
point all we want to do is make a call for reflection and caution,
taking a lesson from the wisdom of anthropology—beware of the solid
truths of one’s own culture.
Conclusion
To be sure, Appreciative Inquiry (AI) begins an adventure. The urge
and call to adventure has been sounded by many people and many
organizations, and it will take many more to fully explore the vast
vistas that are now appearing on the horizon.
As said at the outset, we believe we are infants when it comes to
our understanding of appreciative processes of knowing and social
construction. Yet we are increasingly clear the world is ready to
leap beyond methodologies of deficit based changes and enter a
domain that is life-centric. Organizations, says AI theory, are
centers of human relatedness, first and foremost, and relationships
thrive where there is an appreciative eye—when people see the best
in one another, when they share their dreams and ultimateconcerns
in affirming ways, and when they are connected in full voice to
create not just new worlds but better worlds. The velocity and
largely informal spread of the appreciative learnings suggests, we
believe, a growing sense of disenchantment with exhausted theories
of change, especially those wedded to vocabularies of human
deficit, and a corresponding urge to work with people, groups, and
organizations in more constructive, positive, life-affirming, even
spiritual ways. AI, we hope it is being said, is more than a simple
4-D cycle of discovery, dream, design, and destiny; what is being
introduced is something deeper at the core.Perhaps our inquiry must
become the positive revolution we want to see in the world. Albert
Einstein’s words clearly compel: “There are only two ways to live
your life. One is as though nothing is a miracle. The other is as
though everything is a miracle”.
18
14
13
12
10
In: Operations Management
Section 1.
You must use sub-queries to answer section 1
questions.
Q1: List the length of the longest track in the 'metal' genre.
Q2: List the artistid, artistname and entrydate of all artists whose entrydate is earlier than everyone who has a 'directmail' leadsource.
Q3: List the artistid, artistname and entrydate of all artists whose entrydate is earlier than anyone who has a 'directmail' leadsource.
Q4: List the artistname and entrydate of the artist with the earliest entry date.
Q5: List the track titles of all titles in the 'alternative' genre.
Q6: List all genres from the Genre table that are not represented in the Titles table.
Q7*: List track titles and lengths of tracks with a length longer than all tracks of the 'metal' genre.
(Hint: This requires sub-query within a sub-query)
Q8: List the track title with longest length in seconds.
Section 2
You must use either Equi-join or Inner join: (you are free to choose anyone of the two kinds of joins)
Q9: List the album title and the title of all tracks recorded in StudioID 1
Q10: List each title from the Title table along with the name of the studio where it was recorded.
Q11: Find the name of the sales person who works with the member with last name 'Alvarez'
Q12: List the names of all members from California and the names of the salespeople that they work with.
Q13: List the names of all artists who have recorded more than one title and the number of titles they have recorded.
Q14: Report the name of the title and number of tracks for any title with fewer than 9 tracks.
DROP TABLES IF EXISTS Artists,Genre, Members, Titles,
Tracks,SalesPeople,Studios,XrefArtistsMembers;
DROP TABLES IF EXISTS
Authors,Publishers,Titles,Title_Authors,Royalties;
DROP TABLES IF EXISTS
Products,Customers,Orders,Order_details;
DROP TABLES IF EXISTS Sailors,Boats,Reserves;
CREATE TABLE Artists (
ArtistID int,
ArtistName varchar (50) NOT NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
Country varchar (20) NULL ,
WebAddress varchar (40) NULL ,
EntryDate date NULL ,
LeadSource varchar (10) NULL
);
Insert Into Artists Values(1,'The
Neurotics','Peterson','NC','USA','www.theneurotics.com','2003-05-14','Directmail');
Insert Into Artists Values(2,'Louis Holiday','Clinton','IL','USA'
,NULL,'2003-06-03','Directmail');
Insert Into Artists
Values(3,'Word','Anderson','IN','USA',NULL,'2003-06-08','Email');
Insert Into Artists
Values(5,'Sonata','Alexandria','VA','USA','www.classical.com/sonata','2003-06-08','Ad');
Insert Into Artists Values(10,'The
Bullets','Alverez','TX','USA',NULL,'2003-08-10','Email');
Insert Into Artists Values(14,'Jose MacArthur','Santa
Rosa','CA','USA','www.josemacarthur.com','2003-08-17','Ad');
Insert Into Artists Values(15,'Confused','Tybee
Island','GA','USA',Null,'2003-09-14','Directmail');
Insert Into Artists Values(17,'The Kicks','New
Rochelle','NY','USA',NULL,'2003-12-03','Ad');
Insert Into Artists
Values(16,'Today','London','ONT','Canada','www.today.com','2003-10-07','Email');
Insert Into Artists Values(18,'21 West
Elm','Alamaba','VT','USA','www.21westelm.com','2003-02-05','Ad');
Insert Into Artists
Values(11,'Highlander','Columbus','OH','USA',NULL,'2002-08-10','Email');
CREATE TABLE Genre (
Genre varchar (15)
);
Insert into Genre Values('alternative');
Insert into Genre Values('classical');
Insert into Genre Values('jazz');
Insert into Genre Values('metal');
Insert into Genre Values('R&B');
Insert into Genre Values('rap');
Insert into Genre Values('pop');
CREATE TABLE Members (
MemberID int ,
FirstName varchar (25) NULL ,
LastName varchar (25) NULL ,
Address varchar (60) NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
PostalCode varchar (10) NULL ,
Country varchar (20) NULL ,
HomePhone varchar (16) NULL ,
WorkPhone varchar (16) NULL ,
EMail varchar (40) NULL ,
Gender char (1) NULL ,
Birthday date NULL ,
SalesID smallint NULL
);
Insert Into Members Values(10,'Roberto','Alvarez','Rt
1','Anderson','IN','46019','USA','7651552983','7651628837','[email protected]','M','1968-01-18',2);
Insert Into Members Values(31,'Jose','MacArthur','51444
Vine','Santa
Rosa','CA','99999','USA','6331289393',Null,'[email protected]','M','1978-06-24',1);
Insert Into Members Values(13,'Mary','Chrisman','1772 East
117th','Fishers','IN','46123','USA','3171820387',Null,'[email protected]','F','1973-03-01',1);
Insert Into Members Values(15,'Warren','Boyer','167 Alamo
Dr','Alverez','TX','75601','USA','8221722883',Null,'[email protected]','M','1969-04-19',2);
Insert Into Members Values(32,'Doug','Finney','2020
Dubois','Savannah','GA','30003','USA','9821222929',Null,'[email protected]','M','1963-08-04',3);
Insert Into Members Values(19,'Terry','Irving','18a 7th St','Tybee
Island','GA','30004','USA','5411252093',Null,Null,'M','1959-06-22',3);
Insert Into Members Values(21,'Michelle','Henderson','201
Bonaventure','Savannah','GA','30005','USA','8221928273',Null,Null,'F','1964-03-15',2);
Insert Into Members Values(34,'William','Morrow','PO Box 1882','New
Rochelle','NY','10014','USA','9981722928',Null,'[email protected]','M','1965-03-17',2);
Insert Into Members Values(29,'Frank','Payne','5412 Clinton','New
Rochelle','NY','10014','USA','9981737464',Null,Null,'M','1960-01-17',1);
Insert Into Members Values(35,'Aiden','Franks','167 East
38th','Alverez','TX','75601','USA','8321729283','8321723833','[email protected]','M','1983-09-02',2);
Insert Into Members Values(3,'Bryce','Sanders','PO Box
1292','Peterson','NC','27104','USA','6441824283',Null,'[email protected]','M','1966-06-11',2);
Insert Into Members Values(14,'Carol','Wanner','787 Airport
Rd','Alverez','TX','75601','USA','6831223944',Null,Null,'F','1978-11-08',3);
Insert Into Members Values(33,'Brian','Ranier','23 Gregory
Lane','London','ONT','M6Y 2Y7
','Canada','6231842933',Null,Null,'M','1957-10-19',3);
Insert Into Members Values(7,'Marcellin','Lambert','142 Sample
Rd','Alexandria','VA','20102','USA','8331929302',Null,'[email protected]','M','1959-11-14',3);
Insert Into Members Values(8,'Caroline','Kale','1515 Stone Church
Rd','Allen','VA','20321','USA','7321223742',Null,Null,'F','1956-05-30',3);
Insert Into Members Values(9,'Kerry','Fernandez','15
Midway','Lynchberg','VA','21223','USA','2211229384','2211223939',Null,'M','1962-01-16',1);
Insert Into Members Values(26,'Tony','Wong','115 Maple
St','McKensie','ONT','M8H
3T1','Canada','3311692832','3311692822','[email protected]','M','1955-11-01',2);
Insert Into Members
Values(18,'Bonnie','Taft','RR4','Alamaba','VT','05303','USA','3721223292',Null,'[email protected]','F','1960-09-21',1);
Insert Into Members Values(20,'Louis','Holiday','15 Davis
Ct','Clinton','IL','63882','USA','1451223838',Null,Null,'M','1969-07-27',2);
Insert Into Members
Values(22,'Bobby','Crum','RR2','Pine','VT','05412','USA','1831828211',Null,Null,'M','1965-06-10',3);
Insert Into Members Values(28,'Vic','Cleaver','100
Maple','Reston','VT','05544','USA','8111839292',Null,Null,'M','1957-02-10',2);
Insert Into Members Values(30,'Roberto','Goe','14 Gray
Rd','Columbus','OH','48110','USA','2771123943',Null,Null,'M','1967-09-12',1);
Insert Into Members Values(36,'Davis','Goodman','2020 Country
Rd','Columbus','OH','48318','USA','2771152882','2771128833','[email protected]','M','1980-10-27',2);
CREATE TABLE SalesPeople (
SalesID smallint ,
FirstName varchar (20) NOT NULL ,
LastName varchar (20) NOT NULL ,
Initials varchar (3) NULL ,
Base decimal(5,2) NULL,
Supervisor smallint NUll
);
Insert into SalesPeople
Values(1,'Bob','Bentley','bbb',100,4);
Insert into SalesPeople
Values(2,'Lisa','Williams','lmw',300,4);
Insert into SalesPeople
Values(3,'Clint','Sanchez','cls',100,1);
Insert into SalesPeople Values(4,'Scott','Bull','sjb',Null,
Null);
CREATE TABLE Studios (
StudioID int,
StudioName varchar (40) NULL ,
Address varchar (60) NULL ,
City varchar (25) NULL ,
Region varchar (15) NULL ,
PostalCode varchar (10) NULL ,
Country varchar (20) NULL ,
WebAddress varchar (40) NULL ,
Contact varchar (50) NULL ,
EMail varchar (40) NULL ,
Phone varchar (16) NULL ,
SalesID smallint NULL
);
Insert Into Studios Values(1,'MakeTrax','3000 S St Rd
9','Anderson','IN','46012','USA','www.maketrax.com','Gardner
Roberts','[email protected]','7651223000',3);
Insert Into Studios Values(2,'Lone Star Recording','PO Box
221','Davis','TX','76382','USA','www.lsrecords.com','Manuel
Austin','[email protected]','8821993748',2);
Insert Into Studios Values(3,'Pacific Rim','681 PCH','Santa
Theresa','CA','99320','USA','www.pacrim.org','Harry
Lee','[email protected]','3811110033',2);
CREATE TABLE Titles (
TitleID int ,
ArtistID int NULL ,
Title varchar (50) NULL ,
StudioID int NULL ,
UPC varchar (13) NULL ,
Genre varchar (15) NULL
);
Insert Into Titles Values(1,1,'Meet the
Neurotics',1,'2727366627','alternative');
Insert Into Titles Values(3,15,'Smell the
Glove',2,'1283772282','metal');
Insert Into Titles Values(4,10,'Time
Flies',3,'1882344222','alternative');
Insert Into Titles Values(5,1,'Neurotic
Sequel',1,'2828830202','alternative');
Insert Into Titles
Values(6,5,'Sonatas',2,'3999320021','classical');
Insert Into Titles Values(7,2,'Louis at the
Keys',3,'3838227111','jazz');
CREATE TABLE Tracks (
TitleID int NOT NULL ,
TrackNum smallint NOT NULL ,
TrackTitle varchar (50) NULL ,
LengthSeconds smallint NULL ,
MP3 smallint NULL ,
RealAud smallint NULL
);
Insert Into Tracks Values(1,1,'Hottie',233,1,1);
Insert Into Tracks Values(1,2,'Goodtime March',293,1,1);
Insert Into Tracks Values(1,3,'TV Day',305,1,1);
Insert Into Tracks Values(1,4,'Call Me an Idiot',315,1,1);
Insert Into Tracks Values(1,5,'25',402,1,1);
Insert Into Tracks Values(1,6,'Palm',322,1,1);
Insert Into Tracks Values(1,7,'Front Door',192,1,1);
Insert Into Tracks Values(1,8,'Where''s the Rain',175,1,1);
Insert Into Tracks Values(3,1,'Fat Cheeks',352,1,1);
Insert Into Tracks Values(3,2,'Rocky and Natasha',283,1,1);
Insert Into Tracks Values(3,3,'Dweeb',273,1,1);
Insert Into Tracks Values(3,4,'Funky Town',252,1,1);
Insert Into Tracks Values(3,5,'Shoes',182,1,1);
Insert Into Tracks Values(3,6,'Time In - In Time',129,1,1);
Insert Into Tracks Values(3,7,'Wooden Man',314,0,0);
Insert Into Tracks Values(3,8,'UPS',97,0,0);
Insert Into Tracks Values(3,9,'Empty',182,0,0);
Insert Into Tracks Values(3,10,'Burrito',65,0,0);
Insert Into Tracks Values(4,1,'Bob''s Dream',185,1,1);
Insert Into Tracks Values(4,2,'My Wizard',233,1,1);
Insert Into Tracks Values(4,3,'Third''s Folly',352,1,1);
Insert Into Tracks Values(4,4,'Leather',185,1,1);
Insert Into Tracks Values(4,5,'Hot Cars Cool
Nights',192,1,1);
Insert Into Tracks Values(4,6,'Music in You',204,1,1);
Insert Into Tracks Values(4,7,'Don''t Care About
Time',221,1,1);
Insert Into Tracks Values(4,8,'Kiss',218,1,1);
Insert Into Tracks Values(4,9,'Pizza Box',183,1,1);
Insert Into Tracks Values(4,10,'Goodbye',240,1,1);
Insert Into Tracks Values(5,1,'Song 1',285,1,1);
Insert Into Tracks Values(5,2,'Song 2',272,1,1);
Insert Into Tracks Values(5,3,'Song 3',299,1,1);
Insert Into Tracks Values(5,4,'Song 4',201,1,1);
Insert Into Tracks Values(5,5,'Song 5',198,1,0);
Insert Into Tracks Values(5,6,'Song 6',254,1,0);
Insert Into Tracks Values(5,7,'Song 7',303,1,1);
Insert Into Tracks Values(5,8,'Song 8',230,1,0);
Insert Into Tracks Values(5,9,'Song 8 and 1/2',45,1,0);
Insert Into Tracks Values(6,1,'Violin Sonata No. 1 in D
Major',511,1,1);
Insert Into Tracks Values(6,2,'Violin Sonata No. 2 in A
Major',438,1,1);
Insert Into Tracks Values(6,3,'Violin Sonata No. 4 in E
Minor',821,1,0);
Insert Into Tracks Values(6,4,'Piano Sonata No. 1',493,1,0);
Insert Into Tracks Values(6,5,'Clarinet Sonata in E
Flat',399,1,0);
Insert Into Tracks Values(7,1,'I Don''t Know',201,1,0);
Insert Into Tracks Values(7,2,'What''s the Day',332,1,0);
Insert Into Tracks Values(7,3,'Sirius',287,1,0);
Insert Into Tracks Values(7,4,'Hamburger Blues',292,1,0);
Insert Into Tracks Values(7,5,'Road Trip',314,1,0);
Insert Into Tracks Values(7,6,'Meeting You',321,1,1);
Insert Into Tracks Values(7,7,'Improv 34',441,1,1);
Insert Into Tracks Values(7,8,'Hey',288,1,1);
CREATE TABLE XrefArtistsMembers (
MemberID int NOT NULL ,
ArtistID int NOT NULL ,
RespParty smallint NOT NULL
);
Insert into XrefArtistsMembers Values(20,2,1);
Insert into XrefArtistsMembers Values(31,14,1);
Insert into XrefArtistsMembers Values(3,1,1);
Insert into XrefArtistsMembers Values(10,3,1);
Insert into XrefArtistsMembers Values(13,3,0);
Insert into XrefArtistsMembers Values(7,5,1);
Insert into XrefArtistsMembers Values(8,5,0);
Insert into XrefArtistsMembers Values(9,5,0);
Insert into XrefArtistsMembers Values(32,15,0);
Insert into XrefArtistsMembers Values(19,15,1);
Insert into XrefArtistsMembers Values(21,15,0);
Insert into XrefArtistsMembers Values(34,17,1);
Insert into XrefArtistsMembers Values(29,17,0);
Insert into XrefArtistsMembers Values(15,10,1);
Insert into XrefArtistsMembers Values(35,10,0);
Insert into XrefArtistsMembers Values(14,10,0);
Insert into XrefArtistsMembers Values(33,16,1);
Insert into XrefArtistsMembers Values(26,16,0);
Insert into XrefArtistsMembers Values(18,18,1);
Insert into XrefArtistsMembers Values(28,18,0);
Insert into XrefArtistsMembers Values(22,18,0);
Insert into XrefArtistsMembers Values(30,11,1);
Insert into XrefArtistsMembers Values(36,11,0);
show tables;
In: Computer Science
Introduction Rent-a-Car is one of the two car rental agencies serving a small regional airport in the U.S. Midwest. Forty per cent (40%) of its customers are airline passengers and the remaining sixty per cent (60%) are dwellers of the small nearby college town who use rental cars for business and leisure trips. The airport is within two miles from campus and approximately six miles from the city center. It is easy to reach by car, taxi, or city bus. You are a manager of Rent-a-Car. Your fleet consists of 72 cars, of which 47 fall into the “economy” class and 25 in the “luxury” class. Whenever demand for cars in some class exceeds the number of cars available, additional vehicles can be delivered from the nearest company hub in the state capital 70 miles away. Alternatively, some customers unable to rent an economy-class car may be upgraded to a luxuryclass car at no extra cost to them. Your only competitor at this location has a more sophisticated system of car category tiers, which consist of Compact, Economy, Mid-size, and Large cars. Assignment: Part 1 In order to better understand your unit’s operating environment, you are asked to provide an estimate of the demand equation that would account for various factors that affect your customer traffic. Estimating the demand equation is useful for future analysis of your unit’s performance. You need to “request” the data for your empirical study. Specifically, (1) What are you planning to use as the dependent variable in your regression? (2) What other data would you need and can realistically get? You may request information for up to five independent variables. For each variable you “request”, provide reasons why you expect it to be important for your analysis and explain the expected sign of the relationship between the proposed independent variable and the dependent variable Assignment: Part 2 After you have prepared your request, access the data file provided and see what variables you have data for. Then estimate the demand function for Rent-a-Car using regression techniques. Submit a report (Word document) and your analysis (Excel file) for grading. Your report must include separate sections on your initial request for variables (Part 1 above) and any modifications or substitutions you needed to make based on the variables that were available. You will be assessed based on the case that you present as well as your analysis. Make sure you include an interpretation of all coefficients if your estimated demand function.
| Description of variables in the Rent-a-Car data set | |
| PownE | Average daily rate Rent-A-Car charged for its economy cars in a given week |
| PownL | Average daily rate Rent-A-Car charged for its luxury vehicles in a given week |
| Pcomp | Average daily rate of the only competitor across all vehicle categories |
| Session | Binary variable with 1 indicating weeks when college is in session |
| Weather | Number of days in a week with severe weather |
| Unemployment | Number of unemployed workers in the county as of Tuesday each week |
| FlghtWk | Number of flights (in- and outbound) serving the local airport that week |
| CancWk | Total number of flights cancelled that week |
| Holiday | Binary variable with 1 indicating weeks of national holidays (long weekends) |
| Wrecks | Number of major accidents that week |
| Discount | Number of customers in a given week using the 15 percent discount off the base rate offered through our affiliate partner, a credit card company |
| Upgrade | Number of customers who received a free upgrade to a luxury vehicle due to the unavailability of economy vehicles |
| TotalAd | Amount spent on local advertising each week |
| AdBlbd | Weekly spending on billboard ads |
| AdPaper | Weekly spending on ads in local newspapers, including the online version |
| AdTV | Weekly spending on ads placed with local TV |
| QE | Number of rental contracts initiated each week in the economy category |
| Q_length | Number of paid days of rentals, grouped by the agreement starting date |
| Age<25 | Number of rental agreements in a given week for which the person listed as the primary driver on the rental agreement was less than 25 years old |
| Age25_50 | Number of rental agreements for which the person listed as the primary driver on the rental agreement was between 25 and 50 years of age |
| Age51+ | Number of rental agreements in a given week for which the person listed as the primary driver on the rental agreement was 51 years of age or older |
| FleetAge | Average age of our fleet measured in weeks |
| BedTax |
Amounts collected from the 1% local hospitality tax in the county - this information is reported only on a monthly basis |
| Week | PownE | PownL | Pcomp | Session | Weather | Unempl | FlghtWk | CancWk | Holiday | Wrecks | Discount | Upgrades | TotalAd | AdBlbd | AdPaper | AdTV | QE | Q_length | Age <25 | Age 25-50 | Age 51+ | FleetAge | Bed'Tax |
| 1 | 29.99 | 37.99 | 37.75 | 0 | 4 | 701 | 41 | 9 | 0 | 22 | 12 | 8 | 430 | 430 | 0 | 0 | 87 | 334 | 9 | 64 | 14 | 50.3 | 104025.67 |
| 2 | 29.99 | 41.99 | 41.5 | 0 | 1 | 739 | 41 | 2 | 0 | 16 | 7 | 0 | 430 | 430 | 0 | 0 | 76 | 327 | 13 | 46 | 17 | 51.3 | |
| 3 | 24.99 | 26.99 | 35.25 | 0 | 2 | 814 | 41 | 3 | 0 | 12 | 6 | 5 | 430 | 430 | 0 | 0 | 82 | 315 | 20 | 51 | 11 | 52.3 | |
| 4 | 28.99 | 37.99 | 35.5 | 1 | 1 | 880 | 47 | 0 | 1 | 6 | 8 | 2 | 430 | 430 | 0 | 0 | 77 | 275 | 24 | 42 | 11 | 53.3 | |
| 5 | 24.99 | 36.99 | 24.5 | 1 | 0 | 881 | 47 | 0 | 0 | 10 | 10 | 0 | 430 | 430 | 0 | 0 | 76 | 316 | 20 | 51 | 5 | 54.3 | 70251.75 |
| 6 | 29.99 | 43.99 | 28.75 | 1 | 3 | 799 | 47 | 6 | 0 | 17 | 2 | 4 | 430 | 430 | 0 | 0 | 78 | 301 | 15 | 59 | 4 | 55.3 | |
| 7 | 28.99 | 44.99 | 34.5 | 1 | 1 | 857 | 47 | 0 | 0 | 20 | 3 | 1 | 815 | 815 | 0 | 0 | 81 | 355 | 14 | 55 | 12 | 56.3 | |
| 8 | 21.99 | 25.99 | 33 | 1 | 0 | 871 | 47 | 0 | 0 | 4 | 1 | 8 | 815 | 815 | 0 | 0 | 91 | 332 | 20 | 61 | 10 | 57.3 | |
| 9 | 26.76 | 48.99 | 29.5 | 1 | 0 | 870 | 47 | 0 | 0 | 12 | 1 | 0 | 815 | 815 | 0 | 0 | 77 | 260 | 20 | 40 | 17 | 58.3 | 80998.15 |
| 10 | 28.99 | 42.99 | 38.25 | 1 | 2 | 889 | 47 | 3 | 0 | 19 | 9 | 1 | 815 | 815 | 0 | 0 | 84 | 317 | 22 | 44 | 18 | 59.3 | |
| 11 | 25.99 | 37.99 | 28 | 1 | 0 | 855 | 47 | 0 | 0 | 9 | 4 | 1 | 2197 | 815 | 1382 | 0 | 76 | 291 | 14 | 49 | 13 | 60.3 | |
| 12 | 25.99 | 37.99 | 30.25 | 1 | 0 | 911 | 48 | 2 | 0 | 4 | 5 | 0 | 2520 | 815 | 1705 | 0 | 75 | 350 | 10 | 54 | 11 | 61.3 | |
| 13 | 25.99 | 28.99 | 31.5 | 1 | 0 | 894 | 48 | 0 | 0 | 15 | 1 | 0 | 1646 | 815 | 831 | 0 | 68 | 448 | 22 | 36 | 10 | 62.3 | |
| 14 | 24.99 | 38.99 | 28.5 | 0 | 0 | 909 | 48 | 0 | 0 | 5 | 11 | 0 | 815 | 815 | 0 | 0 | 89 | 481 | 38 | 38 | 13 | 63.3 | 72072.62 |
| 15 | 24.99 | 40.99 | 30.25 | 1 | 1 | 956 | 48 | 0 | 0 | 12 | 6 | 11 | 815 | 815 | 0 | 0 | 68 | 261 | 21 | 35 | 12 | 64.3 | |
| 16 | 23.99 | 34.99 | 28.25 | 1 | 0 | 988 | 48 | 0 | 0 | 8 | 5 | 0 | 815 | 815 | 0 | 0 | 63 | 227 | 16 | 32 | 15 | 28.5 | |
| 17 | 30.99 | 41.99 | 36 | 1 | 0 | 983 | 48 | 0 | 0 | 9 | 6 | 0 | 815 | 815 | 0 | 0 | 52 | 186 | 9 | 31 | 12 | 29.5 | |
| 18 | 24.99 | 41.99 | 30.5 | 1 | 1 | 938 | 62 | 3 | 0 | 1 | 10 | 7 | 815 | 815 | 0 | 0 | 94 | 405 | 20 | 64 | 10 | 30.5 | 83166.36 |
| 19 | 26.99 | 41.99 | 31 | 1 | 0 | 939 | 62 | 0 | 0 | 1 | 0 | 1 | 815 | 815 | 0 | 0 | 78 | 314 | 14 | 54 | 10 | 31.5 | |
| 20 | 25.99 | 45.99 | 32 | 1 | 0 | 948 | 62 | 0 | 0 | 3 | 3 | 3 | 1455 | 815 | 640 | 0 | 87 | 338 | 12 | 66 | 9 | 32.5 | |
| 21 | 26.99 | 45.99 | 32.5 | 1 | 0 | 902 | 64 | 0 | 0 | 7 | 4 | 0 | 4965 | 815 | 640 | 3510 | 70 | 248 | 19 | 34 | 17 | 33.5 | |
| 22 | 29.99 | 45.99 | 31 | 0 | 2 | 888 | 64 | 1 | 0 | 17 | 5 | 3 | 4325 | 815 | 0 | 3510 | 86 | 287 | 27 | 45 | 14 | 34.5 | |
| 23 | 29.99 | 41.99 | 33.75 | 0 | 0 | 937 | 64 | 1 | 0 | 12 | 5 | 0 | 4325 | 815 | 0 | 3510 | 68 | 264 | 21 | 31 | 16 | 35.5 | 92470.99 |
| 24 | 29.99 | 41.99 | 31.25 | 1 | 0 | 953 | 64 | 0 | 0 | 12 | 11 | 2 | 4325 | 815 | 0 | 3510 | 84 | 405 | 15 | 51 | 18 | 36.5 | |
| 25 | 24.99 | 41.99 | 32.5 | 1 | 0 | 983 | 58 | 0 | 0 | 8 | 9 | 2 | 4325 | 815 | 0 | 3510 | 86 | 374 | 15 | 52 | 19 | 37.5 | |
| 26 | 28.99 | 40.99 | 34.75 | 1 | 0 | 988 | 58 | 2 | 0 | 9 | 6 | 4 | 4325 | 815 | 0 | 3510 | 84 | 458 | 9 | 60 | 15 | 38.5 | |
| 27 | 24.99 | 46.99 | 33 | 1 | 0 | 995 | 58 | 0 | 1 | 11 | 11 | 4 | 4325 | 815 | 0 | 3510 | 92 | 400 | 10 | 68 | 14 | 39.5 | 91174.48 |
| 28 | 29.99 | 40.99 | 31.5 | 0 | 0 | 961 | 58 | 0 | 0 | 2 | 3 | 0 | 4325 | 815 | 0 | 3510 | 81 | 459 | 11 | 53 | 17 | 40.5 | |
| 29 | 28.99 | 37.99 | 37.75 | 1 | 0 | 996 | 58 | 0 | 0 | 6 | 6 | 2 | 4325 | 815 | 0 | 3510 | 85 | 396 | 14 | 60 | 11 | 41.5 | |
| 30 | 27.99 | 37.99 | 37.5 | 1 | 0 | 945 | 58 | 0 | 0 | 1 | 8 | 2 | 4018 | 508 | 0 | 3510 | 89 | 458 | 14 | 63 | 12 | 42.5 | |
| 31 | 29.99 | 37.99 | 37.25 | 1 | 0 | 986 | 59 | 0 | 0 | 5 | 8 | 0 | 6268 | 508 | 0 | 5760 | 80 | 344 | 16 | 48 | 16 | 43.5 | |
| 32 | 26.99 | 40.99 | 31 | 1 | 1 | 953 | 59 | 0 | 0 | 5 | 5 | 0 | 4018 | 508 | 0 | 3510 | 80 | 269 | 17 | 48 | 15 | 44.5 | 182486.48 |
| 33 | 30.99 | 39.99 | 37.25 | 0 | 0 | 989 | 59 | 3 | 0 | 6 | 3 | 5 | 4018 | 508 | 0 | 3510 | 85 | 332 | 15 | 56 | 14 | 45.5 | |
| 34 | 31.99 | 46.99 | 38.25 | 0 | 0 | 1031 | 59 | 0 | 0 | 13 | 10 | 0 | 4018 | 508 | 0 | 3510 | 77 | 303 | 22 | 37 | 18 | 46.5 | |
| 35 | 30.99 | 46.99 | 31.25 | 1 | 0 | 1042 | 59 | 0 | 0 | 5 | 8 | 0 | 4853 | 508 | 835 | 3510 | 67 | 297 | 23 | 28 | 16 | 47.5 | |
| 36 | 27.99 | 38.99 | 32.25 | 1 | 0 | 1023 | 59 | 0 | 1 | 2 | 9 | 2 | 3477 | 508 | 835 | 2134 | 81 | 478 | 14 | 51 | 16 | 48.5 | 56038.77 |
| 37 | 28.99 | 40.99 | 37 | 1 | 0 | 1045 | 61 | 0 | 0 | 7 | 6 | 1 | 4485 | 508 | 1843 | 2134 | 84 | 263 | 21 | 48 | 15 | 49.5 | |
| 38 | 29.99 | 37.99 | 38.75 | 1 | 0 | 1065 | 61 | 0 | 0 | 11 | 6 | 0 | 2642 | 508 | 0 | 2134 | 78 | 367 | 20 | 44 | 14 | 50.5 | |
| 39 | 30.99 | 41.99 | 37.75 | 1 | 0 | 1037 | 61 | 0 | 0 | 15 | 3 | 0 | 2642 | 508 | 0 | 2134 | 71 | 263 | 22 | 32 | 17 | 33.2 | |
| 40 | 30.99 | 42.99 | 39.5 | 1 | 0 | 1052 | 61 | 0 | 0 | 11 | 11 | 1 | 2642 | 508 | 0 | 2134 | 77 | 222 | 14 | 43 | 20 | 34.2 | 123935.45 |
| 41 | 26.99 | 41.99 | 31 | 1 | 0 | 1055 | 61 | 0 | 0 | 12 | 9 | 3 | 508 | 508 | 0 | 0 | 89 | 279 | 9 | 63 | 17 | 35.2 | |
| 42 | 31.99 | 38.99 | 31.25 | 1 | 1 | 1071 | 61 | 3 | 0 | 12 | 2 | 0 | 508 | 508 | 0 | 0 | 71 | 343 | 16 | 38 | 17 | 36.2 | |
| 43 | 34.99 | 39.99 | 35 | 0 | 0 | 1104 | 61 | 0 | 0 | 11 | 4 | 0 | 1237 | 1237 | 0 | 0 | 61 | 294 | 16 | 27 | 18 | 37.2 | |
| 44 | 28.99 | 40.99 | 35.75 | 1 | 0 | 1145 | 61 | 0 | 0 | 7 | 8 | 0 | 1237 | 1237 | 0 | 0 | 72 | 349 | 14 | 42 | 16 | 38.2 | |
| 45 | 25.99 | 41.99 | 37.5 | 1 | 0 | 1157 | 61 | 0 | 0 | 6 | 3 | 4 | 3117 | 1237 | 0 | 1880 | 101 | 441 | 14 | 71 | 16 | 39.2 | 99591.43 |
| 46 | 34.99 | 46.99 | 31.5 | 1 | 0 | 1136 | 61 | 0 | 0 | 9 | 0 | 0 | 1237 | 1237 | 0 | 0 | 52 | 215 | 16 | 24 | 12 | 40.2 | |
| 47 | 25.99 | 37.99 | 33.25 | 1 | 2 | 1140 | 61 | 2 | 1 | 15 | 2 | 5 | 1237 | 1237 | 0 | 0 | 103 | 388 | 17 | 74 | 12 | 41.2 | |
| 48 | 28.99 | 42.99 | 39.5 | 1 | 0 | 1146 | 58 | 2 | 0 | 12 | 9 | 0 | 1237 | 1237 | 0 | 0 | 75 | 354 | 12 | 49 | 14 | 42.2 | |
| 49 | 27.99 | 45.99 | 37 | 1 | 2 | 1156 | 58 | 4 | 0 | 14 | 1 | 0 | 3852 | 1237 | 0 | 2615 | 95 | 372 | 7 | 71 | 17 | 43.2 | 70942.70 |
| 50 | 34.99 | 40.99 | 30.5 | 1 | 0 | 1166 | 53 | 0 | 0 | 18 | 11 | 2 | 1237 | 1237 | 0 | 0 | 73 | 452 | 9 | 53 | 11 | 44.2 | |
| 51 | 34.99 | 39.99 | 30 | 0 | 1 | 1175 | 53 | 0 | 0 | 21 | 2 | 7 | 1237 | 1237 | 0 | 0 | 89 | 362 | 15 | 55 | 19 | 45.2 | |
| 52 | 26.99 | 41.99 | 35.25 | 0 | 0 | 1155 | 53 | 0 | 1 | 6 | 7 | 0 | 1237 | 1237 | 0 | 0 | 82 | 353 | 14 | 51 | 17 | 46.2 |
In: Economics