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Int. J. Business Innovation and Research, Vol. 9, No. 1, 2015 81
Copyright © 2015 Inderscience Enterprises Ltd.
Effectiveness of electronic service dimensions on consumers’ electronic buying behaviour and exploration of different groups
Anil Kumar* and Manoj Kumar Dash Department of Management Studies, ABV-Indian Institute of Information Technology and Management, Room No. 116, Block-C, Morena Link Road, Gwalior (M.P.), India Email: anilror@gmail.com Email: manojdash@iiitm.ac.in *Corresponding author
Abstract: With the rapid growth of the internet and the high market potential of electronic commerce in India, more and more companies engage their businesses online. The prosperity of e-market denotes that consumers have more and better service quality when they buy than before. But in this competitive electronic era, to understand the unknown mindset of consumer is a very hard and challenging task for electronic service providers. This study specifically focuses on exploring the difference of electronic service quality dimensions across age and gender groups (n = 412) and also analysed its effectiveness. ANOVA and multivariate regression techniques are used for compliance on the objective of study. Unique findings indicate that gender and age play an important role in determining their attitude towards electronic buying. To understand the values of existing and potential customers, this research contributes to marketing research literature by testing the effect of the e-services quality on consumer electronic buying behaviour. Business innovation and timely research on consumers’ behaviour is required while targeting people to serve through electronic and to develop further marketing mix strategies to converts potential customers into active ones. With theoretical contributions, it recommends ways for electronic service provider to enhance their performance.
Keywords: determine; business innovation; electronic commerce; prosperity; performance; research.
Reference to this paper should be made as follows: Kumar, A. and Dash, M.K. (2015) ‘Effectiveness of electronic service dimensions on consumers’ electronic buying behaviour and exploration of different groups’, Int. J. Business Innovation and Research, Vol. 9, No. 1, pp.81–99.
Biographical notes: Anil Kumar is a Full-Time Research Scholar in the Department of Management Studies at the ABV-Indian Institute of Information Technology and Management, Gwalior (M.P.), India. He holds an MSc (Mathematics), and MBA (Marketing) with two years of teaching experience and is currently pursuing his PhD in the area of econometrics modelling and fuzzy optimisation for consumer’s electronic buying behaviour.
Manoj Kumar Dash is currently an Assistant Professor in the Department of Management Studies at the ABV-Indian Institute of Information Technology and Management, Gwalior (M.P.), India. He earned his MA, MPhil, PhD and
82 A. Kumar and M.K. Dash
MBA in Marketing from the Berhampur University, Berhampur (Orissa). He has published more than 53 research papers in various journals of international and national repute. He is the author of three books and has edited five books. He was involved as chair/member in international conference of arts and science held at Harvard University, Boston (USA). His areas of research are: marketing science, consumer behaviour, econometrics modelling in marketing, optimisation and stochastic modelling in marketing, and marketing of financial product and service.
1 Introduction
With the coming of the 21st century, we have entered an e-generation era. The internet has generated a tremendous level of excitement through its involvement with all kinds of businesses starting from e-commerce, e-business, e-CRM, e-supply chain, e-marketplace, e-payment, e-entertainment, e-ticketing, e-learning, to e-citizen or e-government. The internet has opened a window of opportunity to almost anyone because of its ability to make viable the conduct of business in cyberspace or by connecting people worldwide without geographical limitations and provided new opportunities for marketers by offering them innovative ways to promote, communicate and distribute products and information to their target consumers (Thaw et al., 2012). Consumers can order goods and services virtually anywhere, 24 hours a day, seven days a week without worrying about store hours, time zones, or traffic jams (Li and Gery, 2011). The simultaneous and rapid rate of consumer adoption of personal computers and network systems have encouraged and pressured marketers to provide internet retailing sites (Michael, 2010). In the post-liberalisation period, with increase in gross domestic product (GDP), rising per capita income and proliferation of brands, there has been a change in Indian consumers’ consumption pattern, shopping behaviour (Mukherjee et al., 2011). The disposable income of the middle class is rising and the emphasis is more on spending than saving (Dahiya, 2012). In 2007, India was ranked the 12th largest consumer market and it is expected to be the fifth largest consumer market by 2025 after the USA, Japan, China and the UK (McKinsey and Company, 2007). In the post-liberalisation period, the number of rich and middle income Indian consumers has increased, with a corresponding fall in the number of people below the poverty line. Between 2001 and 2010, the rich consumer class increased by 21.4%, while the middle class increased by 12.9% (Shukla, 2010). With a growing middle class, rising GDP and disposable incomes, there is a notable change in the spending pattern of Indian consumers and emphasis is more on spending than saving (Dahiya, 2012).
With the prosperity of e-market in India, purchasing through electronic is increasing day by day because of updating of technology, busy schedule of human being and changing purchasing behaviour (Muhammad et al., 2012; Thaw et al., 2012). Despite India’s improved infrastructure but lack of understanding about consumer behaviour still remains a major obstacle to market cultivation. The significant of this study is to helps companies or manufacturer in developing their business, to understand the consumer electronic behaviour, their needs and expectations. Understanding about electronic purchasing behaviour of consumers could help marketers to find better ways of communicating with their consumers and to guide marketing mix decisions and put some
Effectiveness of electronic service dimensions on consumers’ 83
innovation in their service to satisfied electronic consumer. This study also guides the marketers to achieve the market demand, understand the factors which force consumers on buying behaviour, and given the opportunities to marketers to make an innovativeness and quality on their product and to improve their product-based research and development. In fact, due to a rise in income, increased awareness about products and proliferation of choices, Indian’s consumer will become pickier with his purchases (Akroush, 2012). Product, positioning and packaging innovation will be the key for companies to attract this new consumer (Tipu, 2012). Companies will have to drive innovation differently for different regions and consumer classes (Hemalatha et al., 2009). Market researchers, therefore, need to examine carefully the relationship between product and marketing innovation, and other important variables that make the introduction of a product successful in new markets (Haverila, 2011; Dershin, 2010; Bikramjit, 2010). Previous studies have focused predominantly on web advertising rather than the fundamental issues relating to why consumers make a decision to buy products on the internet (Korgaonkar and Wolin, 1999). These studies mainly investigated internet user demographics, reasons for shopping online, respondent’s preferred items when buying online, and satisfaction or dissatisfaction with online shopping. If electronic service providers know the e-service quality (e-SQ) factors affecting consumer buying behaviour and the associations between these factors and type of online buyers, then they can further develop their marketing mix strategies to converts potential customers into active ones.
Consumers purchase decisions are a very and greatly influenced by their attitudes, behavioural intention, local environmental security perception and their e-satisfaction (Al-Hawary, 2013). The e-SQ of the e-marketer plays an important role for the success of their retailing market (Bikramjit, 2010). The perception on service quality in electronic marketing among customers depends upon their level of expectation from the services providers. Customers of different origins differ in the perception on some quality factors of online marketing which rest on their perceived social values, security concern, personal trust, disposition attitude and overall culture (Barness et al., 2003). Improvement service quality according to the need of customer is very important for every vender (Michael, 2010). This study gives insight how the services quality online factors influence the customer’s willingness to buy in Indian online market. The main objectives of the study are to identify the main e-service dimensions which are impact on consumers’ electronic buying behaviour and their e-satisfaction.
This paper is classified into six sections. Section 1 explained about the introduction of the study. A review of the relevant literature is explained in Section 2. Section 3 explains about adopted methodology in the study. Section 4 described the analysis part of the study. Major findings of research are given in Section 5. This is followed by Section 6, which discussed the conclusion.
2 Literature review
With the increase of e-service adoption in business field, the importance of measuring and monitoring e-SQ in the virtual world has been recognised. The rise of internet-based services has changed the way that firms and consumers interact (Springer, 2013). Dabholkar (1996) conducted a research work on the dimensions of e-SQ focusing on website design and he argued that seven dimensions of e-SQ can be illustrated as the
84 A. Kumar and M.K. Dash
basic parameters in the judgement of e-SQ, including website design, reliability, delivery, ease of use, enjoyment and control. Later on, Yoo and Donthu (2001) developed a four-item SITEQUAL scale focusing mainly on website characteristics and Zeithaml et al. (2002) developed an e-SQ measure, consisting of five dimensions:
1 information availability
2 ease of use
3 privacy/security
4 graphic style
5 reliability.
An e-SQ scale called WEBQUAL, which is composed of 12 dimensions developed by Loiacono et al. (2002). There is growing recognition of different variability in the outcome of e-SQ studies in terms of the quality dimensions (Waite, 2006; Kim et al., 2006). Madu and Madu (2002) developed a 15 dimensions scale of e-SQ based on better understanding of customer and providing services to meet the needs and expectations of customers. Santos (2003) argued that both active and in cub active dimensions are important in e-SQ and both of the dimensions should be taken into account in e-SQ assessment. An 11 sub dimensions scale is put forward based on the two dimensions of e-SQ (Santos, 2003). To gain the satisfaction of customer is the main motive and final objective of the organisation (Kumar, 2012). Yang and Fang (2004) further examined the differentiation of dimensions to online service satisfaction and dissatisfaction. They suggested that there are four salient quality dimensions leading to both satisfaction and dissatisfaction: responsiveness, reliability, ease of use and competence. Gounaris et al. (2005) argued that the dimensions of perceived e-SQ are influenced by different antecedents. Parasuraman et al. (2005) carried out a study on internet service quality based on their earlier research on service quality in the traditional distribution channels, and developed an ESQUAL scale based on the seven dimensions. The ESQUAL scale comprises 11 dimensions in eservice quality, and later Parasuraman et al. (2005) developed the ESQUAL into to a seven dimensions scale. Kim et al. (2006) extend the dimensions developed by Parasuraman et al. (2005) into a nine dimensions scale in eservice quality in order to use them for content analysis and evaluation of websites in the apparel retailing sector (Kim et al., 2006). Sohn and Tadisina (2008) put forward a six dimension model for e-SQ assessment based on their empirical study in internet-based financial institutions. Cristobal et al. (2007) suggested that the perceived quality of a website or the degree of customer’s satisfaction to a website is especially relevant to customer’s loyalty to a website, and propose a four dimensions scale of e-SQ based on customer’s satisfaction and website loyalty.
A quality model for electronic services which includes three (environment quality, delivery quality, outcome quality), dimensions and nine sub-dimensions (graphic quality, clarity of layout, attractiveness of selection, information quality, ease of use, technical quality, reliability, functional benefit, emotional benefit) is developed by Fassnacht and Koese (2006) and the proposed model is rigorously tested by means of a large aggregated sample drawn from the customer bases of three different electronic services for the conceptualisation. Online service delivery is very different from traditional service delivery. Information provided by or collected from customers can be gathered and analysed by the e-service provider and used as the basis for the customisation of the
Effectiveness of electronic service dimensions on consumers’ 85
service that the organisation offers to the customer (Rowley, 2006). Sohn and Tadisina (2008) used different e-SQ dimensions (trust, speed of delivery, reliability, ease of use, customised communication, website content and functionality) on online financial services. With rapid growth of the internet and the globalisation of market, companies accepted and adopted the new information and communication technology to offer e-services to their customers. The online environment is dynamic and website attributes can impact on customer satisfaction (Dholakia and Zhao, 2010). Sahadev and Purani (2008) liked four components of e-SQ: efficiency, fulfilment, system availability, privacy, with trust and satisfaction of the customers. A proposed scale for measuring e-SQ an eight-dimension scale for measuring e-SQ with rewording and modifying of the SERVQUAL instrument: website design, reliability, fulfilment, security, responsiveness, personalisation, information and empathy developed by Li and Suomi (2009). By conducting exploratory factor analysis and structural equation modelling, Swaid and Wigand (2009) found that e-SQ is measured on six dimensions: information quality, website usability, reliability, responsiveness, assurance and personalisation. Furthermore, the study identifies the influence of the individual dimension of e-SQ on the different types of service loyalty. Structural analysis reveals that assurance is the most important factor affecting ‘price tolerance’, while reliability is the factor with the greatest influence on ‘preference loyalty’. The dimension of responsiveness is the only one having significant negative impact on ‘complaining behaviour’. Online retailers are provided with tactical strategies on how to immunise online shoppers’ loyalty against switching behaviour and price sensitivity. The refined scale of five dimensions (customer service, web design, assurance, preferential treatment, and information provision) with 70 items in the measurement scale for measuring the service quality of internet banking was identified by Ho and Lin (2010) to improve customer satisfaction, build customer trust and create loyal customers. A four-factor solution (E-ServQual) represented by ‘personal needs’, ‘site organisation’, ‘user-friendliness’ and ‘efficiency’ was found by Herington and Weaven (2009) to predict the overall customer satisfaction with banking performance, Rolland and Freeman (2010) defined ‘e-tail SQ’, a 15-item scale to measure five key user values (labelled ease of use, information content, fulfilment reliability, security/privacy and post-purchase customer service) and scale items derived from French data are found to be similar to those identified in previous international studies and gave practical implications – ‘e-tail SQ’ can help online retailers in the French marketplace to measure service quality delivered, and thereby to improve it, and may be transferable to other national markets. Table 1 described the used sample items of e-SQ dimensions of our research. Table 1 E-SQ dimensions scaling
Dimension Sample items Sample support references Website design 1 Fast presentation Dabholkar (1996),
Yoo and Donthu (2001), Zeithaml et al. (2002),
Fassnacht and Koese (2006) and Sahadev and Purani
(2008)
2 Navigation structure 3 Provide up to date information about 4 Easy to find products in this sites 5 Web contents 6 Search facilities 7 Web appearance
86 A. Kumar and M.K. Dash
Table 1 E-SQ dimensions scaling (continued)
Dimension Sample items Sample support references
Responsiveness 1 Prompt response service Madu and Madu (2002) and Parasuraman et al. (2005) 2 The company quickly resolves problems
3 I encounter willing to help customer any time
4 Convenient option for returning items 5 After sales services; prompt dispatch
and delivery of products 6 This site has customer service
Assurance 1 Trustworthiness Zeithaml et al. (2002) and Madu and Madu (2002) 2 Accessibility
Empathy 1 Address complaints friendly Madu and Madu (2002) and Parasuraman et al. (2005) 2 Consistent courteous
3 Personal attention 4 Understand your specific needs
Reliability 1 Accurate delivery service Zeithaml et al. (2002) and Lee and Lin (2005) 2 Transactions with this site are error-free
3 Order tracking functions 4 Provides its services at the time 5 Compensates policy
Privacy 1 It protects information about my web-shopping behaviour
Parasuraman et al. (2005)
2 Not share personal information
Online services are now an integral part of most organisations. Their online presence is for extra valued added features to serve their customers better, however, many of them are not aware of how to make their online presence more attractive to their valued customers such as meeting their needs and having a strategic plan to retain customers using their websites (Behjati et al., 2012). Evanschitzky et al. (2004) argued that the most obvious difference between traditional and electronic retail services is the replacement of human-to-human interaction with human-to-machine interaction and therefore, new or modified approaches to conceptualising and measuring satisfaction may be needed for e-commerce settings. Accordingly, e-satisfaction can be defined as the overall affective evaluation a user has regarding his or her experience related with the website. For success in e-commerce, retailers need to provide high quality websites that attract and retain shoppers (Kim and Lee, 2006; Loiacono et al., 2002). User satisfaction is an important predictor of online consumer behaviour and the success of a web-based system. If customers are satisfied with the services received through the online system, it is likely they will keep using the system. On the other hand, if customers get frustrated and dissatisfied with the online system, they would be unlikely to come back for a visit (Xiao and Dasgupta, 2005). Understanding of what creates a satisfying customer experience becomes crucial for online-stores success in attracting more customers to purchase through online (Rabiei et al., 2011; Kumar, 2012).
Effectiveness of electronic service dimensions on consumers’ 87
The ability to measure the level of customer satisfaction with electronic purchasing is essential in gauging the success and failure of e-commerce. To do so, internet businesses must be able to determine and understand the values of their existing and potential customers (Schaupp and Langer, 2005). It is, therefore, crucial for any e-service provider manager to keep a close watch on customer satisfaction, customer loyalty and the customer’s intention to recommend the company (Kumar and Rathee, 2012). Therefore, this study is significant to provide a deep understanding about the affecting factors on e-satisfaction of consumer in Indian market and that could help marketers to find better ways of communicating with their consumers, for improving the electronic service quality and to guide marketing mix decisions.
3 Methodology
3.1 Sample and data collection
Students from Indian renowned institutes: National Institute of Technology (NITs) and Indian Institute of Information Technology and Management (IIITMs) are used as the subject in our study. They are experienced and regular users of the internet, representing the most appropriate population of e-commerce user for e-commerce research. Drennan et al. (2006) argued that university students are representative of a dominant cohort of online users. In the survey, items of variables are developed by adapting existing measures to the research context. All items are scored on a five-point Likert-type scale ranging from (1) strongly disagree to (5) strongly agree. The survey questionnaire consisted of two sections. In the first section, respondents were asked to answer questions about basic information, such as the gender, education level, and so on. The second section consists of the questions measuring variables. Respondents answered the questions in second section based on the web that they chose in the end of the first section. We give out 400 survey questionnaires in NITs Library. Only 300 questionnaires have been taken back and we collected 250 from Indian Institute of Information Technology. We ruled out the questionnaires that were conducted incompletely and eliminate the questionnaires with no online purchase experiences. We screened out 412 valid questionnaires as our research sample. Details of the respondents such as age, gender, and education are depicted in Table 2. Table 2 Demographic characteristics of respondents
Number of respondents Percentage (%) Age Less than 20 150 36.4 21–35 220 53.3 Above 35 42 10.3 Gender Male 292 70.8 Female 120 29.2 Education Under graduation 250 60.7 Under post-graduation 120 29.1 Others 42 10.2
88 A. Kumar and M.K. Dash
3.2 Normality and multi-collinearity of data
This research involves a relatively large sample (n = 412) and therefore, the Central Limit Theorem could be applied and hence there is no question on normality of the data. Tolerance test and variance inflation factor (VIF) (Kleinbaum et al., 1988) methods were utilised in order to determine the presence of multi-collinearity among independent variables. As can be seen from this data,
1 none of the tolerance levels is less than or equal to .01
2 all VIF values are well below 10 (Table 3).
The acceptable Durbin-Watson range is between 1.5 and 2.5. In this analysis Durbin-Watson value of 1.543, which is between the acceptable ranges, shows that there is no auto correlation problems in the data used in this research. Thus, the measures selected for assessing independent variables in this study do not reach levels indicating multi-collinearity. Table 3 Test of collinearity
Constructs Tolerance VIF
Website design .547 1.552
Responsiveness .699 1.512
Reliability .501 1.764
Empathy .534 1.577
Assurance .700 1.766
Privacy .672 1.247
3.3 Reliability and validity of the data
The Cronbach’s alpha coefficients are computed to quantify the scale reliabilities of the indentified factors and to make comparisons in constructs. The Cronbach’s alphas of website design, responsiveness, reliability, empathy, assurance and privacy are 0.785, 0.737, 0.772, 0.874, 0.785, and 0.851 respectively. All of these Cronbach’s alphas are > 0.65, higher than the minimum cut off score of 0.60 (Nunnally, 1978), 0.65 (Lee and Kim, 1999), or 0.70 (Nunnally and Bernstein, 1994). We conclude that all latent variables have adequate reliabilities. The Kaiser-Meyer-Olkin (KMO) measure of sampling and Bartlett’s of sphericity and value varies between 0 and 1 and a value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors (Field, 2000). Field (2000) recommended accepting values greater than 0.5 as acceptable. The value of KMO is > 0.50 for each construct and the significance of Bartlett’s test is less than 0.001 (Table 4). KMO and Bartlett’s test indicates that all the factors are suitable for factor analysis. Varimax rotation method used to rotate factors. The rotated component matrix indicates that all the indicator items loaded very high (> 0.681) on their respective factors and below 0.40 all the other factors, suggesting good convergent validity and discriminant validity for each latent variable.
Effectiveness of electronic service dimensions on consumers’ 89
Table 4 Factor and communalities results
Factor Item Variable loading Communalities KMO Barlett’s test sig.
Website design WS1 0.756 0.836 0.702, p = .000 WS2 0.735 0.721 WS3 0.765 0.717 WS4 0.944 0.916 WS5 0.832 0.916 WS6 0.736 0.933 WS7 0.725 0.905
Responsiveness RS1 0.651 0.831 0.846, p = .000 RS2 0.849 0.823 RS3 0.649 0.926 RS4 0.845 0.923 RS5 0.959 0.922 RS6 0.825 0.888
Reliability RE1 0.794 0.877 0.692, p = .000 RE2 0.684 0.823 RE3 0.870 0.862 RE4 0.841 0.749
Empathy Em1 0.824 0.958 0.773, p = .000 Em2 0.821 0.957 Em4 0.821 0.958 Em5 0.889 0.942
Assurance As1 0.762 0.827 0.650, p = .000 As2 0.752 0.849
Privacy P1 0.756 0.741 0.708, p = .000 P1 0.686 0.716
4 Empirical analysis
4.1 Comparison of factors of e-SQ dimensions on the basis of gender and age
To compare variance and means of factor used one way ANOVA. The one-way classification refers to the comparison of the means of several (univariate) populations. Considering an experiment having m treatment groups of or m different levels of a single factor A. one supposes ni observations have been made at the i – the level giving a total of
1
m ii
N n =
= ∑ observations. If yij is the observed score corresponding to the jth observation at the ith level or treatment group, the analysis of variance model for such an experiment is given as:
( 1, 2, ; 1, 2, , )ij i ij iy μ e i m j n= + + = =… …α (1)
90 A. Kumar and M.K. Dash
where μ (μ – real number) a common is (or general) mean common to all the observations, αi is the special effect due to the ith level of the considered factor and eij is the realisation of the random error associated with the jth observations, at the ith level or
treatment group. Without of generality one assumes 1
0 m
i ii n
= =∑ α (in the other case one
transform 1
m i ii
μ n μ =
+ →∑ α )). The 1 2( )mN n n n= + + + observed or measured values yij are considered as realisations of mathematical samples 111 12 1( , , , ),nY Y Y…
221 22 2 ( , , , ) ,nY Y Y… …… 1 2( , , , ).mm m m nY Y Y… Thus, (1) implies,
( 1, 2, ; 1, 2, , )ij i ij iY μ E i m j n= + + = =… …α (2)
where μ and αi (i = 1, ……, m; j = 1, …, ni) are (generally unknown) real parameters. Moreover, it is assumed that Eij are random normally distributed independent variables with the expected value E(Eij) = 0 (that means, the random influence is a non-systematic one) and the variance var(Eij – σ
2 (σ >) (that means, the variability of the random influences is constant). With these propositions the model (2) has the following matrix structure.
TY X E= +β (3)
Using the least squares method, that means, 2
min 1TY X m ∈
− → + β
β R
(4)
Or components-wise,
( ) 2 1
2
1 1 , , mini
m
m n ij ii j μ
Y μ = = ∈
− − →∑ ∑ …α α
α R
(5)
With the condition 1
0 m
i ii n
= =∑ α one obtains the following estimates 1ˆ ˆˆ, mμ …α α of μ,
α1 …, αm respectively:
1. ..
1. ..
ˆ .. ˆ
ˆ
μ Y Y Y
Y Y
= = −
= −
1α
α
(6)
With
.. 1 1
1 im n iji j
Y Y N = =
= ∑ ∑ (7)
. 1
1 ( 1, 2 ).i
n i ijj
i Y Y i m
n = = =∑ ……
For testing the hypothesis,
Effectiveness of electronic service dimensions on consumers’ 91
1 2 3: 0o mH = = =…α α α α (8)
We uses as a (test) statistic. If the hypothesis Ho is true, then the statistic is F-distributed with (m – 1, N – M) degree of freedom. That means the identity of the m treatment means α1, α2, α3, … αm can be statistically verified by a test based on the comparison of the mean square between the treatment groups and the means square between the treatment groups and the means square within the treatment groups. The first ANOVA test was run on the sample to understand whether there existed any significant difference of male and female respondents about e-service dimensions. Table 5 Comparison of factors of e-SQ dimensions on the basis of gender
E-service dimensions Sum of squares df Mean square F Sig.
Website design Between groups 2.710 1 2.710 2.779 .086
Within groups 498.411 411 .975
Total 501.121 412
Responsiveness Between groups 5.105 1 5.105 5.005 .016
Within groups 521.241 411 1.020
Total 526.346 412
Reliability Between groups 3.311 1 3.311 3.841 .041
Within groups 440.434 411 .862
Total 443.745 412
Empathy Between groups 5.597 1 5.597 4.616 .032
Within groups 619.579 411 1.212
Total 625.176 412
Assurance Between groups .610 1 .610 1.010 .215
Within groups 308.547 411 .604
Total 309.157 412
Security/privacy Between groups 4.650 1 4.650 6.857 .009
Within groups 346.550 411 .678
Total 351.201 412
The second ANOVA test was run on the sample to understand whether there is any significant difference on the age basis of respondents about e-service dimensions and results shown in Table 6.
The ANOVA results show that there is no significant differences in all the six factors expect assurance of e-service dimensions of the customers on the basis of their gender (Table 5) but the factor assurance is significant differences (F(1,411) = 1.010, P > 0.05) on the basis of their gender. The same results the researches got that there is no significant differences in all the six factors expect assurance of e-service dimensions of the customers on the basis of their age (Table 5) but the factor assurance is significant differences (F(3,409) = .939, P > 0.05) on the basis of their age factor.
92 A. Kumar and M.K. Dash
Table 6 Comparison of factors of e-SQ dimensions on the basis of age
E-service dimensions Sum of squares df Mean square F Sig.
Website design Between groups 8.278 3 2.759 2.850 .027 Within groups 492.843 409 .968 Total 501.121 412
Responsiveness Between groups 15.951 3 5.317 5.302 .001 Within groups 510.395 409 1.003 Total 526.346 412
Reliability Between groups 11.001 3 3.667 4.313 .005 Within groups 432.744 409 .850 Total 443.745 412
Empathy Between groups 14.849 3 4.950 4.128 .007 Within groups 610.327 409 1.199 Total 625.176 412
Assurance Between groups 1.702 3 .567 .939 .322 Within groups 307.456 409 .604 Total 309.157 412
Security/privacy Between groups 14.151 3 4.717 7.124 .000 Within groups 337.049 409 .662 Total 351.201 412
4.2 Correlations and multiple regression analysis
Correlation is a statistical method used for measuring or describing the relationship between two variables. Finding correlations among variables is essential, yet it cannot be described as a relationship between cause and effect. The information given can only be taken as an indicator (Dimitriadi, 2000). Correlations among the six factors are presented in Table 7. Table 7 Correlations within factors
Website design Responsiveness Reliability Empathy Assurance Privacy
Website design 1 .236** .162** .167** .478** .138** Responsiveness .236** 1 .232** .538** .240** .189** Reliability .162** .532** 1 .591** .279** .152** Empathy .167** .138** .591** 1 .184** .415** Assurance .478** .240** .279** .184** 1 160** Privacy .138** .189** .152** .215** .160** 1
Note: **Correlation is significant at the 0.01 level.
From the above it is safe to say that there is a significant correlation between each other in the e-service dimensions. As mentioned before, correlation analysis cannot be described as a relationship between cause and effect (Dimitriadi, 2000). To overcome this
Effectiveness of electronic service dimensions on consumers’ 93
limitation linear multiple regressions was employed to describe the association among the factors and to form a mathematic model.
In order to determine mathematical model of multiple regression and to test it, in case of researching relationship between two phenomena and in case of prediction of the value of dependent variable, first we are going to identify variables and then to find out random sample n-size for the chosen values of dependent variables. Suppose that k phenomenon is identified as independent variable (predictor), or Xi, i = 1, 2 … k and Y as dependent random variable. The whole multiple linear model can be presented as one equation for dependent variable Yi:
0 1 1 2 2 3 3i k k iY x x x x ε= + + + + + +β β β β β (9)
where
Yi dependent random variable
x1, x2, x3, x4, …, xk are values of independent
β0, β1, β2, β3, … βk model parameters (regression coefficient)
εi a supporting element or a random error which has normal distribution, zero mean and constant variance.
Multiple linear regression model (9) consists of two parts: determined ( )iY ′
0 1 1 2 2 3 3i k k iY x x x x ε′ = + + + + + +β β β β β (10)
Stochastic (εi), so that from (9) we can get:
i i iε Y Y ′= + (11)
Determined part of the linear regression model is an average value of dependent variable (Yi) for the given values of independent variables:
( ) 0 1 1 2 2 3 3i i k k iY E Y x x x x ε′ = = + + + + +β β β β β (12)
And other values of Yi show average values E(Yi). The whole regression model (9) was estimated by the sample regression model:
0 1 1 2 2 3 3ˆi k ky b b x b x b x b x= + + + + +… (13)
where we have ŷ adjustable or foreseen value of dependent variable Yi, x1, x2, x3, …, xk, are values of independent variables, b0, b1 … bk are estimations of unknown parameters β1, β2, β3, …, βk. We should choose the multiple linear regression model which presents in the most suitable way the relationship between observed phenomena. It can be achieved by minimising a sum of square equations of empirical points from the regression model (for example, regression plane when k = 2), or:
( )22 ˆ mini i ie Y Y= − =∑ ∑ (14) where ei is random error in sample. Multiple linear regression model as statistical model does not mean only mathematical expression but also assumptions which supply the
94 A. Kumar and M.K. Dash
optimal estimation of parameters β1, β2, β3, β4 …, βk. These assumptions are usually connected with random error:
• the random error has normal distribution
• it is equal zero (on the average)
• supporting elements have equal variances.
Generally, in multiple regression model we apply testing:
0 : 0 and : 0 (for 1, 2, , );i A iH H i k= ≠ = …β β
The test statistics i
i i
b
b t
S = has the student’s t-distribution with degrees of freedom
n – k – 1. We accept null hypothesis if:
/ 2it t< α
In our case, customer overall satisfaction about e-services is the dependent variable (Υ1: e-satisfaction) and website design (Xwsd), responsiveness (Xres), reliability (Xrel), empathy (Xemp), assurance (Xass.) and privacy (Xs/p) are the independent variables. The mathematical model for equation (9) is:
1 0 1 2 . 3 . 4 . 5 . 6 /wsd res rel emp assu s pY b b X b X b X b X b X b X= + + + + + + (15)
Table 8 Multiple regression results
Model Unstandardised
coefficients Standardised coefficients t Sig.
B Std. error Beta (Constant) –.237 .084 - 6.200 .001** Website design 1.0136 .028 .835 20.297 .000*** Responsiveness .433 .023 .297 6.987 .000*** Reliability .254 .031 .142 2.939 .023* Empathy .239 .025 .136 2..932 .006** Assurance –.280 .037 –.236 –11.196 .000*** Privacy .212 .029 .057 3.852 .000***
Notes: R2 = 0.811, Adjusted R2 = 0.462, (*) p < .05, (**) p <.01, (***) p < .001. Dependent variable: over satisfaction about e-SQ dimensions.
Regression results are shown in Table 8. In Table 8 computed F-values and R2 are displayed to understand the overall significance of equation (15). All of the models yield significant p-values and R2 explained 82.8% of the variance in attitudes of overall satisfaction of customer toward e-service dimensions was explained.
Then, equation (9) comes out the following from with help of regression results.
1 . .
. /
.237 1.036 .433 .254
.239 .280 .212 wsd res rel
emp assu s p
Y X X X X X X
= + + + + + − +
(16)
Effectiveness of electronic service dimensions on consumers’ 95
5 Findings
E-SQ dimensions have been revealed as a key factor in search for sustainable competitive advantage, differentiation and excellence in the service sector. Customers’ evaluations of the e-service quality are critical to service firms that aim to improve their marketing strategies so accurate measurement of e-services quality that a major concern to management. But, whereas measuring criteria of e-services quality and satisfaction of customer are fuzzy and ambiguous but available methods measuring them generally is classic kind. So, being a manger of any e-service provider company that it is necessity to design appropriate and suitable model about e-SQ dimensions, which can directly impact on customers’ intention and their satisfaction so that customer feel more satisfied and more attract to transact with e-service provider. All selected e-services dimensions are significant and positive impact on overall e-satisfaction of customer but the assurance dimension of e-service has significantly but negative impact on customers’ e-satisfaction. Vendor should think about this factor to enhance their performance in this competitive environment and should try to improve other dimensions as well. In analysis of variance on the basis of age and gender, the factor assurance also not significant, on basis of this analysis and after doing generalisability of our e-vendor should think all factor of e-SQ but assurance should take seriously so that trust of customer should increase and customer intension and retention will increase. Vendor should identify the reasons of negative impact of assurance factor of customers’ satisfaction and should start work on it so that customer satisfaction will increase. All βi of e-SQ dimensions are not equal to zero and satisfied the given hypothesis, but some dimensions have positive as well as negative impact on customers’ e-satisfaction. Website design and empathy are support on our hypotheses means that there is positive correlation between all these electronic service quality dimensions and directly impact on customers buying behaviour but only one dimension (assurance) in our study not support our hypotheses means that customers’ are not happy with responsiveness of e-service vender. Vender should think about this dimension carefully so that purchase intention of customer about their service not harm. The further research can be done on the basis of assurance factors’ dimensions to improve and enhance the electronic service quality; after getting the feedback from customers, some innovation can be done on this particular dimension. Secondly there is a scope for a comparative study analysis to measure the effectiveness as well as their e-satisfaction.
6 Conclusions
Innovation is of vital interest to companies and countries across the globe and much has been written about techniques to stimulate and manage the process. The adoption of business innovations and other B2B e-commerce applications and technologies have a number of distinct benefits for an organisation. Most industries face continuing pressures from rising R&D costs, shortening product lifecycles and global competition. These challenges have increased the focus on shortening development times, which again puts pressure on the efficiency of front end innovation. The research gives an idea to service provider that before providing any new products/services innovation we must understand electronic behaviour of consumer so that we can manage our resources and can provide better services to our customer. This study provides useful managerial implication for
96 A. Kumar and M.K. Dash
managers to investigate what leads their customers to be loyal and then determine the component of their loyalty and the role of electronic services to become them loyal. Second, managers also should keep in touch with the customers, segmenting customers by their purchase habits, and hiring service-oriented employees. E-commerce embraces opportunities for the business growth. The adoption of e-commerce significantly involves a consumer to make judgements in an environment of critical conflicting forces, namely security, privacy, trust and risk concerns. However, it brings many challenges for the usage and implementation of this technology. These challenges have a significant impact on business success. E-service dimensions have a vital role in the process of customer’s choice of product through electronic and considers a main mean utilised to link the producer with the customer. The effects of service quality and satisfaction can build the customers’ loyalty and their retention. Customer retention is a buzzword in all types of services and different groups of consumers believe that different electronic services dimensions are important. Therefore, electronic services dimensions appear to be a promising market segmentation criterion and the drivers of commitment. Vender should think about this dimension carefully so that purchase intention of customer about their service not harm. The further research can be done on the basis of assurance factors’ dimensions to improve and enhance the electronic service quality; after getting the feedback from customers, some innovation can be done on this particular dimension. Secondly, there is a scope for a comparative study analysis to measure the effectiveness as well as their e-satisfaction. Our sample size is limited; it can be increased to generalise the result.
Acknowledgements
The authors want to extend their gratitude towards the editor, Prof. Angappa Gunasekaran and the anonymous reviewers’ for their indispensable suggestions and comments that improved the quality of the paper significantly.
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