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AIMA Journal of Management & Research, May 2014, Volume 8 Issue 2/4, ISSN 0974 – 497 Copy

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MEASURING SALES FORCE PRODUCTIVITY- A

STUDY OF THE RETAIL SALES EMPLOYEES

Sweta Saurabh Assistant Professor; School of Business, Galgotias University, Greater Noida

Abstract: Measuring productivity is relevant to all organizations and especially critical for

organizations dealing in service sector. The paper studies the various factors of productivity of employees

working in retail store and examines the inclusion of relevant factors to measure the employee productivity.

The present study uses the technique of factor analysis. The result revealed the number of factors suitable

to examine the employees’ productivity.

Key words: Productivity; satisfaction; retail employees

Introduction

In India because of the increasing number of nuclear families, growing size of the

working women segment greater work pressure and increase in commuting time,

convenience has become a priority for Indian consumers. The consumers want everything

under one roof for easy access and multiplicity of choice. The retail scenario in India is

unique. The Global Retail Development Index developed by A.T. Kearney has ranked

India first, among the top 30 emerging markets in the world. There is a changing pace in

India which is reflected in the Indian consumer’s lifestyle and his habits. According to

Indian Retail Industry Report, 2011, there has been estimation from Goldman Sachs that

the Indian economic growth could actually exceed China by the year 2015. India has the

potential to deliver the fastest growth over the next 50 years.

In recent years, growing competitiveness among companies and the globalization of

markets have given rise to an economic environment in which it is becoming increasingly

difficult for companies to survive. In this context, efficiency and productivity have

become an important issue for managers, both in the manufacturing and service sector.

As opined by Van Biema and Greenwald (1997), the service sector’s size and importance

has doubly grown in recent years, productivity has not grown as fast in the service sector

as in the manufacturing sector.

Review of Literature

Bain (1982) defined productivity as the contribution towards an organizational end result

in relation to resources consumed. The end result of any retail store is to enhance

customer satisfaction and maximize productivity.

McLaughlin and Coffey (1990) stated that increasing productivity in the service sector

can be difficult to achieve due to the characteristics of services i.e. intangibility and

heterogeneity; which makes the measurement of service productivity a challenging task.

The intangible and heterogeneous nature of services makes them difficult to quantify.

Given the importance that retail activities have in the service industry, retail productivity



plays an important role in the control and management of retail organizations, providing

vital information for a number of tactical, strategic and policy related decisions in the

retail industry.

In the study, the focus is on developing a model of the construct “Productivity of retail

sales employee” from a store level perspective. Retailers have developed and used

several methods to measure the productivity of the employees and have come up with

significant results. Katzell and Yankelovich (1975) cited that productivity on technical

level involves the ratio of output (for eg., the ratio of units produced or man-hours); but

on a non-technical level it may be viewed as performance. Performance shall be

described in terms of the behaviour of workers with respect to some standard. In addition

to production or output, worker performance shall also be seen in terms of turnover,

absenteeism, or the like. Further, the work of Huseman, Hatfield, and Gatewood (1978)

viewed productivity in terms of seven components i.e. quantity of output, quality of

output, absenteeism, turnover, tardiness, satisfaction, motivation.

Productivity is the ratio of what is produced to what is required to produce. This ratio is

in the form of an average, expressing the total output of some category of goods divided

by the total input like labour or raw materials. In principle, any input can be used in the

denominator of the productivity ratio. Normally, productivity is the relation between

output and input. Clampitt and Downs (1993) analyzed productivity in two different

companies. He used one company from service organization and the other one from

manufacturing organization. The study found that productivity of the employee was

indicated by “the amount of work” an employee does. Subsequently, the second most

important indicator of productivity seemed to be “getting the job done”, followed by

“how good the employee are with customers”. The study referred productivity as

quantity; quality; getting job done; please customers; goals; timeliness; and best efforts.

The study stated that service companies concentrate more on external factors since they

have a closer contact with customers. And retail being the customer centric sector, the

productivity shall be measured based on the basis of external factors.

Gamble (2006, p. 1463) points out that while the service sector has attracted increasing

attention for HRM studies, “the retail sector has been neglected”. Although a small

number of studies of work-related outcomes can be found in the retail setting, studies on

the productivity of sales employees are rare.

Particularly the study estimates retail productivity, given the importance that retail

activities have in the service industry. Furthermore, retail productivity plays an important

role in the control and management of retail organizations, providing vital information

for a number of tactical, strategic and policy related decisions in the retail industry. Good

(1984) pointed out that the previous papers on this topic has created a large menu of

measures, models and methods for capturing and rewarding productivity, but the lack of

agreement on the measurement of productivity makes it difficult to provide with any

normative conclusions. Parsons (1997) stated that productivity is estimated in units that



vary from physical activity to monetary value, which has caused confusion and

controversy.

Donna (1996) studied the leadership behaviours and its relationship with employee

productivity, and job satisfaction of hospital managers. In the study “productivity scale”

was developed to measure the productivity of the employees. Productivity indicators

were identified from the previous literature (Bain 1982; Suttermeister 1976). The

indicators included goal attainment for unit and for the organization, supply and linen

costs, labor costs, service , professional growth, meeting productivity goals, meeting

deadlines, being well organized, accomplishing a large amount of work, accuracy,

absenteeism, prevention of turnover, and departmental problem solving. Although the

findings supported the existence of a positive relationship between managers use of the

leadership behaviors and employee productivity, but the study was limited by the use of

the self-reporting productivity measure.

Ahmed and Patricia (1995) also worked in the area of retail productivity and stated factor

affecting productivity. The study was on the use of modern technology, employee

training programmes, store size, location and financial positions. The study lacked the

measurement of employee productivity of the retail sector.

Later, Sharma and Choudhary (2011), in their study measured the operational efficiency

of retail stores in Chandigarh- Tricity. The study was conducted to measure the

relationship between the efficiency and the size of the stores. Operation research based

method-DEA was used, input variables like size of retail store, experience of manager

and location of store was optimised and the output variables included sales and customer

satisfaction. The study could not gather instrument for the measurement of employee

productivity.

Voordt (2004) in his findings on productivity and employee satisfaction talked about the

importance of flexible workplace, modern information and communication technology,

cost savings, workplace innovation and employee satisfaction. The study focused upon

employee productivity in relation to an open structure in an organization. Literature on

real estate, facility management, business administration and environmental psychology

stated the following indicators to measure productivity like actual labour productivity,

perceived productivity, amount of time spent, absenteeism due to illness and indirect

indicators; of which perceived productivity provides a reasonable indicator to measure

actual productivity, but the reliability and validity of the measurement method remained

questionable.

Further, the study by Bataineh (2011) opined that the happier people are within their job,

the more satisfied they are to be. Productivity was measured by factors like goal

attainment, worker safety, job satisfaction, physical well being, delegation of power by

management, empowering employees, decision making authority, faith in employees,

education, pay, departmental problem solving and reward and praise.



Borkar and Paul (2013) opined that employment in a physically taxing job is a major

cause (ILO 2011) of botheration among several workers in an organised retail sector.

Quality environment and proper hygienic conditions like availability of first-aid kit,

training on health and safety measures, safe drinking water, written instruction regarding

safe working procedures, etc. plays an important role in workers motivation to achieve

their personal and professional goals.

Also, previous literatures has proved that work environment can considerably affect an

individual’s ability, growth, development and motivation towards job (Laschinger et al.,

2004; Gagne and Deci 2005; Bitner 1992).

In retail stores, constant engagement with customers and continuous shelf management

often make employees stand for long hours. Also void of proper sitting arrangements

results into high level of dissatisfaction. Many a times interpersonal experiences and clear

communication between workers and with supervisors not only reduces workers

inefficiency but also helps them to do a far better job and improve commitment. ILO

(2011) states that workers who have received little or no training, or who have carried out

relatively simple and repetitive tasks for many years, will have limited knowledge and

may face difficulties when confronted with new and unfamiliar tasks and the safety

requirements associated with them. Training and development acts as a key ingredient in

performance improvement. As suggested by Williams and Arnett, enterprises that

perform consistently will tend to invest in employee training and development so as to

make workers competent and improve bottom-line results. It will also lead to retention of

talent.

Accordingly, researchers use productivity study for different purposes. Hence for this

study, retail productivity shall be defined as the “productivity of retail sales employees”

especially the store level employees. Mishra (2011) opined that the existing retail

productivity models fail to provide satisfactory fit for Indian retail sector. Therefore, the

study shall focus upon the development of a model to assess the productivity of the retail

sales employees.

The study used previous literature to identify the indicators to measure productivity

(Clampitt and Downs, 1993; Donna, 1996; Bain 1982; Suttermeister 1976). Based on the

literature the present study identified seven dimensions of productivity viz. employee

empowerment, rewards and recognition, physical well-being, please customers,

compensation, working environment and goal attainment. These dimensions are studied

in one of the leading retail store in Faridabad and the model is depicted in figure 1 below.

The paper aspires to test the applicability of the factors to measure the productivity. The

findings shall contribute to the somewhat limited studies on scale refinement especially in

India.

Figure 1: The Research Model for the Productivity Dimensions of the Sales Employees

scored. These can be found in Figure 1 below.

Productivity Dimensions

Employee Empowerment Physical Well-being Compensation

Rewards & Recognition Please Customers

Training & Development Working Environment



Hypothesis

Based on the research model in figure 1 the following hypothesis is developed.

H0 = The variables are uncorrelated in the population.

Methodology

Sample and Procedure

The sample for the selection of organization was conveniently selected from the directory

of retail stores in Faridabad, Haryana. It was made sure that the sample chosen cater to

every need of the family and that it scores over other stores with its value for money

proposition for Indian customers. The store supervisor and floor managers were contacted

and informed about the purpose of the reaserch, confidentiality issues, and the reporting

of the results.

Randomly the store level employees were selected to fill the questionnaire. The 5-10

participants per variable guideline is commonly used in factor analysis (Joreskog and

Sorbom, 1989; and Streiner, 1994). No considerations were taken in terms of gender and

age, however the study made sure that the sample size covered all the personnel placed in

store. With regard to survey of the employees, three hundred fifty questionnaires formed

the basis of the sample size. However, three hundred questionnaires were properly filled

and returned representing 85.71 per cent which was significant for the study.

Table 1 Socio-economic Profile of Employees

Frequency Percent

Age

under 20 yrs 28 9.3

21 to 30 yrs 256 85.3

31 to 40 yrs. 16 5.3

Gender

Male 204 68.0

Female 96 32.0

Educational Qualifications

High school 23 7.7

Graduate degree 263 87.7

Post-graduate degree 14 4.7

Years of service in the organization

Less than 6 months 97 32.3

6 months to 1 year 65 21.7

1 year to 3 years 85 28.3

3 years to 5 years 8 17.7

Source Primary Data



Measures

The employee productivity indicators used by Clampitt and Downs (1993); Donna

(1996); Borkar and Paul (2013); Suttermeister (1976) ; Ahmed and Patricia (1995) and

Voordt (2004) was utilized in this study. The resulting pool of 33 items was roughly

classified into worker safety, physical well-being, employee empowerment, goal

attainment, please customers, rewards and recognition, compensation, and training and

development. The questionnaire received by subjects contained 33 items. The

questionnaire was measured using a seven-point Likert scale ranging from: 1= strongly

disagree; 2= disagree; 3= disagree somewhat; 4= undecided; 5= agree somewhat;

6=agree; 7= strongly agree. The demographical details (gender, work experience,

education, age, years of service) also comprised of the questionnaire. These details were

collected using a nominal scale with pre-coded options. None of the questions were

reversed-coded.

Statistical Tool

Statistical Package for Social Sciences (SPSS) is used for statistical analysis of the

collected data. The data is analyzed by using the:

• Reliability analysis

• Principal Component Analysis

Analysis

Cronbach's alpha is the most common measure of internal consistency ("reliability"). It is

most commonly used to determine if the multiple Likert questions in a survey /

questionnaire that form a scale is reliable. The below mentioned table (Table 1) denotes

the Reliability Statistics table that provides the actual value for Cronbach's alpha.

Table 1

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based

on Standardized Items N of Items

.753 .720 24

In our study, we can see that Cronbach's alpha is 0.753, which indicates a high level of

internal consistency for our scale with the specific dataset. Cronbach’s alpha based on

standardized items is 0.720 that shows the covariance of the variables.

Factor analysis was performed on the data for identification of the factors which affect

the productivity of the employees preferred by the respondents in one of the leading retail

store in Faridabad. Factor Analysis identifies common dimensions of factors from the

observed variables that have a high correlation with the observed and seemingly

unrelated variables but no correlation among the factors.

Principle Component Analysis is the commonly used method for grouping the variables

under few unrelated factors. A factor loading is the correlation between the original

variable with the specified factor and is the key to understanding the nature of that

particular factor.



In this study, Principal Component analysis has been used since the objective is to

summarize most of the original information (Variance) in a minimum number of factors

for prediction purposes. Here the factors are extracted in such a way that factor axes are

maintained at 90 degrees, meaning that each factor is independent of all other factors. A

factor is a linear combination of original variables factors also represent the underlying

dimensions that summarize in account for the original set of observed variables. An

important concept in factor analysis is the rotation of factors. We have used Varimax

Rotation to simplify the factor structure. Only the factors having latent roots (eigen

values) greater than 1(unity) are considered. An Eigen value is the column sum of

squares for a factor. It represents the amount of variance in data. We chose those factor

loadings which were greater than 0.3 (ignoring the signs) and loaded them on the

extracted. A factor loading is the correlation between the original variables and the

factors, and is the key to understanding the nature of a particular factor. The final step in

factor analysis is naming the factors. This labeling is intuitively developed by the factor

analyst based upon the appropriateness for representing the underlying dimensions of a

particular factor.

Floyd and Widman (1995) stated that factor Analysis is one of the most commonly used

procedures in the development and evaluation of psychological measures. It is

particularly useful with multi-item inventories designed to measure personality, attitudes,

behavioral styles, and other multifaceted constructs of interest to social scientists.

Further, Hooper, also opined that factor analysis examines the inter-correlations that exist

between a large number of items (questionnaire responses), and in doing so, it reduces the

items into smaller groups, known as factors. These factors contain correlated variables

and are typically quite similar in terms of content or meaning. EFA does not discriminate

between variables on whether they are independent or dependent, but rather it is an

interdependence technique that does not specify formal hypotheses. The second reason to

employ factor analysis would be to refine the number of items on a scale for the purposes

of scale development (DeVellis, 2003).

In the present study, the purpose of the analysis was to develop a relevant scale to

measure the productivity of the employees of a retail store, by examining whether all the

dimensions of measuring productivity can be replicated.

Factor Analysis and Findings

Prior to our comparative analyses, we tested the eligibility of the data for factor analysis

by using the Kaiser-Meyer-Olkin measure of sampling adequacy (MSA) (Kaiser et al.

1974). The null hypothesis, that the variables are uncorrelated in the population, is

rejected by Bartlett’s test of sphericity (Table 2). The approximate chi-square statistic is

.002946 with 276 degrees of freedom, which is significant at the 0.05 level. The value of

the KMO statistic (0.662) is also large (>0.5).

Table 2



KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .662

Bartlett's Test of

Sphericity

Approx. Chi-Square 2.946E3

Df 276

Sig. .000

The below given table (Table 3) depicts the determination based on eigenvalues and

determination based on percentage of variance. In this approach, only factors with

eigenvalues greater than 1.0 are retained which represents the amount of variance

associated with the factor. Hence, it shall be noted that eight factors with a variance

greater than 1.0 are included.

In the percentage of variance approach, the number of factors extracted is determined so

that the cumulative percentage of variance extracted by the factors reaches a satisfactory

level of atleast 60 percent of the variance. The total variance accounted for by all the

eight factors was 69 percent which is quite high, and this establishes the validity of the

study. Naming the factors has been considered on the basis of the size of factor loading of

the variables. Greater a factor loading for a variable greater are the chances of the factor

being named after the specified variable.

Table 3

Table 4 provides the varimax rotated factor loadings against the 24 variables measuring

productivity of the sales employees of the retail store. This was obtained in 6 iterations

through SPSS (Version 16) Software Package.

Factor analysis using Varimax rotation finds eight derived factors, each having eigen

value greater than unity. In the rotated factor matrix, those variables which had factor

Total Variance Explained

Component

Rotation Sums of Squared Loadings

Total % of Variance Cumulative %

1 2.899 12.081 12.081

2 2.814 11.724 23.806

3 2.598 10.824 34.629

4 2.007 8.361 42.991

5 1.857 7.737 50.728

6 1.772 7.382 58.110

7 1.366 5.690 63.799

8 1.280 5.333 69.132

Extraction Method: Principal Component Analysis.



loading of above 0.30 (ignoring the signs) are grouped under their respective derived

factors.

The factor 1 has high coefficients for variables V23 ( freely share your feelings with co-

workers), V27 ( decent language in the organization), V21 ( freely share feelings with

supervisor), V25 ( support from co-workers ) and V33 ( receive support from supervisor).

Therefore this factor may be labeled as “working environment” factor. Factor 2 shows

high coefficients for variables V7 (clean drinking water facility), V15 (flexible work

timings), V12 (cafeteria and healthy food) and V18 (sitting arrangements) which shall be

termed as “physical well-being” factor. Similarly variables V31 (involved in making

decisions), V32 (higher management shares information), V28 (career advancement

opportunity) and V29 (ideas and opinions sought) loaded strongly on factor 3 to be

termed as “employee empowerment” factor. Factor 4 shows variables V5 (receive timely

incentives after completion of targets), V8 (efforts recognized & appreciated), V10

(supervisor values your suggestion) and V16 (receive praise from supervisor) as high

coefficients and termed as “rewards and recognition” factor. Factor 5 with higher

loadings on variables V26 (empathize with customer needs) and V22 (helping customers)

and termed as “helping customers” factor. Similarly, factors 6, 7 and 8 has subsequently

higher loadings on variables V20 (training needs), V24( training on new products), V2

(receive pay according to experience & skills), V3 (aware about organizational goal) and

V30 (accomplish predetermined goals) and shall be categorized as “training and

development” ; “compensation” and “goal attainment” factor.

Table 4

Rotated Factor Matrixa (Loading criteria >.30)

Factor

Var. No. Attributes 1 2 3 4 5 6 7 8

V23 Share your feelings with co-workers .880

V27 Use of decent spoken language in

the workplace .838

V21 Freely share your feelings with

supervisors .679 .308

V25 Receive support from co-workers at

the time of need .583 .429

V33 Receive support from supervisor at

the time of need .488 .406

V7 Clean drinking water facility .876

V15 Flexible work timings .820

V12 Cafeteria and healthy food .766 .324

V18 Sitting arrangements .761

V31 Involved in making decisions .779



V32 Higher management shares

information .729

V28 Career advancement opportunity .715

V29 Ideas and opinions sought .343 .694

V5 Receive timely incentives after

completion of targets -.767

V8 Efforts recognized & appreciated .744

V10 Supervisor values your suggestion .641 .373

V16 Receive praise from supervisor .374 .553 -.456

V26 Empathize with customer needs .892

V22 Helping customers .308 .814

V20 Organization understands training

needs .935

V24 Training on new products .903

V2 Receive pay according to experience

& skills .903

V3 Aware about organizational goal -.770

V30 Accomplish predetermined goals .320 .561

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6

iterations.

Result and Conclusion

The study has demonstrated the dimensions of employee productivity in the context of

retail store level sales employees. The assessment and further application of the scale

shall allow the retail stores to evaluate the productivity of the sales employees in terms of

intangible proximity.

The author is doing a research on the retail store employee’s productivity and this study

shall provide the insight about the measurement scale to be used for further research.

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Journal of Corporate Real Estate; Vol. 6. No.2 Pg. 133

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