JMARVolume Five

Fall 1993

Reporting ManufacturingPerformance Measures to Workers: An

Empirical Study

Rajiv D. BankerGordon Potter

andRoger G. Schroeder

University of Minnesota i

Abstract: Much management accounting research has focused on the provi-sion of periodic, aggregated financiai information to managers for planning andcontrol. Recently, many firms have adopted just-in-time production, total quaiitymanagement, and teamwork practices for their manufacturing operations. Thesenew manufacturing practices rely on increased worker involvement in the con-trol of ail phases of manufacturing, with the expectation that such involvementwill result in the identification of opportunities for process innovations and manu-facturing performance improvements.

The central theme of this paper is that the adoption of the new manufactur-ing practices necessitates changes in performance reporting and control sys-tems. Successful implementation of these practices requires the workers to iden-tify ways to improve the manufacturing process, reduce defects and ensure thatthe manufacturing operations run efficiently. Reporting manufacturing perfor-mance information provides line personnel with the feedback that is necessaryfor learning and directs their efforts to productivity and quality improvements.Demand for shop floor performance reporting systems Is therefore likely to begreater where these new manufacturing practices are employed.

Using a sample of 362 worker responses from 40 plants, we document thatthe reporting of manufacturing performance measures to line personnel is posi-tively related to the implementation of just-in-time, teamwork, and total qualitymanagement practices. Worker morale is also found to be positively related tothese new manufacturing practices and to the reporting of performance infor-mation. As such, the resuKs provide evidence on the linkage between manufac-turing practices and performance reporting systems.

I. INTRODUCTIONFor most of this centuiy management accounting has focused prima-

rily on the provision of periodic, aggregated financial Information to man-agers for purposes of planning and control. This activity has been influ-enced considerably by external reporting requirements [Johnson and Kaplan1987]. During the past decade, however, many firms have made substan-tial adjustments to their approaches to manufacturing that necessitate sig-nificant changes ln their control systems. Milgrom and Roberts [1990], forInstance, discuss how many plants are making a number of coordinatedrevisions in their production technology, organizational practices and

Helpful comments and suggestions by an anonymous referee and seminar partici-pants at the University of Minnesota, the University of Wisconsin and the 1992 Ameri-can Accounting Association Annual Meeting ave gratefully acknowledged.

34 Journal of Management Accounting Research, Fall 1993

workforce management policies that shift the control of production to theline personnel on the factory floor. Production problems are not identifiedby managers alone, nor are solutions dictated to workers; instead workersare relied upon to come up with ways to improve process quality and pro-ductivity. Consequently, manufacturing performance information needs tobe reported to shop floor personnel. In this paper, we empirically investi-gate the link between manufacturing practice choices and the reporting ofmanufacturing performance measures to shop Door personnel.

Manufacturing practices of just-in-time production (JFT) and total qualitymanagement (TQM) have recently been adopted by many Japanese andU.S. manufacturers. Moreover, many plants now emphasize teamwork andencourage their workers to solve problems and to generate innovative ap-proaches to improve production. Studies of automobile components andother industries described In Voss [1987] emphasize the contrast in orga-nizational practices. Traditional manufacturing plants tend to be laid outby machine or process function. Line personnel, separated from their co-workers by inventory, become specialized by repeatedly processing largebatches of similar materials. Inventories are pushed through the systemwith quality inspections conducted by quality control personnel occurringat the end of production. In contrast, the new manufacturing practicesemphasize products and customers rather than mass production. Prod-ucts are grouped into cells, and workers are assigned to these cells witheach employee performing several functions. Products are pulled throughthe system on demand. Batches are small, there is little work-in-processinventory and teams are responsible for production and quality. These newworkforce management practices stress worker involvement [Zipkin 19911.Workers learn through doing which results in improved ability to recognizeproblems and to implement solutions LAokl 19861. Reporting productivityand quality information to line personnel provides them with the feedbacknecessary for learning and production improvement.

A review of the recent practitioners' literature indicates that manage-ment accountants are becoming increasingly interested in the implemen-tation of new manufacturing performance measurement systems.' Someaccounting practitioners are beginning to recognize that they must expandtheir horizons and become fully cognizant of the changes that are occur-ring in manufacturing if they wish to retain their position as the primarysource of performance reports in organizations (Howell and Soucy 1987;Simon 1990]. The Increasing attention to the need for the Involvement ofcontrollers in the reporting of operations-based performance measures rep-

'Thls echoes the call by Kaplan [1983, 1984), and Is reflected also in Lammert and Ehrsam119871 and McNair and MosconI [1987]. Kaplan [1984. p. 414] states:

Management accountants may feel that their own area of comparative advantageis to measure, eoUect, aggregate, and communicate financial information. Thiswill remain a valuable mission. But it is not likely to be a goal that will be decisiveto the success of their own organization, and if senior managers place too muchemphasis on managing by the financial numbers, the organization's long-termviability may become threatened.

Eccles [19911 presents evidence suggesting that if accountants do not address the need forchanges in reporting systems, the responsibility may be given to other staff functions.

Banker, Potter, and Schroeder 35

resents a return to the origin of management accounting systems, ratherthan being a new task for the accountant (Johnson and Kaplan 1987].

Little is known about the factors influencing a firm's choice of a manu-facturing performance reporting system [Karmarkar et al. 1987]. In thispaper, we argue that this choice is tnfiuenced principally by the firm s manu-facturing practices. Implementation of TQM, JIT, and teamwork practicesputs the control of production ln the hands of the workers, and thereforeincreases the value of reporting manufacturing performance informationto line personnel.^ We test this hypothesis using questiormaire responsesfrom 362 workers in 40 plants located ln the United States.

The paper is organized as follows. In the next section we describe thenew manufacturing practices and their Implications for the role of manu-facturing performance information. This Is followed by a discussion of theresearch methods In Section III and empirical results ln Section IV. Weconclude in Section V with a discussion of the results and some sugges-tions for future research.

U. BACKGROUND AND HYPOTHESES

New Manufacturing Practices

In the past two decades many basic industries in the United Stateshave faced increasing competition from foreign manufacturers. In response,many U.S. plants have begun to adopt new manufacturing practices toIncrease their global competitiveness. Three manufacturing practices thatare Increasingly being adopted by U.S. plants are total quality manage-ment (TQM), just-in-tlme manufacturing (JIT) and teamwork.

Total quality management, TQM, promotes involvement of the entireorganization in continuously improving quality. Aspects of TQM includesimplicity of product design, interaction with suppliers, and continuousupkeep of production equipment [Johnson and Kaplan 1987]. More im-portantly, the responsibility for detecting nonconforming items shifts froma quality control department to line personnel. Young et al. [1988] r«fer tothis as process-based quality control, an approach in which quality is builtinto a product by workers as it moves through the factory. TQM makeseach worker responsible for quality control and for stopping productionwhen there is a manufacturing problem [Monden 19891. Workers are en-couraged to identify ways to improve product and process quality. Theirobservations on the shop floor also help fine-tune new product and pro-cess designs [Cole 19831.

Closely related to the emphasis on quality is JIT production practice.Johnson and Kaplan [1987]. Maskell [1989] and Schonberger [1986] state

^Fosterand Homgren [1987, p. 25] state:The general trends in cost control activities at both the shop level and the plant levelthat we have observed in JIT plants are: a declining role for financial measures, andan increasing role for personal observation and nonflnancial measures.One reason for this trend is that production workers play a pivotal role in cost controlactivities. Workers directly observe nonflnanclai variables on the shop floor, wherethey are intuitive and easy to comprehend.

36 Journal of Management Accounting Research, Fall 1993

that manufacturing actions consistent with JIT Include running produc-tion on a demand-pull basis, placing production control with workers, andstreamlining the production process. Young et al. (1988] assert that a keyaspect of the Just-in-time/pull system is the tight relationship betweenproduction goals and current production. Implementation of JIT produc-tion is also associated with a reduction in the number of labor grades andthe employment of multifunction workers.^ As the holding of inventorybuffers declines, greater coordination Is required [March and Simon 1958].Responsibility is therefore placed with workers and work teams to controlthe process, resulting In a greater demand for workers to learn multipleJob skills [Monden 1989; Schonberger 1986; Foster and Horngren 1987].

The Association for Manufacturing Excellence has published severalreports on another significant change in managing manufacturing opera-tions. Many U.S. plants now encourage workers to work In teams to tackleproblems on the shop fioor. Workers are encouraged to pool their knowl-edge of the production process and come up with innovative approaches toimprove productivity and quality and to reduce production lead time. Re-ports by Burghard [19901, Puckett and Pacheco [1990], Rhea [19871 andRosen [1989] suggest that plants encouraging small group problem solv-ing on the shop floor have substantially Improved quality and productivity,and significantly reduced defect rates and cycle times.

Just-in-time, quality and teamwork are closely related improvement ef-forts that are sometimes classified together as world class manufacturingpractices [Schonber ger 1986; Milgrom and Roberts 1990]. A high level of qualityIs a prerequisite for JIT improvement effori s, otherwise production will bestopped constantly due to defective parts [Monden 1989]. Young et al. [1988]find that the potential performance benefits of just-ln-tlme are only realizedwhen a quality program is in place. On the other hand. JTT serves to improvequality as Inventory levels are reduced and problems, which were previouslyhidden by inventory, are uncovered. Teamwork is essential in implementingboth JIT and quality practices, as has been documented by Schonbei:ger [19861.Imal [1986] provides numerous examples of production teams contributingto quality Improvements at Japanese plants.

The common theme of these new manufacturing approaches is the at-tempt to fully utilize the talents of workers on the shop floor by puttingproduction under their control. Workers are encouraged to solve problemsand improvise. In a contingency theoretic framework these approaches rep-resent a shift from the traditional "mechanistic" organization to an evolv-ing "organic" organization [Lawrence and Lorsch 19671. Workers are nolonger assigned to highly programmed tasks with supervision from above,rather they are encouraged to be more flexible and interactive. Respect forthe worker is also cultivated under these types of production practicesIMonden 19891.* Zipkin (1991, p. 46] writes;

^Foster and Horngren [1987] report that one manufacturer reduced the number of laborclassifications from 26 to 5 over a three year period beginning with the implementation ofaJIT system.^Reliance on shop floor workers to control production and process improvement is in starkcontrast to the more traditional bureaucratic or hierarchical systems that have been soprevalent ln U.S. manufacturing firms in the twentieth century [e.g., Covaleski and Aiken19861.

Banker, Potter, and Schroeder 37

One of the central arguments for JIT has been that it boosts workers'morale while enlisting their efforts In the productivity Improvement pro-cess. JIT has long been Identified as part of the movement towards Em-ployee involvement."

Hypotheses

We have argued that JIT. TQM and teamwork practices shift produc-tion control to personnel on the shop floor. Worker control is expected toIncrease worker morale and motivate them to contribute to process im-provements and innovations. One ofthe primary perceived benefits ofthenew manufacturing practices Is the gain in efficiency that occurs from theuse of on-the-spot knowledge and problem solving abilities of workers [Aoki19861. In order for workers to identify problems and opportunities, andcoordinate their efforts, management needs to provide them feedback in-formation in the form of manufacturing performance measures.^

In Juran's Qualitij Control Handbook, Baker [1988. p. 10.28-291 statesthat shop floor workers in the new production systems must be providedthe means for self-control: workers must have the ability to regulate theirwork (self-control), they must know what they are doing (feedback for learn-ing), and they must know what they are supposed to do (goal directinginformation). Performance feedback to workers is necessary to enable them"to determine the relationship between their own behavior and the out-comes the process is producing." Self-control with feedback of results andknowledge of goals makes it possible for workers to discover means to im-prove the production process. 'The freedom afforded individuals causestheir creativity, intelligence, and skill to be challenged."

The necessity of feedback for learning is one of the most dependableand most studied findings in the cognitive sciences (e.g.. Manis 1968).Tversky and Kahneman [19861 assert that effective learning takes placeonly under certain conditions, and requires accurate and immediate feed-back about the relation between the situational conditions and the appro-priate response.^ In addition, records help bolster memory, which unaidedmay be subject to a number of selectivity biases (Hogarth 19801.

The value of shop floor information is also consistent with organiza-tional behavior research that has shown that feedback helps promote taskoriented behavior [Ashford and Cummlngs 1984; Ilgen et al. 1979]. Ap-pealing to prior goal theory research studies. Wexley and Yukl [19841 rec-ommend that employees should have specific performance goals to guide

literature on total quality management IDemlng 1986: Ebrahlmpour and Lee 1988;Garvin 1983. 1986; Sehonberger 1986; Tagucbi and Clausing 1990; Takeuchi and Quelch19831 also suggests that firms with successful quality programs provide specific Informationto workers on process output. Garvin [1983] states: "Successful monitoringof quality assumesthat the necessary data are available, which Is not always true. Without speclfie and timelyInformation on defects and field failures, improvements In quality are seldom possible."Schonberger [1986. p. 32] states: There Is substantial benefit from (shop noorinrormatlon)...Informationofvalue to the company does not stay in people's heads. It eomesout and is made available so others ean leam fmm It."^Tversky and Kahneman assert that a number of economic and psychological studiesdemonstrate that learning cannot result from monetary incentives alone. Elnhom and Hogarth(19781 discuss many of the barriers to etTecttve teaming. Goldberg [19681 lists three conditionsfor learning: I) feedback. 2) the ability to rearrange cases (variation), and 3) the ability totally the accuraty of one's hypotheses.

38 Journal of Management Accounting ResearcK Fall 1993

behavior. Johnson and Kaplan [1987]. Peters and Waterman [1982. p.267]and Utzlg [1988] also suggest that Information can be used to promotedesired employee behavior. Govindarajan and Gupta [1985] state that whenperceived rewards are attached to specific performance measures, behav-ior is guided by the desire to optimize those performance measures. There-fore, shop floor performance information may be used to promote specificworker behavior [Daniel and Reitsperger 19911. Since the new manufac-turing practices rely on the workers for process improvements, their ef-forts may be guided in this direction by providing them quality and pro-ductivity information.

We hypothesize therefore that the perceived value, and hence the pro-vision of manufacturing performance Information to line personnel, is posi-tively related to just-in-time, quality, and teamwork practices.HJ: The availability of information on productivity and quality is positively

related to the extent of implementation of just-in-time, quality, andteamwork programs.The above hypothesis addresses Kaplan's suggestion that firms should

provide information on productivity and quality. It is silent, however, onthe form and content of such information. Quality and productivity infor-mation may include customer satisfaction surveys, field failure rates, valueadded per employee and other plant-wide efficiency measures. On the otherhand, charts displaying defect rates, schedule compliance and machinebreakdowns represent information that can be identified easily with spe-cific production cells or work stations.

Analytic tools for process control, such as process flow charts. Paretoanalysis plots, fishbone charts, histograms, run diagrams, control chartsand scatter diagrams also require the posting of specific performance in-formation on the shop floor [Ishikawa 1972; Schonberger 1986. Chapter7]. As workers acquire greater responsibility in the new manufacturingenvironment to control their production schedules and to inspect their ownoutput, posting of such charts becomes important for providing them withthe necessary feedback information. Therefore, we also test a detailed hy-pothesis concerning the provision of visual chart information about spe-cific shop floor operations.

Hgi The posting of charts about defects, schedule compliance and machinebreakdown on the shop floor is positively related to the extent of Imple-mentation of just-in-time, quality and teamwork programs.Finally, we note that these new manufacturing practices rely critically

on worker involvement. Klein [1989] and Zipkin [1991] note an increase inworker stress and decline in morale in some plants. Womack et al. [1990].however, find worker morale to be higher in auto assembly plants thathave adopted the new practices. Clearly a drop In worker morale wouldargue against increases in economic efficiencies stemming from workerinvolvement, and forestall any potential value from reporting manufactur-ing performance information to workers. Therefore, we also test:

Hg: Worker morale is positively related to the extent of Implementation ofjust-In-tlme. quality, and teamwork programs.

Banker, Potter, and Schroeder 39

m . RESEARCH METHOD

Sample Plants

The data used in this research consist of questionnaire responses from362 workers from 40 manufacturing plants situated in the United States.^To construct this data base Initially 60 plants were randomly selected fromlists of manufacturing plants stratified to equally represent the transpor-tation equipment, electronics and machinery industries. The plant manag-ers were contacted by letter and telephone to solicit participation in thestudy. For the 42 plants that agreed to participate, questionnaires wereadministered to ten randomly selected workers at each plant. Two plantsretumed fewer than five worker surveys and are excluded from the analy-sis. The actual number of worker surveys retumed from the 40 plantsincluded in this study ranged from 6 to 10 per plant. The questionnaireswere collected directly to ensure confidentiality of worker responses. Sum-mary information on costs, employment, square footage and capacity utili-zation for the 40 plants is presented in Table 1.

Twelve of the plants were visited by the research team in the initialphase of this study. These site visits included a tour of the facility andinterviews with the plant manager, quality manager, plant accountant, aproduction manager, a process engineer, supervisors and workers. The ques-tionnaire instruments were also pretested during these site visits. In manyof the plants that were visited, charts and graphs were displayed on theshop floor. For example. In one plant each department had bulletin boardswhich showed the latest production statistics. These Included several dif-ferent statistical process control and quality charts reporting defect ratesfor the parts produced by that work center. Pareto charts, cause-and-ef-fect diagrams and run charts for certain critical parts [e.g., Deming 1986;Ishakawa 1972]. Charts displaying daily schedule compliance (planned vs.actual schedule), charts on machine breakdowns and maintenance actlvl-

^Slnce our research focus is on manufacturing performance Information provided to workers.our tests are based on worker responses. While workers were the principal informants,secondary information was collected from supervisors and managerial staff to validate workerresponses.

Table 1Descriptive Information About Sample Plants

(n=40)Quartiles

VariableTotal Plant EmploymentTotal Manufacturing Costs

(millions)Investment in Plant and

Equipment (millions)Total Plant Area (thousand

square feet)

Mean6 3 8

$74.8

$21.4

316

StandardDeviation

779

$81.1

$19.6

530

2 5 %

182

$24.0

$5.5

75

6 0 %

4 1 9

$41.i

$14.6

166

7 5 %

7 1 5

$104.6

$29.8

3 0 0

40 Journal of Managerrvent Accounting Research. Fall 1993

ties, and charts pertaining to weekly productivity measures were also evi-dent. In one plant, quality control and production Information was dis-played in realtime on a computer screen. It was clear that a great deal ofvisual Information was being provided to the workers on the shop floor

The plant visits also revealed that the extent of implementation of thenew manufacturing practices varied within the plant. Most plants beganthe implementation for specific production cells and product groups, andgradually extended the practices to the rest of the plant. There were alsodifferences between different production departments within plants thatwere not organized into cells. For example, in one plant we found the work-ers In the fabrication department using elaborate defects and productioncharts, while the workers In the final assembly department were Just be-ginning to display these charts. The workers in the fabrication departmenthad completed their training courses in statistical process control meth-ods, while workers in other departments were yet to take these same courses.Because of such variations in the implementation of the new manufactur-ing practices within plants, we conduct our analysis at the level of indi-vidual worker responses.

Independent VariablesThe independent vartables in our model are the following four scale

measures constructed to represent JIT, TQM. and teamwork practices, andthe extent of decentralization:1) Jrr = A scale measuring use of JIT manufacturing practices.2) TQM = A scale measuring Implementation oftotal quality program.3) TEAMWK = A scale measurtng use of small teams for problem solving

on the shop floor.4) DECENT = A scale measurtng decentralization of authority.The questions Included In each scale are presented In Table 2. The actualquestionnaire included these questions in a randomly assigned order. Eachquestion within a scale Is measured on a five point Likert scale, whereeach worker was required to respond on the range "strongly agree = 5" to"strongly disagree = 1." Scales are constructed by equally weighting thequestions.

The first scale measures key aspects of JIT implementation from aworkers perspective: compliance to a dally production schedule and learningmultiple skills. Young et al. [1988] state that a just-in-time practice main-tains a tight relationship between current production and production sched-ule goals. Schonberger 119861 asserts that employee involvement In pro-duction and setups requires them to learn multiple skills. The quality scale(TQM) measures key aspects of a quality program: quality incentives, workerinspection of output, and stoppage of production for quality problems. Younget al. [19881 argue that key aspects of a quality program require that allwork stop when there is a production problem and that workers are re-sponsible for identifying errors. The teamwork scale (TEIAMWK) measuresthe extent to which workers organize into small teams to solve problemsencountered on the shop floor. The decentralization scale (DECENT) mea-sures the extent to which workers can make decisions without consulting

Banker, Potter, and Schroeder 41

Table 2Scale Measures* for Manuiacturintf Practices

1) J IT ScaleI) Our schedule Is designed to allow time for catching up. due to production

stoppages for quality problems.li) Direct labor undergoes training to perform multiple tasks in the produc-

tion process.111) Plant employees are rewarded for learning new skills.iv) We usually meet the production schedule each day.

2) TQM ScaleI) Workers are rewarded for quality improvement.II) If I improve quality, management wiU reward me.iii) Production is stopped Immediately for quality problems.Iv) I inspect my own output ."

3) TEAMWK Scalei) During problem solving sessions, we make an effort to get all team mem-

bers ' opinions and ideas before making a decision.ii) Our plant forms teams to solve problems.Iii) In the past three years, many problems have been solved through small

group sessions.

4) DECENT Scalei) I can do almost anything I want without consulting my boss.ii) Even small matters have to be referred to someone higher up for a final

answer (Reverse scale).iii) This plant is a good place for a person who likes to make his own deci-

sions.tv) Any decision I make has to have my boss's approval (Reverse scale).v) There can be little action taken here until a supervisor approves a decision

(Reverse scale).

'Each response is measured on a scale from "strongly agree = 5" to "strongly dis-agree = 1.""Based on factor analysis this question Is omitted from the scale.

their supervisors. This scale was developed and validated by Alken andHage 119661 to measure hierarchy of authority. Authorizing workers to makeproduction decisions is an important aspect of these new manufacturingpractices.

We examined the four scales for reliability (consistency) and validity.Reliability addresses the extent to which measures or responses are de-pendable, or free of error [Nunnally 1967: Kerlinger 19731. Scale reliabilityis evaluated by examining Cronbach's alpha. This coefficient is based onthe correlations among the responses comprising a scale. The alpha coeffi-cients for the manufacturing practice scales are presented in Panel A ofTable 3. They range from a low of 63.5 percent for JIT to a high of 77.2percent for TQM. consequently the scales are reliable.^

^Nunnally 11967) states that alpha coefricients of 50 percent to 60 pereent are sufilclent fornew scales. He aiso argues that increasing reilabiilty beyond 80 percent is often wasteful forbasic research.

42 Journal of Management Accounting Research, Fall 1993

Table 3RelUbUity and Validity Tests

of Manufacturing Practice Scales

Panel A. Reliability and Construct Validity SUtisUcs

Cronbach's Alpha

Proportion of VarianceLoading on largest Factor

No. of Factors withEigenvalue > 1

JIT

.635

.476

1

TQM

.772

.632

1

TEAMWK

.701

.611

1

DECENT.739

.479

1

Panel B: Consistency Statistics

Variation in Worker ResponsesExplained by Plant Effect

Spearman (and Pearson)Correlation Between AverageWorker and Supervisor/Manager Responses

Pearson correlations are In parentheses.

JIT

.275**

.603**

.550)**

TQM

.486**

.743**(.763)**

TEAMWK

.338**

. 7 0 9 "(.697)**

DECENT

.296**

.478**(.616)**

Panel C: Pearson Partial Correlations (After Industry Control) for Criterion-Related Validity

JIT TQM TEAMWK DECENT

Cycle Time -.336* -.183 -.223

Supplier Certification .418** .356* .157

Rework -.281 -.351* .131

-.125

.472**

.008

Cycle time

SupplierCertification

Rework

Logarithm of number of days from receipt of raw materials untilcustomer receipt of product.

Percent of suppliers that are certified.

Percent of products requiring rework at final Inspection.

Panel D: Spearman (and Pearson) Correlation with Responses of Supervisors.Plant Managers, and Production Personnel

JIT Production

Kanban r*uU System

Authorization for QualityProblems

Worker Control

JIT

-.380**(.388)**.251(.319)*.276*(.240).240

(.159)

TQM

.322*(.307)*.243(.249).368'*(.353)*.199

(.002)

TEAMWK

.549**(.449)**.171

(.137).405**

(.431)**.504**(.346)*

DECENT.088(.160).005(.068).214

(.322)*.343*

(.418)*

Banker, Potter, and Schroeder 43

Table 3 (Continued)

JIT TQM TEAMWK DECENT

Employee Involvement .454** .411** .660** .289*(.409)** (.361)* (.610)** (.431)**

Pearson correlations are in parentheses.*Indlcates significant at 5% level (one-tail).•*Indicates significant at 1% level (one-tail).

JIT Production = Response of supervisors, process engineer and plant produc-tion manager to question "all major department heads withinthe plant work towards encouraging just-in-tlme production."

Kanban Pull = Response of supervisors, process engineer and plant produc-System tion manager to the question "we use a Kanban pull system for

production control."

Authorization = Response of supervisors, process engineer and plant produc-for Quality tion manager to the question "direct labor is authorized to stopProblems production for quality problems."

Worker Control = Response of plant production manager to the statement "pro-duction control is ln the hands of workers."

Employee = Response of supervisors, process engineer and plant produc-Involvement tion manager to the question "our top management strongly

encourage employee involvement ln the production process."

Each question is measured on a five-point Likert scale where "strongly agree = 5"and "strongly disagree = 1."

While the responses of workers at the same plant may differ due tovarying involvement in key aspects of the manufacturing practices imple-mented in different parts of the plant, there is likely to be some consis-tency between the responses from the same plant. The percentage of varia-tion in worker responses explained by the plant effect ranged from 27.5percent for JIT to 48.6 percent for TQM, the plant effect was highly signifi-cant (p < .001) for all four scales. We also compared the average workerresponse at each plant with the average of responses of two/three supervi-sors from the same plant. The results presented in Panel B of Table 3indicate that all the correlations are highly significant (p < .001). rangingbetween .478 for DECEMT and .743 for TQM.

We evaluated the practice scales for content validity, construct validity,and criterion-related validity [Kerlinger 1973]. Content validity is strivedfor by constructing questions that represent key aspects of the new manu-facturing practices. We have discussed earlier why the scales representessential attributes of the new manufacturing practices from a worker'sperspective. Construct validity is supported by factor analysis. Panel A ofTable 3 reports that each scale loads on one factor with the percentage ofthe variation explained by that factor ranging from 47.6 percent for JIT to63.2 percent forTQM.^

Criterion-related validity is evaluated by examining Pearson partial cor-relations between the manufacturing practice scales and three other vari-

*The TQM scale question #4, "I inspect my own output," is dropped based on factor analysis.From this point forward, all measures of the TQM scale exclude question #4.

44 Joumal of Management Accounting ResearcK Fall 1993

ables that are expected to be highly correlated with the practices. Partialcorrelations are used to examine the correlations after controlling for in-dustry representation. The criterion variables are plant-wide measures ofcycle time (ln days), percent of units requiring rework and percent of sup-pliers certified. Cycle time is chosen because a key benefit of JIT Is thereduction of inventories. Reduction of rework requirements is a primarygoal of quality improvement programs and supplier certification is expectedto improve Input quality and facilitate JIT production. Because these threevariables are measured at the plant level, the responses of workers from aparticular plant are averaged for this analysis. These correlations are pre-sented in Panel C of Table 3. With the exception of the insignificant posi-tive partial correlation between DECENT and rework, and TEAMWK andrework, all correlations are in the expected direction and five are signifi-cant at the 5 percent level. We also compare our new manufacturing prac-tice variables with the responses of plant management to workforce re-lated questions in Panel D of Table 3. The practice scales are strongly re-lated to managers" perceptions of JIT production, authorization for qualityproblems, worker control and employee involvement. This suggests thatour scale measures based on worker responses are capturing importantattributes of the new manufacturing practices in place at plants.

Dependent VariablesMeasures of availability of information and level of morale were also

solicited from workers in the questionnaire surveys. All of these questionsare presented in Table 4. Similar to the independent variables, each ques-tion was measured on a five point Likert scale, where responses rangedfrom "strongly agree = 5" to "strongly disagree = 1." The first two informa-tion availability questions correspond to recommendations by Kaplan (1983]that information on quality and productivity should be compiled and re-ported to employees. The last three represent specific types of charts thatare posted on the shop floor for feedback purposes. Each worker also re-sponded to six questions that are compiled into a scale representing workermorale. This scale is constructed from a subset of Mowday and Steer's(19791 commitment scale. The scale appears to have reliability and con-struct validity as the alpha coefficient is 83.9 percent and the largest fac-tor explains 56.7 percent ofthe variation.

The percentage of variation in worker responses that is explained bythe plant effect ranged from 22.4 percent for machine breakdown charts to34.5 percent for defects charts, all significant at the .001 level. Averageworker responses are consistent with average supervisor/manager re-sponses from the same plant. All correlations are highly significant, andrange from .338 for machine breakdown charts to .729 for defects charts(see Table 5).

Estimation ModelThe following system of equations portrays the hypothesized relationships

between the dependent variables and the new manufacturing practices:4

+Gjw, (1)

Banker, Potter, and Schroeder 45

Table 4Description of Dependent Variables*

Quality and Productivity lT\formation Avaitaldlity1. Quality = Information on quality performance Is readily available to em-

ployees.2. Productivity = Information on productivity is readily available to employees.

Posting €if Charts on the Shop Floor3. Defects = Charts showing defects are posted on the shop floor.4. Schedule = Charts showing schedule compliance are posted on the shop

floor.5. Machine = Charts plotting the frequency of machine breakdowns are

posted on the shop floor.

Worker Morale6. Worker Morale Scale:"

i) I am willing to put in a great deal of effort beyond that normally expected.11) I talk up this organization to my friends as a great organization to work for.lil) I would accept almost any type of job assignment in order to keep working

for this organization.Iv) I am proud to tell others I am part of this organization.v) This organization really Inspires the best ln me ln the way of Job perfor-

mance.vl) I am really glad i chose this organization to work for over others.

'E^ch variable is measured on a five point Likert scale ranging from "strongty agree= 5" to "strongly disagree = i.""Cronbach alpha is .839 and percent of variation explained by the largest factor is.567.

where:Y = quality information, productivity information, defects charts, sched-

ule compliance charts, machine breakdown charts, and worker mo-rale, j = 1. ... 6:

X = JIT. TQM. TEAMWK. DECENT, i = 1. ... 4;w = worker w. w = 1, ... 362.

Two issues had to be resolved in order to estimate the relation betweenthe manufacturing practices and the dependent variables. One concern isthe potential correlation among the error terms of workers located in thesame plant. Following Johnston [19841. we specify this relationship as anerrors components model. Specifically, each worker's response error. €^,is assumed to comprise two components: [L , a plant-wide fbced effect term,and 6j^. an independently and identically distributed worker specific ran-dom error term, so that €j^ = Mjp + V ^°^ worker w in plant p. Conse-quently, dummy variables representing each plant are introduced into themodel to account for the plant effect term:"'

format also results in predictor variables that are the same across the six equations.Since the explanatory variables across equations are identical, there Is no eflidency gainfromjolntlyestimatingasystem of related equations |Thell 19701. Therefore, no adjustmentln OLS estimates Is necessary for the potential correlation among the six responses of theindividual worker.

46 Journal ojManagetnent Accounting ResearcK Fall 1993

8

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39

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where Dp^ = 1 If worker w is ln plant p. and = 0 otherwise. Industry differ-ences can be examined by comparing the average plant effect terms byIndustry.

A second issue Is that the worker responses to the survey questionsare ordered categorical variables [Maddala 1983]. While the worker moralescale is the average of six responses and approaches a continuous variable(takes on 25 possible values), the worker responses to the dependent vari-ables concerning shop floor Information can take on only five discrete val-ues. Following Maddala [1983. pp. 46-491 and Cox and Snell [1989. pp.158-161] we also estimate the parameters using a maximum likelihoodnonlinear ordered polychotomous loglt estimation routine.*' The left handside of the estimation model in (2) now represents the proportional oddsfor each ordinal level [McCullagh 19801. These estimates are consistentand asymptotically, normal and efficient. We construct likelihood ratio testsfor overall model fit. and employ a measure of goodness-of-fit. R = 1 - [like-lihood ratiop/", suggested by Maddala [1983. p. 39]. ^ i^e results of thisestimation are presented ln Table 9 for comparison with OLS estimatesreported in Table 8.

IV. EMPIRICAL RESULTS .

Descriptive Statistics

Summary Information on the explanatory variables is presented in Table6. Panel A indicates that the means of the scale predictor variables rangefrom a low of 2.91 for TQM and DECENT to a high of 3.42 for TEAMWK.The mean JIT response is close to that of TQM at 2.97. The quartiles andstandard deviation information indicates that all four scales have a similardegree of variation. Spearman and Pearson correlations are reported inPanel B of the table. The correlations document significant positive asso-ciations among the variables representing the new manufacturing prac-tices of JIT. TQM and TEAMWK. This is expected, given that Just-in-time.quality and teamwork programs are complementaiy practices that are some-times grouped together as world class manufacturing practices. The sig-nificant positive association with the decentralization variable is also con-sistent with the view that the new manufacturing practices give line per-sonnel more control over the day-to-day operations.

Summary data on the dependent variables are presented in Table 7.The Panel A univarlate statistics reveal that average worker responses forthe availability of quality and productivity Information are greater than theaverage responses for the posting of charts for defects, schedule compli-ance and machine breakdowns. Having a chart on defects or schedule com-pUance on the shop floor is likely to be a subset of the Information on

"The parameters are estimated using the PROC LOGIST proeedure In SAS.' ^he measure may understate the goodness-of-fit as its upper bound Is strictly less than one

[Maddala 1983].

48 Journal of Management Accounting Research Fall 1993

Descriptive Information on theManufacttirlnjE Practice Scales

(n=362)

Panel A. Summary Statistics

Variablej r rTQMTEAMWKDECENT

Panel B: Correlation Matrix

JITTQM

DECENT

Mean

2.972.913.422.91

JIT1.000.509*.422*.141*

StandardDeviation

.70

.75

.81

.75

TgM

.511*1.000.375*.123*

26%

2.502.333.002.40

60%3.003.003.673.00

TEAMWK.415*.342*

i.OOO.266*

75%

3.503.674.003.40

DECENT.150*.096.240*

1.000

Spearman correlations are above the diagonal. Pearson correlations are below thediagonal.**Indicates significant at 5% level.

quality or productivity that Is available to the worker. The availability ofmachine breakdown charts is much lower than the availability of defectand schedule compliance charts, suggesting that reporting information onbreakdowns may depend on the machine maintenance policy of the plant.Panel B of Table 7 presents pairwise Spearman and Pearson correlationsbetween the dependent variables. The significant correlations suggest thatall of the information variables are driven by similar economic factors.Worker morale is also positively related to each of the workers" informationresponses.

Hypotheses TestsThe results of the regressions relating quality and productivity Infor-

mation, posting of charts and worker morale to the new manufacturingpractices are presented in Tables 8 and 9. The estimation results with thepolychotomous ordered loglt model are very similar to the OLS results,and therefore we only discuss the OLS results here. All of the regressionsIn Table 8 are significant. The amount of variation ln the worker responsesthat can be explained by the predictor variables ranges from 32.8 percentto 52.3 percent.

The most striking result across all the regressions is the consistency ofthe sign on each predictor variable's coefficient. ^ The parameter estimates

'^Influenttal observation analysis was conducted using the RSTUDENT. COVRATIO. h, andDFFITS statistics of Belsley etal. [19801. OLS regressions were re-estlmated after droppingobservations that exceeded two or more of the thresholds identified by Belsley et al. TTie

(Contlned on page 52)

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52 Journal of Management Accounting ResearcK Fall 1993

for the predictor variables do not reverse signs across equations. The sta-tistical significance of the individual coefficients does vary by type ofnonfinancial information.^'* The JIT, TQM and TEAMWK scale-measuresexhibit strong positive association with the likelihood that infonnation onquality and productivity is provided to workers. DECENT is not signifi-cantly related to the provision of this information. All of the predictor vari-ables are significantly related to the posting of schedule compliance charts,while only JIT and TQM are significantly related to the provision of ma-chine breakdown charts. The regression result for defects charts indicatesthat provision of such information is significantly related to JIT and DE-CENT. Information on defects might be expected to be more closely relatedto TQM than to the other manufacturing practices. Recall, however, thatthe pairwise correlation between JIT and TQM is 51 percent. On re-estl-mating the defects regression in Table 8 after excluding the JIT variable,the coefficients on TQM and TEAMWK rise to .227 (p=.O22) and .122(p=.O94), respectively. Finally, a F-test on the plant coefficients Indicatesthat in each regression there is also a significant plant effect. A compari-son of average plant effects, however, does not reveal a significant differ-ence between industries.

Table 8 also presents evidence on the strong positive relationship be-tween worker morale and the new manufacturing practices. In fact, over50 percent of the variation in morale is explained by the regression vari-ables. JIT and TEAMWK are particularly strong explanators. It contradictsthe concerns expressed by Zipkin [19911 and others that workers experi-ence lower morale with the introduction of the new manufacturing prac-tices. This is reassuring as these practices rely on workers to Identify im-provement opportunities when they are given more control over the day-to-day production.

V. CONCLUDING REMARKS

This paper examines the association betweenjust-in-time. quality, andteamwork practices and the provision of manufacturing performance in-formation to line personnel. Because these new organizational and workforcemanagement practices put the control of production in the hands of linepersonnel, we posit a positive relationship between the extent of imple-mentation of these practices and the provision of information to workerson the shop fioor. We present evidence on this relationship using a sampleof 362 worker responses from 40 plants located in the United States.

Footnote 13 (Continued)number of observations dropped ranged between 12 and 17 for the six regressions. TTie re-estlmatlon Increases the R for all the regressions. The R now range between 43 percentand 58 percent. All the earlier significant coefilcients remain significant. In addition, twomore coefncients become significant at the 5 percent level: the JIT coefficient In the qualityregression and the TEAMWK coefficient in the machine breakdown regression.

'^The predictor variables are positively correlated and the evidence presented here may bebest interpreted In overall terms as Indicating a strong positive relation betweenimplementation of world class manufacturing practices and provision of manufacturingperformance information to workers. The Belsley-Kuh-Welsch diagnostics, however, indicatethat mulUcollinearity is not a serious problem. The highest condition index Is only 25.5,and in only one case (associated with condition index value 17.2) do the variance componentsfor two of the independent variables (JIT .59, TQM .67) exceed .4.

Banker, Potter, and Schroeder 53

Two important findings emerge from this research. First, we documentthat the provision of information to shop fioor workers is positively relatedto the implementation of Just-In-tlme. quality, and teamwork practices. Assuch, our results provide evidence of a link between manufacturing prac-tices and control systems that emphasize the role of workers. This is im-portant given that little is known about factors that influence firms' choicesof performance reporting systems. Second, we establish that employeemorale is positively related to the existence of just-in-time, quality, andteamwork production ^stems, and the provision of shop floor performanceInformation. This finding is useful given that there have been contrary opin-ions expressed concerning the impact of these new practices on workerstress and morale (Klein 1989; Zipkin 1991].

This study also raises a number of questions for future research. Oneinteresting avenue is to determine if firms that provide performance infor-mation to workers on the shop floor experience more productivity gainsfrom the implementation of these new manufacturing practices than firmsthat implement these practices but do not provide shop floor performanceinformation. Another direction for future research is to examine the at-tributes of manufacturing performance reporting systems that promoteworker learning. While it is well known that feedback is a necessary condi-tion for learning, the impact of different types and forms of feedback, andthe timing of feedback (on learning), need to be examined in this environ-ment. Both Hayes and Clark [1985] and Teece and Winter 119841 believeresearch on learning is critical for advances in modem management. Webelieve research on the link between manufacturtng performance report-ing and learning is important for modem management accounting. Futureresearch studies may also include detailed documentation of reporting sys-tems that have been adapted to the new manufacturtng practices, and ex-amine transition problems encountered in changing from the old to thenew systems. An important research question is whether the changes inthe shop floor performance reporting systems result from the workers' owninitiative in the new manufactuing environment, or whether the changesare dictated by plant management and corporate accountants. Empiricalresearch isjust beginning in this important area of management account-ing.

54 Journal of Management Accounting ResearcK Fall 1993

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