SuGyaan 1 - ssim.ac.in · towards Online Grocery Shopping in India”, by Krishan Kumar Boora & Kiran examined the planned behavior of consumers towards online grocery shopping in

Volume: IX, Issue - I, Jan - June, 2017

CONTENTS

Title Page #

ARTICLES

An Empirical Study of Measuring Consumer Behavior towards Online Grocery Shopping in India 3 - Krishan Kumar Boora & Kiran

A Study on Impact of Organizational Commitment on job satisfaction with Organizational Citizenship Behavior as a Mediating Factor among Engineers in South India Home Appliances Manufacturing Firms 16 - Dr. S. Lara Priyadharshini & Dr. S. Suganya

Examining the Investment Profile of Household Investors: A Study of Punjab 32 - Dr. Tina Vohra

The Impact of Online Service Quality on Tourists’ Satisfaction: An Empirical Study 47 - Dr. Nitasha Sharma

Copyright: Siva Sivani Institute of Management, Secunderabad, India. SuGyaan is a bi-annual publication of the Siva Sivani Institute of Management, NH-44, Kompally, Secunderabad - 500 100.

All efforts are made to ensure correctness of the published information. However, Siva Sivani Institute of management is not responsible for any errors caused due to oversight or otherwise. The views expressed in this publication are purely personal judgments of the authors and do not reflect the views of Siva Sivani Institute of Management. All efforts are made to ensure that published information is free from copyright violations. However, authors are personally responsible for any copyright violation.

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EDITORIAL – JAN-JUNE -2017SuGyaan 2

It gives me immense pleasure in presenting before you the issue of SUGYAAN- Volume-IX, Issue-I, Jan–June, 2017, Management Journal of Siva Sivani Institute of Management. In its ninth year of existence Sugyaan has received a tremendous response from its readers and contributors. Our sincere gratitude to the readers, authors and reviewers for their continuous support and encouragement. In our continuous effort to contribute to the cause of nation building by promoting quality research through thought provoking ideas in the form of research papers, articles, case studies, and book reviews in the journal. Current Issue of Sugyaan have included four research papers from the discipline of marketing and finance. The first research paper titled “An Empirical Study of Measuring Consumer Behavior towards Online Grocery Shopping in India”, by Krishan Kumar Boora & Kiran examined the planned behavior of consumers towards online grocery shopping in select regions of Delhi-NCR and Haryana. The main objective of the study is to examine the factors that measure consumer behavior towards online grocery shopping. The study proposed to test the hypotheses, does attitude, subjective norms and perceived control behavior have a significant impact on the purchase intentions of consumers in online grocery shopping? The study concluded that attitude and perceived control behavior have a significant influence on the purchase intentions of consumers for online grocery shopping. The second research paper titled “A Study on Impact of Organizational Commitment

on job satisfaction with Organizational Citizenship Behavior as a Mediating Factor among Engineers in South India Home Appliances Manufacturing Firms”, by Priyadarshini & Suganya, proposed a model to evaluate the mediating role of OCB on organizational commitment and job satisfaction among production engineers. Research results indicated that affective commitment and normative commitment are positively related to OCB. OCB partially mediating the relationship between OC and Job Satisfaction. The third research paper titled, “Examining the Investment Profile of Household Investors: A Study of Punjab”, by Tina Vohra investigated the impact of demographic characteristics of investors on their investment profile viz., type of investment, sources of investment information and duration of investment. Results of the study indicated that demographic variables like age, gender and education qualification and marital status, occupation, monthly income have a significant impact on the investment decisions of the investors. Fourth article titled, “The Impact of Online Service Quality on Tourists’ Satisfaction: An Empirical Study”, by Nitasha Sharma examined factors affecting the tourist’s satisfaction in online tourism by using E-SERVQUAL scale. The results of the study indicated that factors like website design, security, empathy, ease of use have a significant affect on the tourist satisfaction in availing the services from online tourism platforms. We hope you find this issue interesting and look forward for your feedback.


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AN EMPIRICAL STUDY OF MEASURING CONSUMER BEHAVIOR TOWARDS

ONLINE GROCERY SHOPPING IN INDIA

* Krishan Kumar Boora ** Kiran

ABSTRACT Online shopping has witnessed a big change in the life of consumers. Every one know about online functioning and operation for make their life easy. Online shopping is more popular rather than physical store shopping because it is home based shopping that makes life easy and comfortable. Online grocery shopping is a new concept for Indian consumers however, only a few studies has been conducting on consumer response towards online purchase of grocery products in India. The objective of this study is to find out consumer’s intention towards online shopping and the study based on the model theory of planned behavior originated by Ajzen 1991. Descriptive research design has been used and the area of study is NCR & Haryana state (India). Primary and secondary data has been used for finding results and total number of respondents are 300, selected on the basis of convenience sampling technique. The population sample used for this study includes those consumers who have prior experience of buying products online. The data has been analysed in SPSS software by use of correlation and regression analysis. This study has examined factors like attitude, subjective norms, perceived behavior control and intention to purchase online. This concept is new and need some change for motivating consumers and provide better service to them.

Key words: Online grocery shopping, consumer behavior, perishable goods

JEL Classification Code: M3, M30, M31

IntroductionPast studies have revealed that the use of internet for collecting information about consumer market and consumer behaviour has increased manifold (Peterson et.al. 1997). Internet is the medium of communication that measures the impact of advertising in the new media (Klein, 1998) and now it plays important role in the consumer market and consumer behaviour to keep connection between people and information, better internet connection requirement of people continually rise on service providers and on online retailers which are selling product. Online shopping consumers make quick and easy comparison between different types of

* Assistant professor BPSMV Khanpur Kalan, Sonipat, Department of Management Studies, [email protected], Mobile No. 8053215801 ** Research scholar BPSMV Khanpur Kalan, Sonipat, Department of Management Studies, [email protected], Mobile No. 9813044777


SuGyaan 4products. Online grocery market is not new for Indian consumers it covers 60% share of total Indian market (ken research 2015). Grocery products are daily consumable item easily available in supermarkets these are necessity of life goods purchased by consumer in large quantities. Hence, if these are easily available for the consumer online so they can free from carrying bags, parking problem and standing in a line of local market and supermarket. In India online grocery shopping from 2011 and now some major online leading retailing firms offer their service in all over India like Bigbasket, Zopnow, Aaramshop, local baniya etc. these firms cover high maket share and attract more investor. Their three year business captured high market share in India market. Online grocery shopping is less popular in sub urban area because Grocery products are easily available in traditional stores, where consumer can touch; check their freshness before purchasing (Klein, 1998). Traditional and supermarkets are not limited with perishable goods but also provide durable, eatable products like canned foods and non food goods (Schuster & Sporn, 1998). Online grocery retailing is new for sub urban customers so currently they are not willing to purchase perishable product online, so it is required to explore the consumer’s intention towards online grocery shopping. This is based on click and picks concept means consumers click order by internet and pick products at home. India is a country of festival so online grocery shopping is more helpful in festival season and party or marriage time.

Indian consumers are more aware towards quality products now they choose fresh, healthy and balanced nutrients products, which create strong immunities system against disease and promote good health. The professional message deliver by online grocery retailers for consumers is to promote healthy life with delivery of fresh food. They include two new concepts health and wellness in promotion of sale. The major reason for use of online grocery shopping is convenience and use of broadband by consumers in their daily life. In 2017 the big entry of Amazon and Flipkart in online grocery market with existing players Bigbasket, Grofers and Jugnoo (Thimmaya & Kulkarni, 2017) the estimated share of online F&G around $600 million in India and it is expected to grow $5 billion by 2020 at a CAGR 72%. Now the big online grocery players are Amazon, Grofers and Bigbasket and many other closing down their operation like PepperTap.

Review of LiteratureThere are research studies on online grocery shopping. Many of them focused on consumer’s behavior towards online shopping and the factors that create strong intention towards online shopping. Some studies are based on TBP model which is introduced by Ajzen in 1991. As earlier explained that product categories are important for consumers for use of online shopping. (Grewal et.al., 2004) Have found Groceries products have lower acceptance by the consumers because it is new for consumer. E-retailers have to use some promotional strategies. (Burke, 1998; Peterson, Balasubramaniam, & Bronnenberg,


SuGyaan 51997) have suggested that strategies of online retailers for selection of distributors are more effective for internet marketing, because grocery products are perishable goods. These strategies are easy & quick home delivery, high discount on sale, creating customers loyalty by offerring fresh & Quality products. (Schuster & Spoen, 1998) Have told that online grocery retailers are offering click and picks approach to get order and timely delivery of goods at home. Online grocery shopping is more convenient rather than digital product shopping that easily transported and selling throughout the world so retailers are needed to protect grocery products. Edible grocery products are categorized by ease of storage and demand of consumers and perishable goods are not stored item these are frozen goods and stored in refrigerator, they need good transportation sources for quick delivery. So the study is based on TBP model to know about consumer behavior and decision making style towards online grocery shopping so the retailers can sell their grocery products online. Theory of planned behavior developed by Ajzen (1991) in order to understand the factors that influence consumer to use internet services. TBP is extension of theory of reasoned action (TRA) that measure the direct influence of the consumer’s intention on the actual behavior and measured consumer attitude and subjective norm that influence consumer behavior. (Muhammad et al. 2016) have suggested some promotional strategies for online retailers to increase sale of grocery products, like improvement in e-services, creating positive situation for buying, factors of e-service quality and situational factors. These factors positively influence consumers behavior towards online shopping. This study was based on comparison of two theory for measuring consumer behavior that is theory of reason action and theory of planned behavior. ( Hansen, Jensen & Solgaard, 2004) have argued that theory of reasoned action (TRA) defined pre-existing attitude and intention of consumers and theory of planned behavior (TPB) make a relationship between belief and behavior of consumers. Consumers are ready to purchase product in different situation and react according to their need and requirement. Some consumers purchase the product when they feel requirement or need and in second case first they make planning about channel of purchasing then take action.

Attitude is the major determinant of consumer behavior towards online shopping according to Reasoned Action Theory and Theory of Planned Behavior, Ajzen and Fishbein, 1980;


Ajzen, 1985, 1991), (Hansen, 2008) has defined the attitude of consumer is situational factor that influence consumers negative and positive towards a new concept. Previous study found attitude as a strong determinant of consumer behavior intention to shop online. Consumers are also influenced by personal value of attitude. These values are strong determinants of attitude but make moderate effect on consumer’s intention. (Batra & Ahtola, 1990; Spangenberg, Voss, & Crowely, 1997; Voss, Spangeberg, & Grohmann, 2003) have found that attitude can be divided into two separate parts: hedonistic and utilitarians dimensions connected with stimuli. It is helpful to define the nature of consumers. both are connected positive or negative behavior of consumers. But in this study the author found negative hedonistic approach. (shim et.al, 2000) have searched out that internet user search information about product and create intention to purchase product online with attractive offers. Basically product information create positive attitude towards online and repurchase intention. (Kevin et al., 2008, Bloach & Bruch, 1984; Babin, Dardenm 7 griffin, 1994) have concluded that consumer having both approach but first think about product utility that create positive utilitarian and apposite of that is negative utilitarian. (Fishbein & Ajzen 1975) have told that if consumers believe that all related attributes are favorable so they can easily develop positive attitude towards products and services. (Akroush & Al-Debei 2015) have advocated that now it is the time of internet and many people preferred online shopping. (Argyriou, Melewar, 2011) have explained that attitude and subjective norms influence behavioral intention. But subjective norms are strong factors for building consumers intention. Subjective norms are another factor of TBP for measuring consumer belief towards OGS. Attitude of consumers towards products and services can be divided into two parts it can be temporary and stable. It is a cognitive factor that affects consumer behavior according to their level of understanding.

(Ajzen 1991) has defined that subjective norms mean people opinion that relates him like friends, family members that perform certain behavior. (Husin & Rahman, 2013) have argued that OGS decision making style of consumer largely depend on the factors that force to create a positive or a negative behavior, these are subjective norms, attitude perceived value. Some studies are based on subjective norms focused on different group (Husin &Rahman, 2013) have pointed that family is a strong influencer. (Lu, 2012) has told that friends are creating internet awareness and (Park, 2013; Jhou, 2011) have explained positive social pressure which creates high awareness towards new technology. (David, Tong, Yin, 2012; Orapin, 2009; Zhou, 2011; David, Tong, Yin, 2012; Orapin, 2009; Zhou, 2011) have discussed on the group of consumers who highly preferred online shopping. This study is based on youth, some respondent are students and others are professional. (Ajzen, 1991) has found that there is no significant relationship between subjective norms and actual behavior of consumer, but it is used by some researchers as a mediating factor of purchase intention before performing actual behavior. (Yi,Jim,Lim et al. 2016) have conducted a study that

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focus on Y- Gen and use subjective norms as a mediating factor of purchase behavior and found significant relationship between subjective norms and consumer intention.

Perceived Control behavior (PBC) is too old concept in marketing research and use for understanding consumer need and expectation towards a particular brand, and it is another important factor of TPB model. (Roberts, 2000; Payne et al., 2001; Khalifa, 2004; Lindgreen and Wynstra, 2005; Hensen, 2008) have explained that Consumer’s values also influence their buying decision and change their attitude towards product and services. In these studies attitude of consumer defined by personal value. (moliner et al., 2007) have found that perceived behavior control is most effective factor for influencing consumer behavior at starting stage and first step in relationship marketing. (Ha & Janda, 2008) have found that perceived value is consumer perception of what they get and what they had to sacrifice to receive a certain services. It is new concept in twenty first century to create value for the consumers (Chen & Hu, 2010). In the context of Malaysia PBC create strong intentions that show consumer’s satisfaction and loyalty towards their action (Lee et al., 2011). PBC made mediating effect on consumer satisfaction and loyalty (Chang & Wang, 2011). According to (Norouzi et al., 2013) the study explore the understanding of major factors influencing consumers service perceived by undertaking adaptive Neuro-Fuzzy Inference System (ANFIS) method in fuzzy inference system. Intention of person forced by subjective norms sometime decision of consumer influence by parental or peer pressure. They give shape to their behavior directly or indirectly (Cui, Greetman & Hooimeijer, 2016).

Objectives of Study1. To examine the factors influencing consumer behaviour towards online grocery shopping

2. To find out the most preferred product category by consumer through OGS (online grocery shopping).

Hypothesis TestingH1: There is a significant relationship between attitude and purchase intention for online grocery shopping.

H2: There is a significant relationship between subjective norms and purchase intention for online grocery shopping.

H3: There is a significant relationship between perceived control behavior and purchase intention for online grocery shopping

Research MethologyThe present study is descriptive in nature. The reason for using this approach is to study various factors that influence consumer decision making style towards online grocery

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shopping. A survey is conducted with the help of structured questionnaire for collection of information about demographic profile of respondents and various questions related to the experience of internet usage, purpose of online grocery shopping, type of grocery products purchased online etc. All respondents are online shoppers. The survey is conducting during the time period August to October 2016. Due to lack of time data are collected from NCR & Haryana Convenience sampling methods is used for getting response from population and number of respondents are 300. The 280 respondents are selected for finding result and 20 respondents are not purchase anything from online. Present questionnaire based on five point likert scale here, 1=strongly disagree and 5= strongly agree.

Results and DiscussionData analysis

The data collected based on the tools (Questionnaires) developed, edited and analyzed with the help of statistical software (SPSS, MS EXCEL). The data entry and tabulation plan is prepared according to the nature of the study. Initially, descriptive statistics is applied to draw inferences from the collected data. In order to prove or disprove the framed hypothesis for the research correlation analyses and regression analyses are used to find out the inter factors relationship. Subsequently, the results are presented with the help of appropriate tables, diagrams and graphs.

Table 1.1 given below indicates the respondents demographical profile according to the response given by female consumers that are 57% give more participation in online grocery shopping rather than men. The consumers who are engaged in full time and part time work situation are highest user of online shopping 31.8% & 33.6%. People who are having four family members spend more money on grocery shopping 46.8%. Presently in Haryana only 20% consumer purchasing online grocery products and 80% want to purchase their grocery by online channel in future.

Table1. Demographic Profile of consumers

Demographic factors Category Percentage

Gender Male 42.5

Female 57.5

Age (year) 20-25 15.7

26-30 29.3

31-35 34.6

>30 20.4

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Education Matriculation 9.6

Intermediate 9.3

Bachelors 37.8

Masters or above 49.3

Work Situation Full time 31.8

Part time 33.4

Students 13.6

Unemployment 12.9

Retired 8.2

Family Members Two 5.4

Three 27.9

Four 46.8

More than five 20.0

Source: survey by author

The most preferred product categories purchased by consumers through online grocery shopping are fruits, vegetables 73.9%, others (Pulses & Grains, Branded foods, Beverages, Personal care and Household) 68.9%, milk products 46.8% and meat products are less preferred by consumers. The result is based on average response given by consumers who are purchasing grocery through online or want to purchase in future. The respondent who are not purchasing any grocery product online are not part of the study.

Table 1.2 shows reliability of all the variables by computing overall Cronbach’s Alpha using SPSS 21. The value of reliability test come out to be .844, all 45 items are under study. The lowest limit suggest by the Hair, Anderson and Black for cronbach’s alpha to be .70, if all the variables are under this limit can be reliable for study. All the variable of our research study having good reliability value. The analyses represents none of variable (attitude, subjective norms, intension, and perceived behavior control) has less than Cronbach’s alpha .70 and all variable are reliable.

Table 2. Reliability Test Result (n=45)

Factors Cronbach’s AlphaOverall value .832

Intension .896Attitude .876

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Subjective Norms .963Perceived value behavior .950

Source: survey by author

The summary represents the mean and standard deviation of each factor (intension, attitude, subjective norms and perceived behavior control)

Table 3. Descriptive Statistics

Description of variables Mean Std. DeviationIntension to purchase online grocery productsI 1. I have plan to purchase products with internet

3.55 1.27

I 2. I have plan to purchase groceries products by online channel 3.58 1.35I 3. I have certain plans to purchase my groceries online 3.44 1.42I 4. Me and my family feel a strong commitment including online grocery shopping into our weekly routine in near future

3.46 1.36

I 5. I have plan to change shift traditional grocery shopping to online grocery shopping

3.43 1.40

I 6. I have plan to include technological advancement into my daily routine Attitude towards online grocery shopping

3.53 1.41

A 1. 1 prefer online grocery shopping rather than physical store shopping 3.56 1.27A 2. Use of online grocery shopping, we moved towards advance life style 3.46 1.16A 3. Use of online grocery shopping in my family routine would be perfect 3.62 1.26A 4. Use of groceries shopping by internet is comfortable to me and my family’s daily life

3.37 1.17

A 5. Online Purchasing of groceries product is helpful to satisfy our unplanned need.

3.31 1.44

A 6. I prefer technological advancement in my life. Subjective Norms to influence online shopping behavior

3.54 1.33

SN 1. My relatives think that we should use of online grocery shopping to our everyday lives

3.48 1.41

SN 2. My friends motivate me for me use of online grocery Shopping in my life. 3.38 1.43SN 3. My near and dear people give opinions to me for prefer online grocery shopping for my family.

3.47 1.39

SN 4. Most families that are important to my family have suggest to us for online grocery shopping in our everyday lives

3.32 1.45

SN 5. My family would feel positive if routines in my everyday life would be changed

3.16 1.39

SN 6. My friends think that adaptation of technology change helpful to live comfortable life. Perceived Behavioral value of Consumers

3.21 1.37

PBV 1. Overall service quality of online grocery products would be meet my expectation

3.36 1.35

PBV 2. If I purchase some online grocery product that would be fulfill my expectation

3.03 1.47

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PBV 3. The price of product charge by online retailers would be reasonable 3.37 1.45PBV 4. Home delivery service that offer by online retailers would be helpful in my busy schedule

2.76 1.17

PBV 5. The products offer by online retailers is a good value for money 3.32 1.37PBV 6. If I need to buy grocery products in the future I would prefer online grocery shopping

3.35 1.14

Valid N (listwise) 280Source: survey by authors

Correlation AnalysisTable 1.4 shows the inter factors correlation and the value of correlation coefficients of the paired factors not exceeding the value 0.9. This shows the satisfactory validity and scale should have sufficient validity.

Table 4. Bi-variate Correlation Analysis

Variables Attitude Subjective Norms PBCAttitude 1.000

Subjective Norms .681** 1.000PBC .577** .586** 1.000

Intention .696** .582** .738**** Correlation is significant at the level of 0.01 level (Two tailed)

Multiple Regression Analysis Table 1.5 given below shows multiple Regression analysis used to analyze the relationship between a single dependent and several independent variables. After analyzing the results it has been found that β coefficient value of three independent variables which is helpful for consumer to take decision regarding online grocery shopping are:

Table 5. Multi-Linear Regression Analyses

Independent variables Unstd. β coefficient Std. β coefficient t Sig. Attitude .483 .395 7.806 .000Subjective Norms .027 .021 .417 .677PBC .644 .498 10.903 .000RAdjusted R SquareF

.809

.651174.80 (p=0.000)

Dependent variable: intention

Attitude The level of significance β=.395 and p=0.00 which is < 0.05 means attitude plays an important role in influence consumers Intention towards online grocery shopping. Hence,

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H1 hypothesis is accepted.

Subjective Norms β=.021 and p=.677 which is > 0.05 that does not show any significant level correlation between subjective norms and consumers intention towards online shopping. Hence, null hypothesis H0 is accepted.

Perceived control behaviorThe perceived control behavior and consumers intention towards online grocery shopping shows a significant correlation with β=.498 and p=0.005 which is < 0.05 which shows that PBC factor strongly correlated with consumers intention. Hence, H3 hypothesis is accepted.

The result of the study explains with conceptual framework of research.

figure 1.6

Managerial implicationThe finding of the study will help the marketing managers to understand the decision making style of consumers towards online grocery shopping. It can help the marketers for making appropriate targeted marketing strategy for potential buyers. It can also provide all information about consumers as to how they make their buying decision. So the marketers would be able to divide whole markets into two segments, viz., consumer’s need and products preference.

ConclusionAfter analysis of the study, it has been concluded that consumers are aware about new technology and adopt it in their daily life. Online shopping is very popular and online grocery shopping is new concept but still consumers have high intention to use it. This study is examining consumer behaviour by conceptual framework factors are re-introduced with new time, place and product. After analysis some factors are strongly correlated and some are negative or no correlation. From the present study it can be concluded that consumer attitude and perceived behavior control is considered as the most significant factor to create

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positive consumer intention towards online grocery shopping. Subjective Norms create less or no effect on intention of consumers because at present only 20% people purchase product online and 80% having intention to purchase online grocery so, they have less experience and cannot suggest for others. The finding of the study is based on testing the conceptual framework represents that attitude and PBC factors are strongly correlated with intention of consumers towards online grocery shopping.

Scope for Further ResearchThe study is based on TBP model to find out the consumers decision making style towards online grocery shopping, It was limited to one geographical area that is NCR Haryana. Due to lack of time and money we had used convenience sampling techniques that may be failed to represent whole population. The questionnaire was self designed hence there could be possibility of deformation. The study was based on limited factors like Intention, Attitude, Subjective Norms and Perceived Behavior value. So for the future research, researcher can use different location with more sample size and also introduce some other factors that indicate pre-purchase and post purchase behavior of consumers towards online grocery shopping.

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MJ SSIM IX(I),1,2017

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A STUDY ON IMPACT OF ORGANIZATIONAL COMMITMENT ON JOB SATISFACTION WITH

ORGANIZATIONAL CITIZENSHIP BEHAVIOR AS A MEDIATING FACTOR AMONG ENGINEERS IN SOUTH INDIA HOME APPLIANCES MANUFACTURING FIRMS

* Dr. S. Lara Priyadharshini ** Dr. S. Suganya

ABSTRACTThe aim of this paper is to analyze the impact of organizational commitment on job satisfaction with organizational citizenship behavior as the mediating factor for production engineers in South India Home & Appliances manufacturing firms. The analysis and discussion suggest the need for new outline of research to decide whether the existing practices approved out in human resources departments are well directed. The samples were 398 valid questionnaires received. The researcher uses the structural equation model (SEM) to empirically explore the relationships. The results show that affective commitment positively affects the organizational citizenship behavior and normative commitment also positively affects the organizational citizenship behavior. The path coefficient from Organizational Citizenship Behavior to job satisfaction significantly shows a positive relationship. Also the organizational citizenship behavior partially mediates the relationship between organizational commitment (affective, continuance and normative) and job satisfaction. These research findings have also examined the moderating effect of organizational commitment (affective, continuance and normative) and job satisfaction. It is an analytical research which shows the impact and mediating effect of variables. Realistic allegations depend on the development of future research, as justified at all times.

KEYWORDS Organizational Commitment, Organizational Citizenship Behavior, job satisfaction.

JEL Classification Code: L2, L22

IntroductionOrganizational commitment and job satisfaction have been two of the repeating builds in the logical literature about work association. Generally, they have been related with the

* Associate professor, KV Institute of Management and Information Studies, Coimbatore. Email: [email protected] Mobile : 9791843556** Professor, KV Institute of Management and Information Studies, Coimbatore. Email:[email protected] Mobile: 9786694551

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coveted and undesired conduct of the individuals who communicate inside an authoritative framework. For a considerable length of time, hypothetical models have been developing (e.g. Meyer, Stanley, Herscovitch & Topolnytsky, 2002) as they were being empirically tested. However, ahead of the direct approaches, we want to relieve the darkest side of this relationship. Organizational Citizenship Behavior (OCB) is a sort of conduct that can’t be depicted formally part of the set of working responsibilities of the worker; however this kind of representative conduct assumes vital part in association viability and execution (Lin, Chen, & Chen, 2016).

Therefore, this study aims to investigate the relationship between organizational commitment and job satisfaction, with the organizational citizenship behavior as mediator. The rest of this study is organized as follows. In Section “Related works”, the researcher present related works on the theoretical model development. Section “Methodology” describes the methodology, including the research framework, data collection, and measurement. Section “Results” presents the research results. Finally, Section “Implications” presents research summary and implications.

Related Works and Hypotheses DevelopmentRelationship between organizational commitment and organizational citizenship behavior

Affective Commitment

Affective commitment is the most esteemed conduct. It is showed by an enthusiastic connection that advances the representative authoritative citizenship, in advantage of the organization (Wasti, 2003). The essential commitment is viewed as the most unwanted in which the main motivation to have a place with a specific association is that monetary conditions offered are better when contrasted and whatever remains of the accessible choices (Clugston et al., 2000).

Continuance Commitment

Mirabizadeh and Gheitasi (2012) in Iran at a study entitled “Examining the organizational citizenship behavior as the outcome of organizational commitment: Case study of universities in Islam” reasoned that instructive opportunities, work-life arrangement, and strengthening activities had solid positive association with commitment; and hierarchical responsibility additionally affected OCB in like manner (Mirabizadeh and Gheitasi 2012).

Normative Commitment

Meyer and Allen (1991) demonstrate that emotions more often than not persuade people to carry on fittingly in the association and influence it to right. In this way, it is normal that ethical responsibility be emphatically identified with superior conduct, magnificent participation and the showing of authoritative citizenship conduct. Workers who display

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this ethical responsibility are steadfast. They feel an abnormal state of character between their own esteems and the qualities held by that association. They understand that they contribute essentially to a decent aim, to do well from their employments.

H1. Organizational commitment practices affect the Organizational Citizenship Behavior

H1a. Affective commitment positively affects the Organizational Citizenship Behavior

H1b. Continuance commitment positively affects the Organizational Citizenship Behavior

H1c. Normative commitment positively affects the Organizational Citizenship Behavior

Relationship between organizational citizenship behaviors on job satisfaction

The investigates about OCB and its forerunners are creating as of not long ago. According to Jahangir et al (2012) several of its antecedents are leadership behavior, organizational commitment, motivation, job satisfaction and more.

According to Robbins and Judge (2013), job fulfillment is the most significant component on work disposition, and OCB is the consequence of abnormal state of employment fulfillment. A fulfilled worker will tend to indicate hierarchical citizenship conduct, for instance, a representative who has a decent association with his partners will tend to demonstrate benevolent conduct, for example, helping in achieving assignment, and others. Then again, authoritative citizenship conduct won’t happen if the worker isn’t glad, or at the end of the day, disappointed.

H2. The organizational citizenship behavior affects job satisfaction

Relationship between organizational commitment and job satisfactionSmith, Kendall and Huh (1969) view it as how much a worker, by methods for a full of feeling introduction or an uplifting demeanor, accomplishes a constructive outcome in connection to his/her activity, all in all, or to particular individual angles. Locke (1970) shields it as a wonderful or positive enthusiastic state emerging from the evaluation of the activity itself and from related encounters. Cook, Hepworth, Wall and Warr (1981) and Cranny, Smith and Stone (1992) view it as a full of feeling reaction emerging from the examination of genuine aftereffects of an individual occupation, contrasted with those normal, wanted, and required.

H3. Organizational commitments positively affect job satisfaction.

H3a. Affective commitment positively affects job satisfaction.

H3b. Continuance commitment positively affects job satisfaction.

H3c. Normative commitment positively affects job satisfaction.

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Mediating effect of organizational citizenship behaviorSri Indarti et al (2017) the results of their study revealed that the mediating effect (indirect effect) of variable organizational citizenship behavior was found in between personality, organizational commitment and job satisfaction on performance, which thus indicates that the higher the personality, organizational commitment and job satisfaction the higher the performance, and if mediated, organizational citizenship behavior is also higher.

H4. The Organizational Citizenship Behavior mediates the relationship between organizational commitment and job satisfaction.

H4a. The Organizational Citizenship Behavior mediates the relationship between affective commitment and job satisfaction.

H4b. The Organizational Citizenship Behavior mediates the relationship between continuance commitment and job satisfaction.

H4c. The Organizational Citizenship Behavior mediates the relationship between normative commitment and job satisfaction.

Objectives of the StudyThe given literature review states the mediating effect of organizational citizenship behavior on organizational commitment and job satisfaction of the employees in home appliances manufacturing firms in India. The study presented here is mainly focused on the goals:

• To explore the impact of organizational commitment (Affective, Continuance and Normative) over organizational citizenship behavior of production engineers.

• To examine the influence of Organizational Citizenship Behavior in the engineer’s job satisfaction level.

• To analyze the impact of organizational commitment practices in the engineer’s job satisfaction level.

• To investigate the possibilities of OCB as a behavioral mediator of the relationship between organizational commitment and job satisfaction.

• Propose a model to evaluate the mediating role of OCB on organizational commitment and job satisfaction among production engineers.

MethodologyResearch framework

Based on a literature review and the research hypotheses described above, this study has developed a conceptual framework for this research as shown in Fig 1.

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Fig 1.Research Framework

Construct developmentThe interview schedule has been divided into four parts. The first part of the interview schedule deals with the demographic profile of the respondents. The second part deals with organizational commitment (i.e.) Affective commitment (8variables), Continuance commitment (8 variables) and Normative commitment (8 variables) Allen and Meyer (1990), The third part deals with the OCB dimensions (i.e.) Conscientiousness (5 variables) (Husin et al., 2012) Solha, Packianathan, & Ghazali, (2012) (3 items) and Asim, Muhammed, Ali, Syed, (2012) (2 items), Altruism (5 variables) Solha, Packianathan, & Ghazali, (2012) (4 items) and Asim, Muhammed, Ali, Syed, (2012) (1 item) and Team building (5 variables) Solha, Packianathan, & Ghazali, (2012) (4 items) and Piercy,(2006) (1 item). The fourth part of the questionnaire measures the engineers’ job satisfaction (5 variables) Bernotaite (2013), Mangy (2012). All the variables taken up for the consideration in this study are measured with 5 points Likert scale with the range of 1 – Strongly disagree to 5 – Strongly agree.

Data collectionThe primary objective of this study is to evaluate the mediating role of Organizational Citizenship Behavior on organizational commitment and job satisfaction among engineers especially working in research & development and product & design from home appliances manufacturing units in South Indian firms. A total of 427 respondents were considered and purposive sampling was used to assess the opinions of production engineers regarding HRM practices, Organizational Citizenship Behavior and engagement. Out of the above, only 398

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questionnaires were returned and found to be in reusable level resulting in a response rate of 95%. Data collected through the questionnaire has been analyzed to fulfill the objectives of the study.

Results5.1. Sampling frameTable 1 shows that all respondents identified were Male, with 100%. Regarding age criteria, it is found that maximum respondents are found between the age limit of 20-30 years which constitute 61.3%. Out of 200 respondents, 62% of them are married and as far as educational qualification is concerned, it is noticed that most of them are graduates (80.6%). While considering the experience, it is seen that 80.1% of employees possess experience between 1-5 years in the present organization and 67.4% of employees have experience between 6-10 years. 46.1% of employees draw a monthly salary of above Rs.20, 000.

The researcher uses the structural equation model (SEM) to empirically explore the measurement and structural models.

Table 1 Demographic Profile of the sample respondents: (N = 398).

S. No. Demographic Variables Number of respondents

(N=398)

Percentage

1. Age20-30 years31-40 yearsAbove 40 years

25211729

63.3%29.3%7.28%

2. GenderMaleFemale

398-

100%-

3. Marital StatusMarriedUnmarried

253145

63.5%36.43%

4. Educational QualificationGraduatePost graduate

32474

81.40%18.59%

5. Experience in the present organization1- 5 years6-10 yearsAbove 10 years

3246212

81.40%15.57%3.91%

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6. Total Experience1- 5 years6-10 yearsAbove 10 years

8626943

21.6%67.58%10.80%

7. Monthly Salary1. Below Rs. 10,0002. Rs.10,000-15,0003. Rs.15,000-20,0004. Above Rs. 20,000

1657139186

4.02%14.32%34.92%46.73%

Model modificationThe researcher first conducted a CFA to examine the construct distinctiveness of the three main variables used in the current study. We report the GFI, CFI, NNFI, SRMR, RMSEA and Normed Chi-square following Hair, Anderson, Tatham & Black, (1998). Organizational Citizenship Behavior shows good fit for GFI 0.953 (better > 0.90), CFI 0.965 (better > 0.950), NNFI 0.939 (better > 0.90),SRMR 0.052 (better < 0.08), RMSE 0.069 (better < 0.08), Normed Chi-Square 2.289 (<3). However, the overall indexes of organizational commitment do not meet the criteria (GFI 0.763, CFI 0.723, NNFI 0.702, SRMR 0.091, RMSEA 0.113, and normed chi-square as high as 4.912). Even though job satisfaction RMSEA (0.163) and normed chi-square (5.298) do not show a good index; the GFI (0.907), CFI (0.959), NNFI (0.939), and SRMR (0.052) index still reveal a good fit index. Since the overall result of the initial model’s confirmatory factor analysis is far from good, it was modified by removing some items. By these modifications the problem of insignificant measurement error was solved, and the model fit reached a good standard, as shown in Table 2.

Table 2 - Final model’s confirmatory factor analysis.

Index Organizational Commitment

Organizational Citizenship Behavior

Job Satisfaction

Final model variables 1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18,19,20,21,

22,24,25,26,27

21,22,23,24,25,26,27,28,29,30,31,32,33,34,35

36,37,38,40

GFI 0.923 0.953 0.977

CFI 0.979 0.965 0.951

NNFI 0.913 0.939 0.973

SRMR 0.050 0.052 0.469

RMSEA 0.071 0.069 0.068

Normed chi-square 2.223 2.224 3.437

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Measurement modelAfter modifying the research model, the measurement test was performed, with the results shown in Table 3. The value of skewness is still less than 2 and the kurtosis is less than 7, which means that the modified model is normal, meeting the normality assumption (Curran, West, & Finch, 1996).

Table 3

Final model’s Mean, Standard Deviation, Kurtosis and Skewness

Variable Dimension Mean Std. Deviation Kurtosis Skewness

Organizational commitment


3.549 0.73987 0.437 0.857


3.713 0.73498 0.263 0.247


3.103 0.71487 0.429 0.116

Organizational citizenship behavior

Altruism 3.123 0.73456 0.834 0.082

Conscientiousness 3.286 0.70273 0.898 0.149

Team building 3.301 0.70534 0.303 0.712

Job satisfaction JOS 1 2.323 0.86648 0.423 0.456

JOS 2 2.598 1.24560 0.856 0.117

JOS 3 3.397 1.39115 0.919 0.267

JOS 4 2.302 1.30567 0.812 0.398

Table 4 shows the reliability and correlation matrix of each dimension. The reliability of the construct is acceptable if Cronbach’s a exceeds 0.70, and item-to-total correlations have greater than 0.50 (Sparkman, Hair, Anderson, Tatham, & Grablowsky, 1979). Affective commitment has significant correlation towards all others dimensions, continuance commitment is significantly correlated with affective commitment, normative commitment, organizational citizenship behaviour, and job satisfaction, and normative commitment is significantly correlated with all other dimensions except organizational citizenship behavior. Also, altruism is correlated with affective commitment, continuance commitment and teambuilding, while only continuance commitment and job satisfaction are not correlated with conscientiousness. Team building shows correlation with all commitment dimensions and job satisfaction.

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Table 4Final model’s Reliability Analysis and Correlation Matrix of dimensions.

Construct Dimension Num ber of Items

Cronbach’salpha

AFC COC NOC ALT CONS TB JOS

Dimen- sion

Vari able


Affective commitment

9 0.73 0.731 1

Continuance commitment

8 0.72 0.376** 1


8 0.86 0.346** 0.234** 1

Organizational citizenshipbehavior

Altruism 5 0.70 0.739 0.229* 0.071 0.187** 1

Conscien- tiousness

5 0.74 0.134** 0.070 0.202** 0.123 1

Team building

5 0.73 0.409** 0.187** 0.061 0.403** 0.197** 1

Job satisfaction

4 0.71 0.624 0.205** 0.305** 0.049 0.067 0.367** 1

Note : AFC – Affective commitment, COC– Continuance commitment, NOC– Normative commitment, ALT – Altruism, CONS – Conscientiousness, TB – Team building, JOS – Job satisfaction**Correlation is significant at the 0.01 level (2-tailed).*Correlation is significant at the 0.05 level (2-tailed).

Structural ModelDirect relationship Fig. 2 shows the results of proposed model. The path coefficient from affective commitment (AFC) to Organizational Citizenship Behavior (OCB) is significantly positive (0.83, t- value 4.86), supporting the hypothesis that affective commitment (AFC) positively affects organizational citizenship behavior (H1a). The path co-efficient from continuance commitment (COC) to Organizational Citizenship Behavior (OCB) is not significant (0.16, t-value 1.23), which does not support the hypothesis (H1b) that continuance commitment positively affects Organizational Citizenship Behavior (OCB). The path coefficient from normative commitment (NOC) to Organizational Citizenship Behavior (OCB) is significant (0.47, t-value 1.98), which support the hypothesis (H1c) that normative commitment (NOC) affects organizational citizenship behavior (OCB).

The path coefficient from Organizational Citizenship Behavior (OCB) to job satisfaction (JOS) significantly shows a positive relationship (0.84, t-value 3.88), and so the hypothesis

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that the Organizational Citizenship Behavior positively affects job satisfaction (H2) is supported. The path coefficient from affective commitment (AFC) to job satisfaction is not significant (0.42, t-value 2.09), so the hypothesis that affective commitment positively affects job satisfaction is not supported (H3a). The path co-efficient from continuance commitment to job satisfaction is not significantly positive (0.13, t-value 1.54), which does not support the hypothesis (H3b) that continuance commitment positively affects job satisfaction. However, the path coefficient from normative commitment (NOC) to job satisfaction is significant and shows a positive relationship (0.90, t- value 4.93), upholding the hypothesis (H3c) that normative commitment (NOC) positively related to job satisfaction. To sum up, Table 4 shows the variable path model.

Chi-square = 160.13, P-Value = 0.00000, RMSEA = 0.063

The indirect relationship (mediating effect) Following the direct relationship analysis, Table 5 shows the existence of mediating effect on the structural model of this study. The relationship between affective commitment and job satisfaction shows significant indirect effect (t- value 2.93). The relationship between affective commitment and Organizational Citizenship Behavior is upheld as is the relationship between Organizational citizenship Behavior and job satisfaction. But there is no significant relationship between affective commitment and job satisfaction, and so the mediating effect of Organizational Citizenship Behavior in the relationship of Retention Oriented Compensation and Engagement is fully supported (H4a). Furthermore the relationship between continuance commitment and job satisfaction shown in Table 5 does

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not show a significant indirect effect (t-value 1.27), so H4b is not supported. Meanwhile, the relationship between normative commitment and job satisfaction shows significant indirect effect (t- value 2.88). The relationship between normative commitment and Organizational Citizenship Behavior is not supported, while the relationship between Organizational Citizenship Behavior and job satisfaction, as well as the relationship between normative commitment and job satisfaction are supported, hence H4c is fully supported.

Table 5 Discriminant validity.

Variable Model C2 df Dc2


Unconstrained 193.56 79 -

Affective commitment – continuance commitment

286.23 81 81.72*

Continuance commitment – Normative commitment

378.57 83 149.63*

Affective commitment – Normative commitment

407.12 86 223.57*



Altruism – conscientiousness 386.48 71 248.38*Conscientiousness – team building

413.78 73 253.67*

Altruism – Team building 446.75 79 278.71*

Table 6 Variable path table

Path Parameter estimate

Standard error

t-value Standard solution

Hypothesis Result

AFC-OCB 0.81 0.21 4.67*** 0.79 H1a Supported

COC-OCB 0.17 0.17 2.01 0.11 H1b Not supported

NOC-OCB 0.43 0.16 2.11 0.45 H1c supportedOCB-JOS 0.84 0.31 3.87*** 0.76 H2 Supported

AFC-JOS 0.56 0.31 1.97 0.53 H3a Not supported

COC-JOS 0.33 0.24 2.45 0.23 H4b Not supported

NOC-JOS 0.79 0.31 4.91*** 0.82 H5c Supported

Note: ***t-value is significant at the 0.001 level

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Scope for Further ResearchThe present study has three major limitations which should be focused in future study. First, the study was limited to production engineers along with relatively small sample size which was preferred through purposive sampling method confined with small geographic area. Second, since the data was collected from a few production sectors located in Tamilnadu, we may not know the generalizability of the findings. Future research is thus; recommend to collect data from other states, nations, public enterprises or non-profit organizations to investigate the effect of HRM practices on turnover intention. Third, we examined OCB as the only behavioral mediator in the model. Thus, we still do not confirm much about other behavioral reactions mediate the relationship of HRM on employee engagement. Future studies may therefore further investigate a variety of behavioral mediators such as job craft, employment engagement and flexibility on the relationship.

ConclusionThe current research work tries to answer the questions of how and why organizational commitment practices reinforce desirable individual consequences. In particular we find that: (1) affective commitment and normative commitment are positively related to OCB (2) OCB increase engineers’ job satisfaction (3) OCB fully mediates the effects of affective commitment and normative commitment on job satisfaction.

References1. Allen, N. and Meyer, J. (1990). The measurement and antecedents of affective,

continuance, and normative commitment to the organization. The Journal of Occupational Psychology, 63, 1-18.

2. Asim, M., Muhammad, A.S., Ali, I., & Syed, M.A.J. (2012). Impact of HR Practices on Organizational Citizenship Behavior and Mediating Affect of Organizational Commitment in NGO’s of Pakistan. World Applied Sciences Journal, 18(7), 901-908.

3. BernotaiteZ(2013) Importance of Motivational Factors among Young Employees in the Service Sector.

4. Clugston, M. , Howell, J.P. and Dorfman, P.W. (2000), “Does cultural socialization predict multiple databases and foci of commitment?”, Journal of Management , Vol. 26 No. 1, pp. 5-30.

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7. DavoudiS (2012) A Comprehensive Study of Organizational Citizenship Behavior (OCB): Introducing The Term, Clarifying Its Consequences and Identifying Its Antecedents. A Journal of Economics and Management 1: 73-85.

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9. Husin, S., Chelladurai, P., & Musa, G. (2012). HRM Practices, Organizational Citizenship Behaviors, and Perceived Service Quality in Golf Courses. Journal of Sport Management, 26(2), 143–158. https://doi.org/10.1123/jsm.26.2.143

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14. Meyer, J. and Allen, N. (1991), “A three-component conceptualization of organizational commitment”, Human Resource Management Review , Vol. l No. 1, pp. 61-98.

15. Mirabizadeh, M., & Gheitasi, S. (2012). Examining the organizational citizenship behavior as the outcome of organizational commitment: Case study of universities in Ilam. Management Science Letters, 2(3), 951–960. doi:10.5267/j.msl.2012.01.016

16. Piercy, N. F. (2006). Driving Organizational Citizenship Behaviors and Salesperson In-Role Behavior Performance: The Role of Management Control and Perceived Organizational Support. Journal of the Academy of Marketing Science, 34(2), 244–262. https://doi.org/10.1177/0092070305280532

17. Robbins S, Judge T (2013) Organizational Behavior: Pearson Education.

18. Smith, P. C., Kendall, L. M., & Huh, C. L. (1969). The measurement of satisfaction in work and retirement. Chicago: Rand McNally.

19. Solha, H., Packianathan, C., & Ghazali, M. (2012). HRM practices, Organizational Citizenship Behaviors, and Perceived Service Quality in Golf Courses. Journal of Sport Management, 26, 143 – 158.

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20. Sparkman, R. M., Hair, J. F., Anderson, R. E., Tatham, R. L., & Grablowsky, B. J. (1979). Multivariate Data Analysis with Readings. Journal of Marketing Research, 16(3), 437. https://doi.org/10.2307/3150726

21. Sri Indarti, Solimun, Adji Achmad Rinaldo Fernandes, Wardhani Hakim, (2017) “The effect of OCB in relationship between personality, organizational commitment and job satisfaction on performance”, Journal of Management Development, Vol. 36 Issue: 10, pp.1283-1293

22. Wasti, S. (2003), “Organizational commitment, turnover intentions and the influence of cultural values”, Journal of Occupational and Organizational Psychology , Vol. 76, No. 3, pp. 303-321.

MJ SSIM IX (I), 2, 2017

APPENDIXFinal model’s confirmatory factor analysis.

Index Organizational Commitment Organizational citizenship behavior Job Satisfaction

Final modelvariables

1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22,24,25,26,27

21,22,23,24,25,26,27,28,29,30,31,32,33,34,35

36,37,38,40

GFI 0.923 0.953 0.977

CFI 0.979 0.965 0.951

NNFI 0.913 0.939 0.973

SRMR 0.050 0.052 0.469

RMSEA 0.071 0.069 0.068

Normed chi-square

2.223 2.224 3.437

Final model’s Mean, Standard Deviation, Kurtosis and SkewnessVariable Dimension Mean Std.

DeviationKurtosis Skewness



3.549 0.73987 0.437 0.857


3.713 0.73498 0.263 0.247


3.103 0.71487 0.429 0.116


Altruism 3.123 0.73456 0.834 0.082

Conscientiousness 3.286 0.70273 0.898 0.149

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Team building 3.301 0.70534 0.303 0.712

Job satisfaction JOS 1 2.323 0.86648 0.423 0.456

JOS 2 2.598 1.24560 0.856 0.117

JOS 3 3.397 1.39115 0.919 0.267

JOS 4 2.302 1.30567 0.812 0.398

Final model’s Reliability Analysis and Correlation Matrix of dimensions.Construct Dimension Num

ber of Items

Cronbach’salpha

AFC COC NOC ALT CONS TB JOS

Dimen- sion

Vari able


Affective commitment

9 0.73 0.731 1

Continuance commitment

8 0.72 0.376** 1


8 0.86 0.346** 0.234** 1


Altruism 5 0.70 0.739 0.229* 0.071 0.187** 1

Conscien- tiousness

5 0.74 0.134** 0.070 0.202** 0.123 1

Team building

5 0.73 0.409** 0.187** 0.061 0.403** 0.197** 1

Job satisfaction

4 0.71 0.624 0.205** 0.305** 0.049 0.067 0.367** 1

Note : AFC – Affective commitment, COC– Continuance commitment, NOC– Normative commitment, ALT – Altruism, CONS – Conscientiousness, TB – Team building, JOS – Job satisfaction**Correlation is significant at the 0.01 level (2-tailed).*Correlation is significant at the 0.05 level (2-tailed).

Structural ModelDirect relationship

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The indirect relationship (mediating effect) Discriminant validity.

Variable Model C2 df Dc2



Affective commitment – continuance commitment 286.23 81 81.72*

Continuance commitment – Normative commitment 378.57 83 149.63*

Affective commitment – Normative commitment 407.12 86 223.57*



Altruism – conscientiousness 386.48 71 248.38*Conscientiousness–team building 413.78 73 253.67*

Altruism – Team building 446.75 79 278.71*

Variable path tablePath Parameter

estimateStandard

errort-value Standard

solutionHypothesis Result

AFC-OCB 0.81 0.21 4.67*** 0.79 H1a SupportedCOC-OCB 0.17 0.17 2.01 0.11 H1b Not supportedNOC-OCB 0.43 0.16 2.11 0.45 H1c supported

OCB-JOS 0.84 0.31 3.87*** 0.76 H2 SupportedAFC-JOS 0.56 0.31 1.97 0.53 H3a Not supportedCOC-JOS 0.33 0.24 2.45 0.23 H4b Not supportedNOC-JOS 0.79 0.31 4.91*** 0.82 H5c Supported

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EXAMINING THE INVESTMENT PROFILE OF HOUSEHOLD INVESTORS: A STUDY OF PUNJAB

* Dr. Tina Vohra

AbstractInvesting is the act of committing money or capital to an endeaver with the expectation of earning some income or profit in future. Investments are expected to yield some gain and experience capital growth over a period of time. Households and individuals are coined as potential investors as they have extra income in the form of savings which could be invested in the financial market. The study is an attempt to discuss the investment profile of the household investors of Punjab and to study the impact of the investors’ demographic characteristics on their investment profile. The study would provide valuable information to the financial institutions in order to design the marketing activities according to the preferences of the investors based on the demographic variables. The results of the study revealed that the investment profile of the investors has a significant relationship with their education level and occupation while gender, age, income and marital status have no significant relation with the investors’ investment profile. The results of the study also potrayed that the investors of different age groups make different choices and for different durations. Therefore, in spite of the fact that a wide range of products are available in the financial markets, the market still needs to come up with more and more innovative instruments. In fact, there is a need to create customized financial instruments in order to suit the needs of investors belonging to different age, occupation and marital groups, thereby making the financial markets more efficient and complete. Moreover, the male as well as female investors are likely to take similar investment decisions. The only thing female investors require is adequate support from their family as well as the society to ensure their adequate participation in the financial markets. Since, educated people understand and appreciate the benefits of making investments. Thus, organizing greater number of education and awareness camps will help to improve the participation of investors in the financial markets. The results of this study are very relevant to bank executives and investment managers and would help them to design an appropriate investment product for the clients, offering them products as per their demographic characteristics.

Keywords: Characteristics, Demographics, Household Investors, Investments, Profile

JEL Classification Code: G0, G11

* Assistant Professor, Department of Commerce, BBK DAV College for Women, Amritsar Punjab-143005, Contact No. 09780253300, E-mail [email protected]

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IntroductionInvesting is the act of committing money or capital to an endeaver with the expectation of earning some income or profit in future (www.investopedia.com). Investments are expected to yield some gain and experience capital growth over a period of time. Virtually all of us make investments. Even if an individual does not make a specific investment in stock, investments are still made in the form of contribution to pension plans, life insurance and post office saving schemes. Bank deposits and Real Estate investing are other forms of investments. Each of these investments has certain common characteristics such as the return and the risk associated with it which the investor must bear (Kabra et al., 2010).Households and individuals are coined as potential investors as they have extra income in the form of savings which could be invested in the financial market. Table 1, shows the contribution of the household sector towards various financial assets.

TABLE 1: FINANCIAL SAVING OF THE HOUSEHOLD SECTORItem Financial Saving of the Household Sector

(At Current prices) (Rs. billion)2011-12 2012-13 2013-14 2014-15

A. Change in Financial Assets 9335.43 10244.52 12792.54 12356.22 Currency 1062.42 1115.21 1019.19 1317.11 Bank Deposits 5259.70 5750.80 7741.76 5792.95

Non-banking deposits 100.21 172.66 305.67 274.36Trade Debt (Net) 45.09 31.83 48.38 41.77Share and Debentures 173.36 437.90 323.53 570.73Units of UTI 0 0 0 0Claims on Government -219.01 -71.23 76.58 -5.96Life Insurance Funds 1956.73 1820.97 2052.22 2347.16Provident and Pension Funds 956.92 1240.20 1362.23 2008.35

B. Change in Financial Liabilities 2901.17 3302.18 4598.04 2766.35C. Net Financial Saving of Household Sector 6434.26 6942.34 8194.50 9589.97Note: Data for 2012-13 and 2013-14 are provisional and that for 2014-15 is based on preliminary estimatesNote: Components may not add up to the totals due to rounding off.

Source: Reserve Bank of India.According to a report issued by the RBI Working Group on Savings, the projected household saving rate in India is likely to increase from 23.2 per cent in 2011-12 to 25.2 per cent in 2016-17, giving an average of 24.4 per cent during the Twelfth Plan (RBI, 2012).

Table 1 shows the savings of the household sector in various financial assets for a period from 2011-12 to 2014-15.

According to a survey by NCAER 2011, the savings and investments by the household sector as a percentage of GDP rose above 30 per cent during the last decade, which is a positive sign for a developing economy.

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Review of LiteratrueThe studies on the investment profile of the household investors has always attracted the attention of the bank executives and investment managers as the knowledge of the same would enable them to design an appropriate investment product for their clients. Gupta (1993) conducted a nationwide survey of 40998 unit holders and 165819 shareholders. The study covered a wide cross section of households, including 19 states/Union Territories and 76 cities/places in India. The objective of the survey was to provide an insight into the changing perceptions and preferences of investors in India. The study revealed a massive shift towards mutual fund products, a moderate continuing shift towards shares and debentures and a shift away from traditional financial assets viz. national saving certificates, life insurance policies, bank fixed deposits and company deposits. Gupta and Choudhury (2001) attempted to examine and compare the preferences of investors for different mutual fund schemes and other financial products such as equity shares, bonds, bank FDs and government saving schemes. The data for the study was collected through a survey of 312 household investors. The authors’ observed that 90% of mutual fund investors had spread their mutual fund holdings over two or more funds. The results of the study revealed that UTI owned US-64 was the most popular mutual fund scheme. Gupta et al. (2001) conducted an All-India survey on the investment preferences of Indian households. The objective of the survey was to provide an intimate view of several aspects of the capital market developments and to get an idea of the investment habits, problems, perceptions and intentions of retail investors in India. The results of the survey revealed that there was a significant shift in the investors preferences from shares towards fixed-income investments i.e. bonds. High quality bonds like the DFI bonds were the most preferred instruments while the bonds of the private-sector companies were the least preferred ones. Rajarajan (2002) identified the association between the demographic profile and the risk bearing capacity of individual investors in Chennai. Out of 450 questionnaires administered, 405 useable responses were received. Chi Square test and Correspondence analysis were applied to the data. The results of the study revealed that there was a strong association between the demographic profile of the individuals and their risk bearing capacity. Kiran and Rao (2004) aimed at segmenting the investors on the basis of their demographic and psychographic characteristics. Demographic variables included age, gender, marital status, place, education, profession, employment status, number of dependents in the family and the annual income of the investor while the psychographic characteristics included risk taking ability and preference for safety, tax saving, liquidity, long term appreciation, high returns, flexibility of installments, risk coverage and size of investments. The investment and saving instruments included stocks, bonds, IPOs, real estate, gold, post office (NCDs), fixed deposits, insurance, recurring deposits, PPF, pension funds and SIPs. Out of 200 questionnaires administered, 96 usable responses were received from all over India. The data was analyzed using Multinomial

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Logistic Regression (MLR) and Factor Analysis (FA). The results of the study indicated that the risk-bearing capacity of the investors was strongly related to their demographics and psychographics. Gupta and Jain (2008) conducted an all-India survey of 1463 household investors on “The Changing Investment Preferences of Indian Households”. The survey was conducted to examine the investor’s preferences towards various types of financial assets and also to study their problems related to the stock market. The data for the study was collected through a structured questionnaire. The study found that the household investors preferred investing in shares as compared to mutual funds. The middle and the upper middle class preferred mutual funds and shares as compared to bank deposits and Govt. savings. Kumar et al. (2008) studied the financial product preference of the respondents belonging to Tiruchipalli town of Tamil Nadu in order to rank their preferences in dealing with six financial investment products i.e. post office savings, bank deposits, gold, real estate, equity investments and mutual funds. A sample consisting of 120 respondents was chosen using Stratified random sampling. The respondents were asked to rank the financial products on the basis of various attributes namely safety of principal, liquidity, stability of income, capital growth, tax benefit, inflation resistance and concealability. The respondents were selected from the tax payers list of the local administration office of Tiruchirapalli Corporation consisting of 60 blocks. Analytical Hierarchy process and Multi criteria decision making techniques were used to analyze the data. The results of the study revealed that there was no financial product that was better on each attribute. It was found that post office savings were the most preferred financial product followed by bank deposits, gold, real estate, equity investments and mutual funds. NCAER (2008) conducted an All India Survey on ‘How India Earns, Spends and Saves’ covering 60,000 urban and rural households. The Survey was conducted to gain insight into the motives of financial savings, the degree of financial security and sophistication of saving and investment decisions made by households in India. The results of survey revealed that people in India save for the long term goals such as emergencies, education and old age but do not invest in long term instruments. The survey also revealed that most of the respondents (65%) preferred keeping their savings in bank and post office deposits and other liquid assets, while 23% of them invested their savings in real estate and gold and 12% of them in financial instruments. Kabra et al. (2010) studied the factors that influenced the investment decision making process of the respondents. The study exmined the behavior of various types of investors working in government and private sectors in India on the basis of their income as well as the amount invested by them. Out of the 700 questionnaires administered, 196 usable responses were received. Techniques such as Regression analysis and Factor analysis were used to analyze the data. The authors concluded that risk averse people preferred to invest in insurance policies, fixed deposits with banks, post office, PPF and NSC. It was also found that the investor’s age and gender affected their risk taking capacity. Parashar (2010) attempted to find out the effect of

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personality traits on investment choice made by individual investors. The data was collected with the help of a structured questionnaire. The questionnaire was designed to study the investor’s risk tolerance and their investment preferences. Cluster Analysis, Kruskal Wallis test, Factor Analysis and Correspondence Analysis were used to analyze the results of the study. Real estate and mutual funds were found to be the most preferred choices of investment among investors while PPF and bank fixed deposit were the least preferred. The results of the study also revealed that risk takers and adventurous people tend to invest in equity and real estate while risk averse people invest in bonds and mutual funds. Sashikala and Prasad Ravi (2010) aimed at segmenting the investors on the basis of their demographic characteristics such as gender and age. A combination of Cluster Analysis and Logit Regression was performed in order to analyze the data. The results of the study revealed that the investment choice of investors varied with their demographic characteristics such as gender and age. Kansal and Singh (2013) attempted to study the gender differences in investment preferences in India. The data from the 2011 Survey of National Council of Applied Economic Research comprising of 38,412 respondents from 52 states and Union territories of India was used for the purpose of the study. Chi square test was used to analyze the results of the study. The results of the study revealed that there was no significant difference between the male and female households as far as investing in the secondary market was concerned. Goyal and Sharma (2014) attempted to discuss the investment behavior adopted by the service and the business middle class people of Rajasthan. 100 respondents were chosen for the purpose of the study and Descriptive Statistics were applied in order to analyze the investment behavior of the respondents. The results of the study revealed that the middle class investors preferred to invest in investment instruments namely real estate, bullion, precious stones, money market and capital market.

Research DesignNeed of the StudyWith the liberalization of the economy and the support of an efficient banking system the Indian capital market now offers a plethora of investment options. The financial services are highly diversified these days. This diversification offers individual investors with a wide range of investment instruments to invest in. Moreover, the other factors like the robust growth in the Indian economy as well as the increase in the amount of information availability have all resulted into the shift in the general trend of investments made by Household Investors. Today new and innovative financial instruments are being offered to meet the requirements of the investor. The present study is an attempt to discuss the impact of the investors demographic characteristics on their investment profile i.e. the type of investment chosen by them, the sources of investment information, the duration for which the investments are made and the purpose behind every investment. The study would

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provide valuable information to the financial institutions in order to design the marketing activities according to the preferences of the investors based on the Demographic Variables. The information on the relationship between the investors demographic characteristics and their investment profile i.e. the type of investment chosen by them, the sources of investment information, the duration for which the investments are made and the purpose behind every investment is very relevant to bank executives and investment managers in order to design an appropriate investment product for the clients.

Objective of the Study The objective of the present study is to discuss the impact of the investors demographic characteristics on their investment profile i.e. the type of investment chosen by them, the sources of investment information, the duration for which the investments are made and the purpose behind every investment.

Research MethodologyThe study is purely based on primary data which was collected using a pre-tested, well-structured questionnaire. The questionnaire was divided into two parts. The first part of the questionnaire was designed to probe into the investment profile of the household investors of Punjab. The respondents were asked questions relating to the type of investment chosen by them, the sources of investment information, the duration for which the investments are made and the purpose behind every investment. Figure 1 shows the various constituents of an investors profile.

FIG 1: CONSTITUENTS OF INVESTORS PROFILE

Investors’ ProfileThe investors’ profile deals with the demographic characteristics which includes age, education, occupation, marital status, income and gender as shown in figure 1.

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Investment ProfileAs shown in Figure 1., investment profile i.e. the type of investment chosen by them, the sources of investment information, the duration for which the investments are made and the purpose behind every investment is one of the constituent of an investors profile.

• Investment Alternatives: Investors are generally selective in investing. The investment behavior of an individual depends upon his personal circumstances as well as his attitude. So it is the investment attitude of an individual that leads to the selection of a particular instrument in the investment portfolio (Kiran, D. and Rao 2004). The capital market offers an investor a wide array of investment alternatives to suit his lifestyle, his family needs and future requirements. There are many ways to invest the money, which are different from each other depending on the risk factor and returns they provide. To know which investment options are suitable, one has to find the characteristics of various investments methods. But investing money ultimately depends on the risk appetite of an individual.

• Investment Objectives: The choice of investment alternatives is governed by objectives such as safety of the principle, assured returns, adequate magnitude of the return and growth in return to commensurate with the rate of inflation. All these factors vary across various investment avenues and hence are also termed as the Purpose of Investment.

TABLE 2: COMPARISON OF THE VARIOUS INVESTMENT AVENUES BASED ON INVESTMENT ATTRIBUTES

Rate of Return

(Annual Income)

Rate of Return (Capital

Appreciation)

Risk Marketability Tax Benefit Conve-nience

Financial SecuritiesEquity Low High High High Yes HighNon convertible Debentures

High Low Low Average Nil High

Bank Deposits Low Nil Low High Yes HighProvident Fund Nil High Nil Average Yes HighLife Insurance Nil High Nil Average Yes HighMutual FundsGrowth/equity Low High High High Yes HighIncome/Debt High Low Low High Yes HighReal Assets

Real estate Low High Low Low Limited Average

Gold/silver Nil Average Average Average Nil Average(http://www.narachinvestment.com/comparison_of_investment_avenues.htm)

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• Sources of Investment Information

Besides the traditional sources of investment information like TV, Newspaper, Magazines, etc information is also available from modern sources such as Internet, Annual reports of companies, Stock Brokers. Besides these sources of information people make investments based on advice from relatives and friends.

• Duration of Investment

For investors, duration is an indicator of measure of the sensitivity of the price of a fixed-income investment to the change in interest rates. Duration is expressed as a number of years. The bigger the duration is, the greater the interest-rate risk or reward is for an investment. The duration for which the investment is made is a determining factor which the investor must consider while making an investment. Risk seeking clients make long duration investments as compared to risk avoiding clients (http://www.investopedia.com/terms/d/duration.asp#ixzz1u5TeiHdH).

The second part of the questionnaire was related to the demographic profile of the household investors. Demographic variables, namely age, marital status, occupation, education and monthly income were considered for the purpose of the study. Out of 100 questionnaires administered 93 usable responses were received. The data were collected from 100 household investors from the four major cities of Punjab, i.e. Amritsar, Jalandhar, Ludhiana and Chandigarh. Sampled respondents were selected using Purposive Sampling Method.

Simple percentage analysis as well as Chi Square test were used to analyze the results of the study. Chi square is a statistical measure used to make a comparison between theoretical population and actual data. The study makes use of Chi square test in order to explain whether the demographic variables and the investors profile are associated or not.

On the basis of these results, interpretations were made and conclusions were drawn. Statistical Package for Social Sciences (SPSS) was deployed for applying these tests.

Demographic Characteristics of the Household InvestorsThe demographic characteristics of the investors, i.e., Age, Gender, Educational Qualification Occupation and Income of the investors have a significant impact on the investments made by the Household investors. The sample comprised of a variety of respondents belonging to different economic and professional backgrounds. The demographic profile of the sample respondents is presented in Table no.3 which shows that the majority of the respondents i.e. 47% of respondents were falling in the age group of 20 to 40 years while 43% of the respondents belonged to the age group of 40 to 60 years, and only 9% of the respondents fall in the age group of over 60 years. Hence, it can be concluded that majority of the young investors were ready to take more risk as compared to the older age groups. Out of the total

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respondents 54% were males and 46% were female investors. As far as the educational qualification is concerned maximum numbers of respondents were educated and qualified i.e. 24% of the respondents were post-graduates, 58% were graduates and only 18% were undergraduates.

TABLE 3: DEMOGRAPHIC CHARACTERISTICS OF THE HOUSEHOLD INVESTORS

Demographic Characteristics No. of Respondents (%)

Age (yrs)20-40 44 (47)40-60 40 (43)

Over 60 9 (10)Total 93 (100)

GenderMale 50 (54)

Female 43 (46)Total 93 (100)

Educational QualificationPost Graduate 22 (24)

Graduate 54 (58)Under Graduate 17 (18)

Total 93 (100)

Occupation

Service 31 (33)Business 30 (33)

Academicians 16 (17)Others 16 (17)Total 93 (100)

Marital StatusMarried 58 (62)

Unmarried 35 (38)Total 93 (100)

Monthly Income (Rs.)Below 20,000 6 (6)20,000-40,000 62 (67)Above 40,000 25 (27)

Total 93 (100)Source: Compiled through survey.

The respondents belonging to different professional backgrounds were studied. 33% of the respondents were servicemen, 17% of the respondents belonged to the business category, 33% were academicians and 17% others. Maximum numbers of respondents (62%) were married while 38% were unmarried. The maximum numbers of respondents’ i.e. 67% had a monthly income ranging from Rs. 20,000 to 40,000.

Investment Profile of the Household InvestorsInvestment Profile of the investors include investment objective, preferable source of information regarding investments, preferred investment avenue and duration of investments

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made by household investors. The investment profile of the sampled respondents is presented in Table no.4.The investment objective of the majority of household investors is to provide safety in case of financial loss followed by liquidity and regular income, thus throwing light on the farsighted nature of the investors. As far as the source of investment information is concerned, investors may use more than one source to gain awareness regarding investments. Findings of the study reveal that investors attach high priority to published information, thereby preferring newspapers. bank deposits are the most popular investment avenue among household investors of Punjab, as it is one of the investment instrument, which ensures reasonable and regular returns along with safety of capital. Furthermore, it throws light on the fact that household investors in Punjab are risk averse. Findings of the study reveal that household investors make investments for a longer duration i.e. Over 10 years, thus throwing light on the fact that households are a potential source of long term investments.

TABLE 4: INVESTMENT PROFILE OF THE HOUSEHOLD INVESTORS

Investment Profile No. of Respondents (%)

Investment Objectives

Capital appreciation 5 (5)Safety 28 (30)Liquidity 22 (25)Tax Incentives 5 (5)Speculation 2 (2)Regular Income 22 (23)Surplus money 9 (10)Total 93 (100)

Preferred Source of information

TV 2 (2)Newspaper 42 (45)Magazines 2 (2)Internet 8 (10)Annual Reports 1 (1)Friends/Relatives 32 (34)Brokers’ Advice 1 (1)Personal 5 (5)Total 93 (100)

Preferred Investment Avenue

Equity shares 3 (3)Debentures 1 (1)Bank deposits 40 (43)P.O Savings 17 (18)Real Estate 3 (3)Mutual Funds 2 (2)Jewellery 27 (30)

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Duration of Investment

Total 93 (100)0-2 years 20 (22)2-5 years 24 (26)5-10 years 19 (20)Over 10 years 30 (32)Total 93 (100)

Source: Compiled through survey.

Analysis and DiscussionThe following are the hypotheses and the results of Chi Square analysis used in order to measure the impact of the investors demographic characteristics on their investment profile

H01: There is no significant relation between the Age and Investment Profile of the Household Investors.

TABLE 5: RESPONDENTS’ AGE → PEARSON CHI-SQUARE TESTS

Value df Asymp. Sig. (2-sided) Decision

Investment Objective 9.911 12 .624 Accept the Null HypothesisSource of information regarding investments

10.044 14 .759 Accept the Null Hypothesis

Choice of investment avenue 24.731 12 .016* Reject the Null Hypothesis

Duration of investments 21.494 6 .001* Reject the Null Hypothesis

Source: Calculated through SPSS, * indicates significant at 5% level of significance.

Since the p value for the variables investment objective and sources of information regarding investments is greater than 0.050 and that of choice of investment avenue and the duration for which the investment is made is less than 0.050 (as shown in Table 5), therefore, there exists a significant relationship between age and the choice of investment avenue as well as the duration for which the investment is made. This shows that the investors of different age groups make different choices and for different durations.

H02: There is no significant relation between the Gender and Investment Profile of the Household Investors.

TABLE 6: GENDER →PEARSON CHI-SQUARE TESTS


Investment Objective .925 6 .988 Accept the Null HypothesisSource of information regarding investments


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Choice of Investment Avenue 11.413 6 .076 Accept the Null HypothesisDuration of Investments 2.064 3 .559 Accept the Null Hypothesis

Source: Calculated through SPSS, * indicates significant at 5% level of significance.As shown in Table 6, the p value for all the variables is greater than 0.050, therefore, the null hypotheses is accepted. Thus, there exists no significant relationship between gender and the investment profile of household investors. This shows that the male as well as female investors are likely to take similar investment decisions.

H03: There is no significant relation between Education and the Investment Profile of the Household Investors.

TABLE 7: EDUCATIONAL QUALIFICATION → PEARSON CHI-SQUARE TESTS


Investment Objective 33.461 12 .001* Reject the Null HypothesisSource of information regarding investments

33.251 14 .003* Reject the Null Hypothesis

Choice of Investment Avenue 34.126 12 .001* Reject the Null HypothesisDuration of Investments 24.215 6 .000* Reject the Null Hypothesis

Source: Calculated through SPSS, * indicates significant at 5% level of significance.It is observed from Table 7 that the p value for all the variables is less than 0.050, therefore, there exists a significant relationship between education and the choice of investment profile of household investors i.e. objective of investment, source of information regarding investments, choice of investment avenue and the duration of investments. This throws light on the fact that educated people understand and appreciate the benefits of making investments.

H04: There is no significant relation between Occupation and the Investment Profile of the Household Investors.

TABLE 8: OCCUPATION → PEARSON CHI-SQUARE TESTS

Valuedf

Asymp. Sig. (2-sided)

Decision

Investment Objective 35.695 18 .008* Reject the Null HypothesisSource of information regarding investments

52.047 21 .000* Reject the Null Hypothesis

Choice of Investment Avenue 34.530 18 .011* Reject the Null HypothesisDuration of Investments 23.308 9 .006* Reject the Null Hypothesis

Source: Calculated through SPSS, * indicates significant at 5% level of significance.Since the p value for all the variables is less than 0.050, there exists a significant relationship between occupation and the investment profile of household investors (as shown in table

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8). The result clearly states that people belonging to different occupations have different investment choice.

H05: There is no significant relation between Marital Status and the Investment Profile of the Household Investors.

Table 9: Marital Status → Pearson Chi-Square Tests

Valuedf


Decision



Choice of Investment Avenue 9.245 7 .301 Accept the Null HypothesisDuration of Investments 11.007 4 .026* Reject the Null Hypothesis

Source: Calculated through SPSS, * indicates significant at 5% level of significance.

It is observed from Table 9 that the p value for all the variables except the duration for which the investments are made is greater than 0.050, as a result, there exists no significant relationship between marital status and the investment profile of household investors except in case of the duration of investment where difference in married and unmarried respondents exists.

H06: There is no significant relation between Monthly Income and the Investment Profile of the Household Investors.

Table 10: Monthly Income→Pearson Chi-Square Tests

Valuedf


Decision



Choice of Investment Avenue 16.204 12 .182 Accept the Null Hypothesis

Duration of Investments 11.719 6 .069 Accept the Null Hypothesis

Source: Calculated through SPSS, * indicates significant at 5% level of significance.Table 10 shows that there exists no significant relationship between monthly income and the investment profile of household investors (p value is greater than 0.050). This finding throws light on the fact that having surplus money may not induce the investors to invest. Lack of adequate knowledge about the various investment alternatives may be the one of reason that prevents people from investing their hard earned money. The finding also suggests that people are still in the habit of hoarding and they do not want to take risk parting with their money.

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Limitations of the Study1. The study has focused on investors’ demographic factors only. The behavior of the

investors may also affected by their psychological aspects but such aspects have been ignored in the study.

2. Sample size is limited to 93 household investors belonging to the major cities of Punjab, i.e. Amritsar, Jalandhar, Ludhiana and Chandigarh. A larger sample covering more geographical area would provide a better insight into the investment behavior of the household investors.

ConclusionThe objective of the study was to examine the impact of demographic characteristics on the investment decisions of household investors. The analysis provides insight into the effect of certain demographic characteristics on an individual’s investment preference. The validity of widely used demographics such as gender, age, income and occupation as determinants of investment is supported, although the relationships found are not as simple as they seem to be. The results of the study potrayed that the investors of different age groups make different choices and for different durations. Although, a wide range of products are available in the financial markets, the market still needs to come up with more and more innovative instruments. In fact, there is a need to create customized financial instruments in order to suit the needs of investors belonging to different age, occupation and marital groups, thereby making the financial markets more efficient and complete. The male as well as female investors are likely to take similar investment decisions. The only thing female investors require is adequate support from their family as well as the society to ensure their adequate participation in the financial markets. It is interesting to note that educated people understand and appreciate the benefits of making investments. Thus, organizing greater number of education and awareness camps will help to improve the participation of investors in the financial markets. The results of this study are very relevant to bank executives and investment managers in order to design an appropriate investment product for the clients, offering them products as per their demographic characteristics.

References1. Goyal, M., & Sharma, A. (2014). A study of investment behavior of middle income

group towards different kinds of investment avenues. IOSR Journal of Business and Management, 16(8), 1-10.

2. Gupta, L. C. (1993). Mutual funds and asset preference: household investor survey 2nd round. New Delhi: Sponsored by the Society for Capital Market Research and Development.

3. Gupta, L. C., & Choudhury, U. (2001). How good are mutual funds – The 5th household investor survey. New Delhi: Society for Capital Market Research and Development.

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4. Gupta, L. C., Jain, N., & Gupta, C. P. (2001). Indian households’ investment preferences with special reference to debt market instruments – Based on the 3rd all India household investors’ survey, New Delhi: Society for Capital Market Research and Development.

5. Gupta, L. C., & Jain, N. (2008). Indian households’ investment preferences - The third all India investors’ survey. New Delhi: Society for Capital Market Research and Development.

6. Kabra, G., Mishra, P. K., & Dash, M. K. (2010). Factors influencing investment decisions of generations in India: An econometric study. Asian Journal of Management Research, 1(1), 308-328.

7. Kansal, P., & Singh, S. (2013). Investment behaviour of Indian investors: Gender biasness, Proceedings of Seventh National Conference on Indian Capital Market: Emerging Issues. India: IBS Gurgaon.

8. Kiran, D., & Rao, U.S. (2004). Identifying investor group segments based on demographic and psychographic characteristics. Retrieved from http:// ssrn.com/abstract=870749.

9. Kumar, S.K., Banu, V.C., & Nayagam, G.L.V. (2008). Financial product preferences of Tiruchipalli investors using analytical hierarchy process and fuzzy multi criteria decision making. Investment Management and Financial Innovations, 5(1), 66-73.

10. NCAER (2008). Nationwide Survey of Indian Households on How India Earns, Spends and Saves. Retrieved from http://www.sebi.gov.

11. NCAER (2011). How households save and invest: Evidence from NCAER household survey. Retrieved from www.nseindia.com.

12. Pandian, P. (2009). Security analysis and portfolio management.(6th ed.). New Delhi: Vikas Publishing House Pvt Ltd.

13. Parashar, N. (2010). An empirical study on personality variation and investment choice of retail investors. Journal of Management Information Technology, 2(1), 33-41.

14. Rajarajan, V. (2002). Determinants of portfolio choice of individual investors. The Indian Economic Journal, 50(1), 81.

15. RBI (2012). Reserve Bank of India: Annual reports. Retrieved from www.rbi.org.in.16. Sashikala & Prasad R. (2010). A study on the effects of demographics on the choice of

investments and ability to take risk, Review of Business and Technology Research, 3(1), 44-49.

Websites:http://www.stockmarketindian.com/indian_stock_market_investing.php http://stockshastra.moneyworks4me.com/learn/where-can-i-invest-my-money/ http://www.narachinvestment.com/comparison_of_investment_avenues.htm

MJ SSIM IX(I), 3, 2017

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THE IMPACT OF ONLINE SERVICE QUALITY ON TOURISTS’ SATISFACTION: AN EMPIRICAL STUDY

* Dr. Nitasha Sharma

ABSTRACTThe information communication technology in India has affected Indian economy in a positive way. Those tourism service providers who are not adopting information communication technology for providing services to their customer, they are facing severe competition for their survival in tourism sector. Therefore, several firms are offering good quality services online in order to satisfy their customers. So, satisfaction of tourists depends on the overall service quality of services in today’s tech savvy world. Accordingly, current study is an attempt to shed the light on this topic and tried to find out the factors affecting the tourists’ satisfaction in online tourism. Data was collected with the help of primary sources by framing a questionnaire on the basis of review of literature and E-SERVQUAL scale. Data was analyzed with the help of Multiple Regression Analysis. It was concluded all the dimensions of online service quality affect tourists’ satisfaction. Impact of customers’ satisfaction on loyalty has not been examined. Further, researcher may empirically examine the relationship between e -service quality, customer loyalty and behaviour intention. The study may helpful for tourism service providers to understand those factors which are perceived important by tourists while they use online mode of booking.

Keywords: Impact, Tourism, Online, Tourists, Satisfaction

JEL Classification Code: M3, M31

IntroductionThe connection between E-service quality and tourists’ satisfaction is a debatable concept. Previous researchers concluded that there is strong link between service quality and satisfaction of customers (Madu and Madu, 2002 and Zeithaml et al., 2002). According to Parasuraman et al., 1988, “E-Service quality is a global judgement, or attitude, relating to the superiority of services, whereas satisfaction is related to a specific transaction”. As per Cronin, 2003, “E-service quality has been regarded as having the potential not only to deliver strategic benefits, but also to enhance operational efficiency and profitability”.

As a result, to continue to exist in ferocious rivalry, various tourism providers are trying their best to improve the quality of their services for the satisfaction of customers. They are also trying to tie up long run relationship with their customers.

* Assistant Professor, Department of Commerce, Doaba College, Jalandhar, Email id: [email protected], Contact no: 09814166796

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Dimensions of E-service Quality:Some of the specific dimensions of e-service quality are as follow:

1. Ease of Use: Wahab et al., 2010 described “Ease of use as the degree to which a person believes that using an information system would be free of effort”.

2. Website Design: It means the whole structure and information displayed on various websites. It also includes color, font size and set of styles. Due to cut throat competition, all the websites are multi functional. It can arouse the interest of tourists by providing good quality photographs, graphics. If a tourism website is remarkable, only then tourists will visit it again and again (Hudson et al., 2000) otherwise they will shift to some other tourism websites. The structure and content presented on these websites should be customer friendly. All the content should be in logical sequence for the facilitation of customers.

3. Responsiveness: According to Madu and Madu, 2002, “Responsiveness includes proper handling of tourists’ problems so that they feel comfortable while booking through tourism websites”. It also includes settling the queries of the tourists so that they suggest the same tourism website to their peer group.

4. Empathy: According to Parasuraman et al., 1985 “Caring and individualized attention, the firm provides to its customers is called empathy”. Empathy means how a tourism website is close to tourists to settle their queries. It is a process to realize tourists’ state of mind without talking to them. Empathy acts as a link between tourism website and tourists (Kaynama and Black, 2000).

5. Reliability: “It is consistency of performance and dependability on those companies who are offering services to the tourists in form of tourism packages” (Parasuraman et al. 1985 and 1988). It is also one of the important tools of e-service quality (Madu and Madu, 2002; Zeithaml et al., 2002 and Loiacono et al., 2002).

6. Security: Security is an important tool of e-service quality. According to Parasuraman et al., 1985, “Security is the degree to which the site is safe and protects customers’ interest”. Customers found peril in the basic atmosphere of e-service field due to misuse of financial and personal information.

7. System Availability: System availability refers to availability of website during navigation. When anyone tries to download something from a travel website, then tourism website should be available.

Customer SatisfactionUsually tourist’ expectations are greater than his perceptions (Kotler, 2006). It is assumed that if expectations of customers did not meet with their perceptions then they are not

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satisfied but it is a misconception. After reviewing the literature, it was concluded that the disconfirmation theory is a primary base for satisfaction of customers (Oliver, 1980 and McKinney et al., 2002). According to this theory, satisfaction is a gap between expectations and perceptions of customers regarding their buying behavior. Post purchase behavior implies that consumer will visit the same tourism websites again and again if he/she is satisfied with the quality of services provided by suppliers.

Customer satisfaction model presents the logic behind tourists’ satisfaction. Tourists are delighted if their expectations met with their perceptions. They feel discontented if their expectations did not meet with their perceptions.

Tourists are also like other customers so they have some initial expectations with their service providers. It is stated from review of literature that e-service quality is the ancestor of tourists’ contentment. Thus, efforts have been made in the current study to check the impact of e-service quality on tourists’ satisfaction.

Review of LiteratureFollowing studies (Shown in Table 1) have been reviewed in order to find out the gap of the study:

Table 1: Studies Related to Impact of Service Quality on

Tourists’ Satisfaction in Tourism SectorAuthor Country Research Objectives Variables Methodology Significant Factors

Mills and Morrison, 2000

Worldwide To distinguish the expected variables of tourists’ happiness and contentment with travel websites

Access, Efficiency, Loading, Appearance and Navigation

Confirmatory Factor Analysis

Appearance and Navigation

McQuilken et al., 2000

Otway, Victoria

To examine the prospects and perceptions of customers

Tangibility, Reliability, Responsiveness, Assurance and Empathy

Regression Analysis

Tangibility, Responsiveness and Empathy

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Author Country Research Objectives Variables Methodology Significant Factors

Liu and Arnett, 2000

Mississippi, USA

To discuss the different kind of information available on tourism website

Information, Service quality, System use, Playfulness and System design quality

Factor Analysis

Information, Service quality and System design quality

Van Riel et al., 2003

Netherlands To assess the indicators of quality with regard to pre-transaction services provided by travel websites

User interface, Accessibility, Navigation, Design, Reliability, Assurance, Responsiveness and Customization

Regression and Factor Analysis

Design and Overall quality of travel websites

Mohamed, 2007

Egypt To find out the gap between desirous services and actual services availed by tourists

Responsiveness, Reliability, Empathy, Resources and Corporate image and Tangibility

Descriptive Analysis

Resources and Corporate image

Ho and Lee, 2007

Worldwide To identify the factors affecting e service quality

Information quality, Security, Website functionality, Customer relationships

Factor Analysis

Information quality and Responsiveness

Prabha- karan et al., 2008

India To analyze the factors of service quality in overseas market and domestic market as well

Tangibility, Responsiveness, Reliability, Service product, Assurance and Service Responsibility

Structural Equation Modelling

T a n g i b i l i t y influences the domestic tourists and Responsiveness influences the foreign tourists

Al-Rousan et al., 2010

Jordon To study the impact of different factors of tourism service quality on tourists’ satisfaction in the Jordanian five star hotels

Service quality such as Empathy, Reliability, Responsiveness and Tangibility

Factor analysis

Empathy, Reliability and Tangibility

Renganathan, 2011

India To consider the hotel guests’ expectations and perceptions of hotel services

Tangibles, reliability, responsiveness, assurance and empathy

Multiple Regressions and Factor Analysis

Responsiveness and Assurance

Haghtalab et al., 2012

Iran To study the relationship between electronic satisfaction and service quality provided by tourism industry

Website convenience, safety, information, website design, and information

Confirmatory Factor Analysis and Path Analysis

All

Hafeez and Muhammad, 2012

Pakistan To create the model to find out the relationship between service quality, customer satisfaction and loyalty programs on customer’s loyalty

Impact of service quality, customer satisfaction and loyalty programs

Correlation and ANOVA

Customer loyalty

Moon, 2013

USA To explore the impact of e-service quality on satisfaction and loyalty

Web design aesthetics, ease of use and virtual tour

SEM Intangible, customer satisfaction and loyalty

Khan and Fasih, 2014

- To study the effect of service quality on customer satisfaction


One sample t-test

All

Oh and Kim, 2016

Different countries

To review hospitality and tourism research on customer satisfaction (CS), service quality (SQ) and customer value (CV)


Coding Scheme

All

Puing and Ming, 2018

China To examine the relationship between the quality of interaction with tourist attraction services and satisfaction

Satisfaction and Behavior re-intention

SEM Positive relation

Source: Adapted from Different Studies

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It is clear from review of literature that satisfaction is being affected by e- service quality. According to Oliver, 1980, Customer satisfaction is a vulnerable issue in the marketing though it has relevance since olden time. It is a part of discussion since concept of marketing has been introduced. Some authors (Madu and Madu, 2002 and Zeithaml et al., 2002) take it as forebear of customer satisfaction, while some other authors are disagreed to this statement (Oliver 1980).

Objective of the StudyAn overview of the literature depicts that satisfaction of tourists is always affected by the quality of services provided by tourism service providers. Satisfaction of those tourists have been examined at domestic level who have booked their journey from traditional travel agencies but no effort has been made so far to check the impact of e-service quality on tourists’ satisfaction in India. Thus, current study is an attempt to shed the light on the same topic.

Research MethodologyTo collect data, primary method of data collection was used. A questionnaire was drafted which was based on E-servqual instrument, originally framed by Zeithaml et al., 2002. Moreover, questionnaire also consists off some items based on review of literature. Data was collected on seven point likert scale where 1 was assigned for strongly disagree and 7 was assigned for strongly agree. Respondents were approached through convenience cum judgmental sampling method. Only those respondents were approached who have used online tourism at least once in their life. Data was collected in the cities of Amritsar, Jalandhar and Ludhiana. These cities were selected due to business tourism and religious tourism as representative cities of Punjab.

Hypotheses for the StudyTo check the impact of service quality on tourists’ satisfaction, some hypotheses were set which are as follows:

H01: There is no impact of the dimension ‘Ease of Use’ on tourists’ satisfaction

H02: There is no impact of the dimension ‘Website Design’ on tourists’ satisfaction

H03: There is no impact of the dimension ‘Responsiveness’ on tourists’ satisfaction

H04: There is no impact of the dimension ‘Empathy’ on tourists’ satisfaction

H05: There is no impact of the dimension ‘System Availability’ on tourists’ satisfaction

H06: There is no impact of the dimension ‘Reliability’ on tourists’ satisfaction

H07: There is no impact of the dimension ‘Security’ on tourists’ satisfaction

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Sample CharacteristicsAs far as the demographic profile (Table 2) of the respondents is concerned, the sample includes different kinds of persons belong to different kind of background. It can be seen from Table 2 that more male respondents participated in survey (55.8%) than female (44.2%) respondents. Furthermore sample population formed the majority (44.7%) in the age group of 30-40 years of age. The next largest category comprised the respondents from 40-50 years of age (23.7%). The next largest category was made up of those who are less than 30 years of age (22.9%). Furthermore, respondents falling in the age category of above 50 are just 8.8%.

Table 2: Demographic Profile of RespondentsParticulars Frequency Percent

GenderMale 266 55.8Female 211 44.2Total 477 100.0

Age (Yrs)

Less than 30 109 22.930-40 213 44.740-50 113 23.7Above 50 42 8.8Total 477 100.0

Marital Status

Married 290 60.8Single 183 38.4Divorcee 4 0.8Total 477 100.0

Education Level

Matriculation 159 33.33Graduation 245 51.4Post Graduation 66 13.8Any other 7 1.5Total 477 100.0

Occupation

Student 99 20.8Businessman 149 31.2Service 150 31.4Retired 41 8.6Housewife 27 5.7Others 11 2.3Total 477 100.0

Monthly Income(Rs.)

Less than 20000 73 15.320000-40000 264 55.340000-60000 110 23.1More than Rs 60000 30 6.3Total 477 100.0

Source: Compiled through Survey

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With regard to marital status of the sample, it is clear from Table 2 that almost 60.8% respondents are married and 38.4% are unmarried whereas 0.8% respondents are divorcee. As far as respondents’ occupation is concerned, then Table 2 explains that majority of the respondents belong to service category (31.4%), followed by businessmen (31.2%), students (20.8%), retired (8.6%) and housewives (5.7%) and other (2.3%). As far as education level is concerned then Table 2 depicts that 51.4% of the respondents are graduates followed by matriculates (33.33%). The next largest category comprised of those respondents who are postgraduate (13.8%). As per income categorization, Table 2 shows that 55.3% respondents are falling in the income category of Rs.20000-40000 followed by 23.1% who belongs to income category of Rs. 40000-60000. Though just 15.3% are falling in the income category of less than Rs. 20000 yet 6.3% are falling in the income category of above Rs.60000 income group.

Analysis and Interpretation of Impact of E-Service Quality on Tourists’ Satisfaction: Multiple Regression analysis was applied to observe the impact of e-service quality on tourists’ satisfaction. Total scores were calculated for dependent and independent variables. Tourists’ satisfaction and seven dimensions of e-service quality scale were taken as dependent variable and independent variables respectively. Tourists’ satisfaction (Table 3) was considered by taking the total score of five statements depicting tourists’ satisfaction for each and every respondent. Furthermore, a preface examination was done to ensure normality, linearity and homoscedasticity of data.

Table 3: Statements Depicting Tourists’ SatisfactionStatementsI am satisfied with the services offered on tourism websites I strongly recommend others to use the online tourism I intend to continue purchase services from the tourism websites in the future It is easy to compare prices of services (Tour Packages) on tourism websites Tourism websites will always be my first choice for future transactions rather traditional methods of booking

Source: Review of LiteratureThe independent variables in the form of regression equation can be expressed as follow:

Yi= β

0 + B

1X

1 + B

2X

2 + B

3X

3……. B

nX

n

Where,

Yi = Dependent Variable

β0 = Constant (coefficient of intercept)

X1,

X2,

X3---- Xn

= Independent Variables

B1, B

2, B

3---- Bn = Regression coefficients

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Another basic assumption of the regression i.e. correlation was examined to check the multicollinearity among independent variables. The maximum value of correlation among independent variables should not be greater than 0.70 (Malhotra, 2006). In the current study, a reasonable correlation was present among all the independent variables as shown in Table 4. It is confirmed that there is no problem of multicollenearity in the data.

Table 4: Correlation TableIndependent Variables

Website Design

Respon- siveness

Empathy System Availability

Reliability Security Ease of Use

Website Design 1.000

Responsiveness -.041 1.000

Empathy -.094 .083 1.000

System Availability

.036 .238 .183 1.000

Reliability -.215 .176 .285 .490 1.000

Security -.215 .156 .325 .352 .533 1.000

Ease of Use -.121 .114 .125 .193 .284 .404 1.000

Source: Calculated through SPSS

Therefore, after examining all the basic assumptions Multiple Regression Analysis was applied on the data. Besides this, results of ANOVA (Analysis of Variance) have been shown in the Table 5 which shows that regression model is fit. The significance level is less than 0.05 which means independent variables have been chosen in a good manner to explain the dependent variable.

Table 5: ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 3545.433 7 506.490 88.269 .000*

Residual 2691.150 469 5.738

Total 6236.583 476

a. Predictors: (Constant), Ease of Use, Responsiveness, Website Design, Empathy, System Availability, Security, Reliability

b. Dependent Variable: Tourists’ satisfaction

Source: Calculated through SPSS; * indicates significant at 5% level of significance

However, model summary has been shown in Table 6. R2 accounted for 0. 568 in the current study presents that 56.80% of the deviation in dependent variable (Tourists’ Satisfaction) has been caused by seven independent variables taken in the current study.

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Table 6: Model Summary

Model R R Square

Adjusted R Square

Std. Error of the

Estimate

Change Statistics Durbin-WatsonR Square

ChangeF

Changedf1 df2 Sig. F

Change

1 .754 .568 .562 2.39542 .568 88.269 7 469 .000* 1.831

a. Predictors: (Constant), Information, Responsiveness, Website design, Empathy, System availability, Security, Reliability

b. Dependent Variable: Tourists satisfaction

Source: Calculated through SPSS; * indicates significant at 5% level of significance

Moreover, Adjusted R2 is another important measure of the success of a model. In the current study its value is 0.5621 meaning hereby that it accounted for 56.20% of the variance in the dependent variable. Apart from it, the value of R shows that a significant relationship exists between dependent variable and independent variables.

Moreover, Table 7 presents main results of Regression Analysis and it is clear from Table 7 that dimension “Security” (b = .319, p = .000) is the best determinant of tourists’ satisfaction. It has a significant impact on the tourists’ satisfaction in online tourism as null hypothesis in case of this dimension was rejected at 5% level of significance. According to Than and Grandon, 2002, “Increasing numbers of online tourists have expressed concern over the potential misuse of personal information and abuses of privacy”. Tourism service providers should be more aware regarding the concept of security.

Table 7: Impact of E-service Quality on Tourists’ Satisfaction

ModelB

Unstandardized Coefficients

Standardized Coefficients

tSig.

Tolerance

Collinearity Statistics

Std. Error Beta VIF1 (Constant) 6.048 2.053 2.945 .003*

Website design .192 .042 .144 -4.521 .008* .906 1.104

Responsiveness .064 .025 .081 2.567 .011* .933 1.071

Empathy .179 .030 .192 5.919 .000* .875 1.142

System availability .147 .051 .105 2.891 .004* .701 1.427

Reliability .183 .042 .174 4.380 .000* .580 1.724

Security .319 .036 .344 8.792 .000* .602 1.662

ease of use .155 .032 .163 4.877 .000* .827 1.210

Source: Calculated through SPSS; Note :(*) Indicates significance at 5% level

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In addition to it, “Website Design” (b = .192, p = 0. 000) is also one of the important determinants of tourists’ satisfaction. As human interaction is absent during booking trip, so a clear and easy presentation of the content on tourism websites is necessary. This finding is similar to the findings of the Zeithaml et al., (2002) and Park and Gretzel (2007). They proposed that “organized and logical tourism website makes it easier for tourists to navigate around the website to locate relevant information”. Online tourism service providers should design their tourism websites in an easy and simple way to attract specially those tourist who are not advanced users of technology.

Further, findings of Regression Analysis also presented that dimension “Reliability” is a good analyst of satisfaction of tourists (b = .183, p = .000) trailed by “Empathy” (b = .179, p= .000). Beta coefficient showed that one unit change in Reliability would affect 0.183 units increase in tourists’ satisfaction if remaining variables are stable. Null hypotheses for both dimensions i.e. Reliability and Empathy were rejected at 5% level of significance which shows that dimensions Reliability and Empathy are affecting the tourists’ satisfaction in online tourism. It means tourism service providers should provide highly consistent services and they should proficiently respond to tourists’ queries and grievance related to their journey.

In addition to it, dimension “Ease of use” (b = .155, p = .000) has significant impact on tourist’ satisfaction also. However, 1 unit variation in “Ease of Use” will cause 0.155 unit increase in satisfaction of tourists. Apart from it, null hypothesis for dimension “Ease of Use” was not accepted at 5% level of significance which means dimension “Ease of Use” is also affecting tourists’ satisfaction in a significant way. These findings are similar to the conclusion of Kim and Lee, 2004. As a consequences, there is a strong need to make the dimension ease of use important. In order to maintain tourists, content displayed on tourism websites should be easy to understand. Moreover, many authors made emphasis on easy payment and easy downloading. For online travel companies, it is important to supply services with ease than generating an exceptional and complex website for tourists. These outcomes are supported by the result of Kim and Lee, 2004. Furthermore, dimension “System Availability” (b = .147 p = .004) has also significant association with tourists’ satisfaction. Null hypothesis is rejected at 5% level of significance for the dimension “System Availability” meaning hereby that dimension “System Availability” has a significant contribution to determine tourists’ satisfaction. However, one unit increase in dimension “System Availability” will change 0.147unit changes in tourists’ satisfaction if other variables remain constant. This result is supported by the findings of Kim and Lee, 2004.

Further, dimension “Responsiveness” (b = .064, p = .011) was also found to be an important dimension affecting the satisfaction of tourists as null hypothesis for this dimension was not accepted at 5% level of significance. Findings of the current study are corresponding

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to the results of Ho and Lee, 2007 and Kim and Lee, 2004. Tourism service providers have to provide accurate and prompt services if they want to sustain the existing tourists. Many authors (Kaynama and Black, 2000; Kim and Lee, 2004 and Parasuraman et al., 2005) have recognized responsiveness as one of the chief factor for determining the satisfaction of the tourists. Cai and Jun (2003) reported that “the dimension of Responsiveness as one of the important dimension in e-service quality, as consumers not only want to receive reliable services but also want prompt services”. It is shown in Table 7 that dimension “Security” is the most significant predictor of tourists’ satisfaction trailed by the dimension “Website Design”, “Reliability”, “Empathy”, “Ease of Use”, “System Availability” and “Responsiveness”. Therefore, e -service quality is the forerunner of satisfaction of tourists. Table 8 presents the summary of findings of regression results.

Table 8: Summarized Findings of HypothesesHypotheses Std.

(b)Significance

LevelResults

H01: There is no impact of the dimension ‘Ease of Use’ on tourists’ satisfaction

.155 .000* Rejected

H02: There is no impact of the dimension ‘Website Design’ on tourists’ satisfaction

.192 .000* Rejected

H03: There is no impact of the dimension ‘Responsiveness’ on tourists’ satisfaction

.064 .011* Rejected

H04: There is no impact of the dimension ‘Empathy’ on tourists’ satisfaction

.179 .000* Rejected

H05: There is no impact of the dimension ‘System Availability’ on tourists’ satisfaction

.147 .004* Rejected

H06:There is no impact of the dimension ‘Reliability’ on tourists’ satisfaction

.183 .000* Rejected

H07:There is no impact of the dimension ‘Security’ on tourists’ satisfaction

.319 .000* Rejected

Source: SurveyTherefore, it can be said that tourists are benefitted by latest technology. Tourism service providers should set online tourism services in a better way so that tourists should not shift to their competitors as well as to traditional travel agents. Overall satisfaction of tourists is required as it is the only thing that assists tourism service providers to protract and augment their business. It is said that a pleased tourist will add one more tourist but a discontented tourist will cut back ten tourists (Uppal and Mishra, 2011).

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Scope for Further ResearchCurrent study presented various important findings for the studies on e-service quality. But every research work is subjected to certain limitations and current study is also not an exception. There are a number of limitations that have been mentioned as follows:

The responses have been collected from three cities of Punjab by taking 500 respondents. The attitudes while providing responses in Punjab may differ from those of the rest of India. However, a more extensive geological sample may produce some other results. Further, authors may use a larger sample size to find out other factors affecting the satisfaction of tourists in online tourism. Any primary data based study carried through a pre-designed questionnaire suffers from the basic limitation of possibility of difference between what is recorded and what is truth, no matter how carefully the questionnaire was filled. The present study may also suffer from this limitation because the people might not have deliberately reported their true opinion due to some biasness.Impact of demographic and psychological variables on tourists’ satisfaction has not been studied in the present study. Further study can be conducted to check the impact of demographic and psychological variables on tourists’ satisfaction. Impact of customers’ satisfaction on loyalty has not been examined. Further, researcher may empirically examine the relationship between e -service quality, customer loyalty and behaviour intention.

Implications and Recommendations of the Study The study may be helpful for tourism service providers to understand those factors which are perceived important by tourists while they use online mode of booking.

Results of the study show that dimension “Security” is the best predictor of tourists’ satisfaction. So tourism service providers need to awaken to this empirical fact and should take quick steps for the security and privacy of tourists.

Managers of tourism companies should assign explicit space for reinforcement and seclusion guidelines.

It is also depicted from results that tourists’ satisfaction is also influenced by website design meaning hereby that online clients are greatly apprehensive about the functionality and usability of tourism websites; therefore the study will help tourism web site designers to understand the requirements of the tourists while framing and maintaining their websites.

The website’s textual richness, good quality and updated information help to improve the effectiveness of tourism websites. Online travel companies should also keep this point in mind while designing their websites.

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The dimension “Reliability” was also found as significant variable affecting tourist satisfaction therefore; tourism service providers should try to provide trustworthy services in order to retain the customers.

Apart from it, tourism website developers need to focus on each and every factor which is important for them to survive in this tech savvy world.

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MJ SSIM IX(I), 4, 2017

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SIVA SIVANI INSTITUTE OF MANAGEMENTS.P Sampathy’s Siva Sivani Institute of Management is promoted by the Siva Sivani Group of Educational Institutions, which has been running the prestigious and internationally renowned Siva Sivani Public Schools for more than four decades. Approved by the All India Council for Technical Education, Ministry of Human Resource Development, Government of India, New Delhi, Siva Sivani Institute of Management started functioning as an autonomous institute in 1992.

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SuGyaan 63

Documents

SuGyaan 1 - ssim.ac.in · towards Online Grocery Shopping in India”, by Krishan Kumar Boora & Kiran examined the planned behavior of consumers towards online grocery shopping in