AN EMPIRICAL STUDY ON THE ORGANIZATIONAL CLIMATE OF INFORMATION TECHNOLOGY INDUSTRY IN INDIA *Dr.Jain Mathew, **Prof.TomyK.K(corresponding author)***Dr. Uma Selvi****Dr Kennedy Andrew Thomas

The Information Technology Industry in India India has found an unexpected opportunity in the new revolution caused by information technology, especially in customized software development. India, with its large pool of qualified technical professionals has been recognized as an important base for software development (Gopalan, 2000; Paul, 2002). With a compounded annual growth rate of 32% between 2005 and 2009 the Indian IT software and services sector has expanded almost twice as fast as the US software sector. The sector is estimated to aggregate revenues of USD 88.1 billion in FY2011, with the IT software and services sector (excluding hardware) accounting for USD 76.1 billion of revenues. During this period, direct employment is expected to reach nearly 2.5 million, an addition of 240,000 employees, while indirect job creation is estimated at 8.3 million. As a proportion of national GDP, the sector revenues have grown from 1.2 per cent in FY1998 to an estimated 6.4 per cent in FY 2011. Its share of total Indian exports increased from less than 4 per cent in FY1998 to 26 per cent in FY2011. Export revenues are estimated to gross USD 59 billion in FY2011 accounting for a 2 million workforce. The year 2010-11 was characterized by

*Professor& Head, Department of Management Studies, Christ University, Bangalore. Email:[email protected] **Professor& Head, Department of Tourism Studies, Christ University, Bangalore. Email:[email protected] ** *Professor & Head, Department of Management Studies, Cauvery College for Women,Thiruchirappally. Email:[email protected] ****Director,Centre for Education Beyond curriculum,Christ University,Bangalore [email protected]

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a consistent demand from the US, which increased its share to 61.5 per cent. Emerging markets of Asia Pacific and Rest of the world also contributed significantly to overall growth. Within exports, IT Services segment was the fastest growing segment, growing by 22.7 per cent over FY2010, and aggregating export revenues of USD 33.5 billion, accounting for 57 per cent of total exports. Indian IT service offerings have evolved from application development and maintenance, to emerge as full service players providing testing services, infrastructure services, consulting and system integration (NASSCOM, 2011).

The world has recognized India’s competitive advantage in software services and today India is a magnet for software clients owing to the quality of its skilled software manpower (NASSCOM, 2010). India has gained a lot of interest as a source of software and has emerged as a leader in the software industry (Heeks, Nicholson and Sahey, 2000). Indian firms develop software for more than three fourth of the Fortune 500 companies and at least half of the Global 2000 corporations (NASSCOM, 2009). The most important success factor for quality software development is having talented and smart people (Brooks. 1987). Being manpower intensive industry, availability, cost, turnover and productivity of manpower are critical to the functioning of the organization. The key to success of Indian software industry is the supply of trained, low cost software professionals (Arora et aI., 1999). Software industry is driven by technology and hence tends to be skill intensive. The level of talent on software project is the strongest predictor of its results (Boehm, 1981). Personnel shortfalls are one of the most severe project risks (Boehm, 1988). Software development is largescale integrated, intellectual work (Humphrey, 1989). The skill of developing software is the skill of managing intellectual complexity (Curtis. 1981).

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The high rate of employee turnover has been one major issue that most software companies have been worried about. Employee turnover causes disruptions in project implementation and loss of skills inculcated through training and hands-on experience. Though some turnover is inevitable and even healthy at times, a high leve1 of turnover could be detrimental to a company’s business in a people-driven industry like software. Since the very survival and success of software companies depend on the availability and the effective utilization of talented people, human resource activities provide the largest source of opportunity for improving software development productivity (Boehm, 1981).

Organizational Climate Organizational climate has been defined as a “perception of the psychologically important aspects of the work environment” (Ashforth, 1985) and is recognized as a potential influence on employees’ workplace behaviour and job satisfaction (Ashforth, 1985). Climate consists of a set of characteristics that describe an organization, distinguish it from other organizations, are relatively enduring over time and influence the behaviour of people in it. The individual worker’s perception of their work environment rather than a consensus view is considered, as different individuals may perceive the same workplace in different ways (Klein, Conn, Smith, & Sorra, 2001).

Organizational climate is defined as shared perceptions or prevailing organizational norms for conducting workplace activities (Reichers & Schneider, 1990). It has been conceptualized as a cognitively based set of perceptual descriptions that define the psychological climate (Jain Mathew, 2008; James&Jones, 1974; Kozlowski&Hults, 1987), and therefore it is possible to measure individual-level perceptions of the organizational climate for updating (Kozlowski &

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Farr, 1988; Kozlowski & Hults, 1987). So the focus is on employees’ perceptions of salient features of the organizational context. Kozlowski and Farr (1988) recommended that research consider the interaction between individual characteristics and perceived situational features of the environment when determining whether technical professionals will voluntarily seek to learn new skills. Perceptions relevant to a specific climate domain such as the innovation climate have motivational implications on congruent behavioural outcomes (Schneider, 1983). According to Campbell (1970) “Organizational climate can be defined as a set of attributes specific to a particular organization that may be induced from the way that organization deals with its members and its environment. For the individual members within the organization, climate takes the form of a set of attitudes and experiences which describe the organization in terms of both static characteristics (such as degree of autonomy) and behaviour outcome and outcome-outcome contingencies.” Organizational climate is a relatively enduring quality of the internal environment that is experienced by its members, influences their behaviour and can be described in terms of the value of a particular set of characteristics of the organization. It may be possible to have as many climates as there are people in the organization when considered collectively, the actions of the individuals become more meaningful for viewing the total impact upon the climate and determining the stability of the work environment (Jain Mathew,2008). The climate should be viewed from a total system perspective. While there may be differences in climates within departments these will be integrated to a certain extent to denote overall organizational climate. Organizational climate influences to a great extent the performance of the employees because it has a major impact on motivation and job satisfaction of individual employees. Organizational climate determines the work environment in which the employee feels satisfied or dissatisfied.

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Since satisfaction determines or influences the efficiency of the employees, we can say that organizational climate is directly related to the efficiency and performance of the employees. The organizational climate can affect the human behaviour in the organization through an impact on their performance, satisfaction and attitudes.

Objective of the study 1. To investigate the influence of biographical variables such as gender, age, experience, marital status, qualification and designation on the organizational climate of Information Technology companies. 2. To study the significant difference between small scale, large scale and multi national companies with respect to organizational climate and its dimensions. 3. To discuss the implications arising out of the study for effective management of IT organizations.

Research design: The present study considers organizational climate experienced currently in a number (n=389) of 38 IT companies situated in India. The study is descriptive and cross sectional type of survey. It signifies the questions to be investigated, the process of sample selection, methods of procedure to be followed, measurements to obtain and comparison and other analyses to be made. The clear design of the study is as follows: Variables of the study: 1

Organizational climate (dependent variable)

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2

Biographical variables namely type of company, Age, Gender, Marital status, Designations, Total work experience, work experience in current organization and Educational qualifications.

Tool Used ORGANIZATIONAL CLIMATE SCALE The organizational climate scale was constructed and standardized originally by Somnath Chattopadhyay and K.G Agarwal, Later it was adapted and standardized by investigator. The adapted and standardized organizational climate scale consists of 70 items to be responded on a five-point scale. Scoring of organizational climate scale is on a five point scale from 1 to 5 for the positive response of strongly disagree scoring is 1, Disagree it is 2, Neutral is 3, Agree is 4, Strongly Agree scoring is 5 and for negative items, the scores are given in opposite direction.

The total

score of the individual was considered to statistical analysis. The total scores once taken, the totals of 70 items are divided into eleven dimensions and are presented in the following table: Table 1: Dimension wise distribution of items of organizational climate scale. Dimensions Performance Standards

Communication flow

Item Nos.

Total Items

6,9*, 10,13*, 30*, 31,57

7

12,17,24,34,37*, 38,49*,

11

52,61,65,67

Reward system

29*, 41,54,66*

4

Responsibility

4,16*, 27*, 40

4

Conflict resolution Organizational structure

1*, 18*, 23,42*, 44*, 45*, 46

7

14*, 19,21,35,47

5

Motivational level

28,32,51,56*, 59,68*, 69

7

Decision making process

2,15,25*, 36,43*, 62*, 70

7

Support system

3*,5*,7,8,20,48,53,55,56

9

Warmth

26,39,60,63,64*

5

Identity problems

11*, 22,33*, 50*

4

The asterisk mark indicates items scored 54321 and all other items are scored as 12345.

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Reliability and Validity After adaptation of the original organizational climate scale by investigator, a pilot study was carried out on a random sample of 100 employees. The reliability and validity of the scale was assessed by the split half reliability technique and the split half reliability coefficient of the organizational climate scale was found to be 0.8980 (89.80%). The internal consistency of the scale was 0.9476 (94.76%). The intra-class correlations were obtained by using the item analysis technique and intra-class correlation coefficient is ranging from 0.3541 to 0.7938. All items of the organizational climate are found to be significant except question numbers 17 and 21 and they were also included in the study. The corresponding validity of the organizational climate scale was found to be 94.76%. The details are presented in the following table:

Table 2: Reliability analysis of organizational climate scale Summary

Values

Cronbach alpha, full scale

0.9476

Standardized alpha

0.9489

Corr. 1st & 2nd half

0.7254

Split-half reliability

0.8980

Guttman split-half

0.8873

Cronbach alpha-first half

0.5247

Cronbach alpha-second half

0.6104

% Of reliability

89.8000

The data for the present study was obtained from 389 employees working in different types of Information Technology companies namely, Small and Medium Enterprises, Large scale Enterprises and Multi National Corporations in information technology industry in India. The employees’ details are represented in the following table. Table-3: Distribution of IT employees according to types of company and gender.

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Type of Company Small

and

Male

Medium 70

Female

Total

18

88

40

162

49

139

107

389

Enterprises Large scale Enterprises Multi

122

National 90

Corporations Total

282

After the data had been collected, it was processed and tabulated using Microsoft Excel - 2000 Software. POPULATION AND SAMPLE OF THE STUDY The population for the study was software companies in Bangalore having commenced operation at least since 2002 because the study focused on identifying the organizational climate of the software companies, which existed at least for three years. Using NASSCOM membership as a measure, the number of software firms in Bangalore was 455 during the base year of data collection. Software companies were generally classified as small and medium-scale, large-scale, and multinational companies. Taking in to account the number of companies as per NASSCOM data, the sample was chosen as 10% of the population. In order to get equal representation it was decided to take 15 companies each in all the three categories, viz., small and medium-scale, large-scale, and multinational companies. In many of the previous studies, the size of the firm was defined in terms of number of employees (Delery and Doty, 1996; Budhwar and Sparrow, 1997; Harel and Tsafrir, 1999; Paul, 2002). This parameter appears to be quite logical in the case of software industry because the key resource in software is human resource. It has been a challenge to decide upon the cutoff number in order to classify firms into two categories, viz., large-scale and small and medium-scale. The studies that have used number of employees to represent the size of firms have differed in the choice of cut-off points to classify firms as small and medium-scale and large-scale firms. A study has been done based on the secondary information available from NASSCOM website and publications like Dataquest, Computers 8

Today, etc. Based on the number of employees in the software companies, it has been decided to classify companies in to small and medium-scale and large-scale at cut-off point of 1000 employees. Hence companies that had 1000 or more employees were grouped into large-scale and companies that had employees less than 1000 were classified as small and medium-scale and multinational companies were taken as they are and most of them had less than 1000 employees. The sample consisted of only those companies that were started in 2002 or before and companies that were based in Bangalore. Bangalore was selected because it has been recognized as the Silicon Valley of India and has the largest number of software companies in comparison with other cities in India. Since the focus was on software companies, the companies focused on IT enabled services were not part of the sample. Since organizations had several software development centres, only one major centre was selected for the study. The sample to be collected from each company was decided to be 5% of the employees in the software company under study. Since the number of employees varies from 50 to 20000 or more across organizations, 5% of the employees of only one development centre were selected. It was decided to administer the questionnaire to only those employees who had a minimum of two-years of work experience in the company. This has been done in order to avoid new employees who had no sufficient information about the organizational climate of the company. Although probability sampling is the ideal sampling process, convenience sampling is also used in research owing to various reasons. Sackett and Larson (1998) argue that a convenience sample can be relevant for research to the extent that it possesses the essential person and setting characteristics that define membership in the intended target population. It was decided to resort to convenience sampling because it was the feasible alternative to get adequate responses given the stringent criteria for enlisting companies and individual respondents within them. Secondly approval and support of the participating companies for the study was a factor not under the control of the researcher. Moreover, cost and time constraints make probability sampling out of reach. Further the assistance of internal coordinators in company was taken to ensure that the questionnaires were distributed to software employees who fulfilled the criteria defined above for respondents.

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3.12 DATA COLLECTION A total of 1000 employees from 45 different software companies in Bangalore city were approached for data collection. An internal coordinator was identified in each company in order to facilitate the data collection based on the number of employees in each unit. Out of 420 responses collected from 40 companies, 389 responses from 38 companies were usable ones. The data was collected from 14 small and medium-scale enterprises, 13 large-scale companies and 11 multinational companies. Analysis of data The data collected have been analyzed using the Karl Pearson’s correlation coefficient, student’s unpaired t-test, one way analysis of variance (ANOVA) using SPSS 11.0 statistical software and the results obtained thereby have been interpreted.

Findings H0 1:

There is no significant difference between males and females with respect to

Organizational climate in IT industry.

Table-4: Result of t-test between males and females with regard to Organizational climate Male (n=282)

Female (n=107)

Variable

Mean

Mean

Organizational climate

236.6950 33.4563

Std.Dev.

Std.Dev.

t-value

Signi.

232.8972 32.4761

1.0078

NS

From table-4 we clearly observe that, Males and females do not differ significantly with respect to Organizational climate (t=1.0078) at 0.05 level of significance. Hence, the null hypothesis is

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accepted and alternative hypothesis is rejected. In other words, the male and female IT employees have similar organizational climate scores. H0 2: There is no significant difference between married and unmarried IT employees with respect to Organizational climate. Table-5: Result of t-test between married and unmarried IT employees with regard to Organizational climate. Married (n=149)

Unmarried (n=240)

Variables

Mean

Mean

Organizational climate

231.4027 32.13

Std.Dev.

Std.Dev.

t-value

238.2875 33.62998 -1.9964

Signi. *

* Significant at 0.05 levels From table-5 we clearly observe that, married and unmarried IT employees differ significantly with respect to Organizational climate (t=-1.9964) at 0.05 level of significance. Hence, the null hypothesis is rejected and alternative hypothesis is accepted. In other words, the married and unmarried IT employees have different organizational climate scores.

H0 3: There is no significant difference between IT employees with 1-6 years and 7 & more than 7 years of total experience with respect to Organizational climate. Table-6: Result of t-test between IT employees with 6 years and 7 & more than 7 years of total experience and Organizational climate. 7 & more than 7 years 1-6 years (n=316)

(n=73)

Variables

Mean

Mean

Organizational climate

235.4209 32.7566

Std.Dev.

Std.Dev.

t-value

Signi.

233.7123 34.9871

0.3965

NS

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From table-6 we clearly observe that, IT employees belonging to 1-6 years and 7 & more than 7 years of total experience do not differ significantly with respect to organizational climate (t=0.3965) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In other words, the IT employees with 1-6 years and 7 & more than 7 years of total experience have similar organizational climate scores. H0 4: There is no significant difference between IT employees with 1-3 years and 4 & more than 4 years of experience in current organization with respect to organizational climate. Table-7: Result of t-test between IT employees with 1-3 years and 4 & more than 4 years of experience in current organization and Organizational climate. 4 & more than 4 years 1-3 years (n=345)

(n=44)

Variables

Mean

Mean

Organizational climate

235.7188 34.1228

Std.Dev.

Std.Dev.

t-value

Signi.

230.2500 23.9147

1.0307

NS

From table-7, we clearly observe that IT employees belonging to 1-3 years and 4 & more than 4 years of experience in current organization do not differ significantly with respect to organizational climate (t=1.0307) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. The IT employees with 1-3 years and 4 & more than 4 years of experience in current organization have similar organizational climate scores.

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H0 5: There is no significant difference between designations with respect to Organizational climate. Table-8: Result of ANOVA between designations of IT employees with respect to Organizational climate. Project Leader/Con Programme Variables

Summary Manager

sultant

r/Analyst

Other

F-value

Signi.

Organizational

Means

242.41

233.32

230.91

2.1248

NS

climate

Std.Dev. 26.60

37.43

32.98

28.60

235.74

From table-8, we clearly observe that the IT employees belonging to different designations (do not differ significantly with respect to Organizational climate (F=2.1248) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In other words, the employees with different designations have similar organizational climate scores. H0 6: There is no significant difference between different education qualifications with respect to organizational climate. Table-9: Result of ANOVA between education qualifications of IT employees with respect to organizational climate. M Tech/

B Tech/ B.E

Variables

Summary

M.E./M.S MCA

MBA

Other

F-value Signi.

Organizational

Means

229.39

233.16 235.97

239.56

235.25

0.4879 NS

climate

Std.Dev. 23.28

35.11 32.63

27.73

38.87

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From table-9 we clearly observe that IT employees belonging to different education qualifications do not differ significantly with respect to organizational climate (F=0.4879) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. The IT employees with different education qualifications have similar organizational climate scores.

H0 7: There is no significant difference between small scale, large scale and multi national companies with respect to Organizational climate and its dimensions. Table 10: The table showing one-way analysis of variance (ANOVA), the variables, SD, F-value and its significance at 0.05 level between different seven professionals with respect to Organizational climate and its dimensions.

Variables

Summary

Organizational climate Means Std.Dev.

Small and Multi Multi Medium Large scale National Enterprises Enterprises Corporations F-value Signi.

229.1477

240.4568

232.6259 3.9818

33.0442

36.3739

28.1912

*

Dimensions of Organizational climate Performance Standards

Means

23.8750

24.7037

24.2302

Std.Dev.

3.8561

3.8800

4.0937

34.9318

36.5741

35.5683

Std.Dev.

6.5422

7.1980

6.7449

Means

13.6591

14.3765

13.8849

Std.Dev.

3.1106

3.3363

3.0647

Means

12.2727

12.9321

13.0719

Std.Dev.

2.1103

2.6937

2.7705

Means

24.4205

24.7778

23.7050

Std.Dev.

4.0193

4.9532

4.0744

Communication flow Means

Reward system

Responsibility

Conflict resolution

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1.3479

NS

1.7900

NS

1.6923

NS

2.7419

NS

2.2057

NS

Organizational structure Motivational level

Means

15.0909

15.5309

15.2302

Std.Dev.

2.7486

3.5301

4.0312

Means

23.0795

24.5988

23.8849

Std.Dev.

4.9392

5.3666

3.9965

Means

22.0000

23.3025

21.7122

Std.Dev.

3.9392

4.7432

4.3058

Means

30.1364

31.6481

31.1871

Std.Dev.

4.8712

6.3427

5.4461

Means

16.2159

17.7407

16.8058

Std.Dev.

3.9667

3.8683

3.6512

Means

13.7727

14.4259

14.5108

Std.Dev.

2.6337

3.1164

3.5618

0.5089

NS

2.8956

*

5.4118

*

1.9977

NS

5.0418

*

1.6346

NS

Decision making process

Support system

Warmth

Identity problems

* Significant at 0.05 levels From Table 10 it is clearly observed that: Small scale, large scale and multi national companies differ significantly with respect to Organizational climate (F=3.9818, 0.05) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In another words, small scale, large scale and multi national companies have similar performance standards scores. Small scale, large scale and multi national companies do not differ significantly with respect to dimension of organizational climate i.e. communication flow scores (F=1.7900, >0.05) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In another words, small scale, large scale and multi national companies have similar communication flow scores.

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Small scale, large scale and multi national companies do not differ significantly with respect to dimension of organizational climate i.e. reward system scores (F=1.6923, >0.05) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In another words, small scale, large scale and multi national companies have similar reward system scores. Small scale, large scale and multi national companies do not differ significantly with respect to dimension of organizational climate i.e. responsibility scores (F=2.7419, >0.05) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In another words, small scale, large scale and multi national companies have similar responsibility scores. Small scale, large scale and multi national companies do not differ significantly with respect to dimension of organizational climate i.e. conflict resolution scores (F=2.2057, >0.05) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In another words, small scale, large scale and multi national companies have similar conflict resolution scores. Small scale, large scale and multi national companies do not differ significantly with respect to dimension of organizational climate i.e. organizational structure scores (F=0.5089, >0.05) at 0.05 level of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In another words, small scale, large scale and multi national companies have similar organizational structure scores. Small scale, large scale and multi national companies differ significantly with respect to dimension of organizational climate i.e. motivational level scores (F=2.8956,