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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 large-
scale 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 Item Nos. Total Items
Performance Standards 6,9*, 10,13*, 30*, 31,57 7
Communication flow
12,17,24,34,37*, 38,49*,
52,61,65,67
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Reward system 29*, 41,54,66* 4
Responsibility 4,16*, 27*, 40 4
Conflict resolution 1*, 18*, 23,42*, 44*, 45*, 46 7
Organizational structure 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 Male Female Total
Small and Medium
Enterprises
70 18 88
Large scale Enterprises 122 40 162
Multi National
Corporations
90 49 139
Total 282 107 389
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
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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
Variable
Male (n=282) Female (n=107)
t-value Signi. Mean Std.Dev. Mean Std.Dev.
Organizational climate 236.6950 33.4563 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.
Variables
Married (n=149) Unmarried (n=240)
t-value Signi. Mean Std.Dev. Mean Std.Dev.
Organizational climate 231.4027 32.13 238.2875 33.62998 -1.9964 *
* 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.
Variables
1-6 years (n=316)
7 & more than 7 years
(n=73)
t-value Signi. Mean Std.Dev. Mean Std.Dev.
Organizational climate 235.4209 32.7566 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.
Variables
1-3 years (n=345)
4 & more than 4 years
(n=44)
t-value Signi. Mean Std.Dev. Mean Std.Dev.
Organizational climate 235.7188 34.1228 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.
Variables Summary Manager
Project
Leader/Con
sultant
Programme
r/Analyst Other F-value Signi.
Organizational
climate
Means 235.74 242.41 233.32 230.91 2.1248 NS
Std.Dev. 26.60 37.43 32.98 28.60
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.
Variables Summary
M Tech/
M.E./M.S MCA
B Tech/
B.E MBA Other F-value Signi.
Organizational
climate
Means 229.39 233.16 235.97 239.56 235.25 0.4879 NS
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
Small and Medium
Enterprises Large scale Enterprises
Multi Multi National
Corporations F-value Signi. Organizational climate Means 229.1477 240.4568 232.6259 3.9818 *
Std.Dev. 33.0442 36.3739 28.1912
Dimensions of Organizational climate
Performance
Standards Means 23.8750 24.7037 24.2302 1.3479 NS
Std.Dev. 3.8561 3.8800 4.0937
Communication flow Means 34.9318 36.5741 35.5683 1.7900 NS
Std.Dev. 6.5422 7.1980 6.7449
Reward system Means 13.6591 14.3765 13.8849 1.6923 NS
Std.Dev. 3.1106 3.3363 3.0647
Responsibility Means 12.2727 12.9321 13.0719 2.7419 NS
Std.Dev. 2.1103 2.6937 2.7705
Conflict resolution Means 24.4205 24.7778 23.7050 2.2057 NS
Std.Dev. 4.0193 4.9532 4.0744
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Organizational structure Means 15.0909 15.5309 15.2302 0.5089 NS
Std.Dev. 2.7486 3.5301 4.0312
Motivational level Means 23.0795 24.5988 23.8849 2.8956 *
Std.Dev. 4.9392 5.3666 3.9965
Decision making
process Means 22.0000 23.3025 21.7122 5.4118 *
Std.Dev. 3.9392 4.7432 4.3058
Support system Means 30.1364 31.6481 31.1871 1.9977 NS
Std.Dev. 4.8712 6.3427 5.4461
Warmth Means 16.2159 17.7407 16.8058 5.0418 *
Std.Dev. 3.9667 3.8683 3.6512
Identity problems Means 13.7727 14.4259 14.5108 1.6346 NS
Std.Dev. 2.6337 3.1164 3.5618
* 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 rejected and alternative hypothesis is accepted. In another words small scale, large
scale and multi national companies have different organizational climate scores.
Small scale, large scale and multi national companies do not differ significantly with respect to
dimension of organizational climate i.e. performance standards scores (F=1.3479, >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, <0.05) at 0.05 level
of significance. Hence, the null hypothesis is rejected and alternative hypothesis is accepted. In
another words, small scale, large scale and multi national companies have different motivational
level scores.
Small scale, large scale and multi national companies differ significantly with respect to
dimension of organizational climate i.e. decision-making process scores (F=5.4118, <0.05) at
0.05 level of significance. Hence, the null hypothesis is rejected and alternative hypothesis is
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accepted. In another words, small scale, large scale and multi national companies have different
decision-making process scores.
Small scale, large scale and multi national companies do not differ significantly with respect to
dimension of organizational climate i.e. support system scores (F=1.9977, >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 support system
scores.
Small scale, large scale and multi national companies differ significantly with respect to
dimension of organizational climate i.e. warmth scores (F=5.0418, <0.05) at 0.05 level of
significance. Hence, the null hypothesis is rejected and alternative hypothesis is accepted. In
another words, small scale, large scale and multi national companies have different warmth
scores
Small scale, large scale and multi national companies do not differ significantly with respect to
dimension of organizational climate i.e. identity problems scores (F=1.6346, >0.05) at 0.05 level
of significance. Hence, the null hypothesis is accepted and alternative hypothesis is rejected. In
another words, the small scale, large scale and multi national companies have similar identity
problems scores.
Implications
Since the organizational climate is very important for the IT companies, they should strive to
create a congenial organizational climate in their organizations for retention of the talent pool
and maintenance of high productivity. A climate of teamwork is key for effective creativity.
There is a significant difference between married women and unmarried women with respect to
organizational climate. It is evident from the mean scores that the unmarried women contribute
to better organizational climate. So the IT companies have to take measures to motivate the
married women employees so that they also contribute to the organizational climate. The
managements can think of flexi timings with proper accountability to support of Married
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employees. The one way ANOVA shows significant difference among small scale, large scale
and multi national IT companies with respect to organizational climate. On comparing the mean
scores it was found that the Indian Large scale IT companies have a better organizational climate
than the Small scale and multi national IT companies. The managements of Small scale and
multi national IT companies should try to improve their organizational climate. Small scale and
multinational IT companies should take quality and rewarding decisions to improve performance
standards. By a right kind of rewarding, motivating, facilitating, and creating participative
working environment the employee morale can be raised. Smaller companies can benchmark
with the large companies, especially the process and practices that can be replicated efficiently in
small and medium firms, through quality circles and continuous improvement programmes.
The study shows a significant difference on support system for female employees, married
employees and employees with more experience, compared to male employees, unmarried
employees and employees with less experience. So the managements should initiate a strong
support system for the female employees, married employees as well as the senior employees.
The female employees have low conflict resolution compared to male employees. So the
management should provide training on the conflict resolution techniques for its female
employees. They have to be empowered to withstand challenging and competitive environment.
The employees should not be discriminated on the basis of gender and there should be methods
to deal with the sexual harassment. Leaders have to ensure perceived organizational justice and
fairness at the workplace through proper remuneration and promotion. The managements can
think of flexi timings with proper accountability to support the employees.
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Conclusion
Since the organizational climate is very important for the IT companies, they should strive to
create a congenial organizational climate in their organizations for retention of the talent pool
and maintenance of high productivity. A climate of teamwork is key for effective creativity.
One of the major problems faced by the IT industry is high rate of attrition. It has been noticed
that IT companies with good organizational climate face less threat of attrition. The significant
difference between the types of companies on organizational climate shows that the large- scale
Indian IT companies have a better mean score showing better 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.
Since satisfaction determines or influences the efficiency of the employees, we can say that
organizational climate is directly related to the effectiveness of an organization. The
organizational climate can affect the human behaviour in the organization through an impact on
their performance, satisfaction and attitudes.
A good organizational climate favors risk taking which will encourage employees to test and
exchange unusual knowledge and ideas for the prosperity of the organization. An atmosphere of
cooperation opens access among group members and creates individual motivation to exchange
knowledge with group members and teamwork. Norms for openness and teamwork in
knowledge-intensive firms facilitate disclosure of information and loyalty building. A climate of
teamwork is key to effective creativity. Creativity is hurt when an organization’s climate is
characterized by a lack of cooperation and results in lack of job satisfaction for the employees.
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