Internet usage is rapidly growing in urban areas like Cosmopolitan cities, semi-urban areas in India. I-enabled services by governmental agencies, educational institutions and commercial sector force users of these services to seek superior internet access like broadband, WiMax in the place of traditional dial-up access. With the reforms in telecom sector in India, many private players started internet services affecting monopolistic public sector telecom giant BSNL (Bharat Sanchar Nigam Limited). With the advent of private ISPs, the perception and brand choice of broadband consumers are witnessing dynamic shift in favor of private players. However, BSNL is still preferred ISP in the minds of Indian broadband users. Cost competitiveness, transparency, paradigm shift in consumer responsiveness etc weigh in favor of the telecom PSU BSNL. This paper attempts to identify the factors affecting brand perceptions of broadband service. Further, it studies the cause and effect, mediating effects of consumer brand perceptions and conceptualizes a mediating-mediating model to capture these effects. The results suggest that consumer adoption of broadband service is affecting the brand perceptions measured in terms of satisfaction.

Keywords: Broadband, Adoption, Normative constructs, mediating.


Worldwide consumer adoption of broadband internet services continues exponentially. Broadband internet penetration is more than 55% in Japan and Korea and 53.6% in United States in the year 2004 (Venkata Praveen, Fotios Harmantzis 2006). According to Internet World Stats, an online internet usage portal, it is only 0.2% in India in 2007. There are four categories of broadband users: 1) Asymmetrical Digital Subscriber Line (ASDL) 2) Cable TV Internet 3) Fiber-to-home 4) Wireless Mobile Internet. But ASDL internet access and Wireless Mobile internet access are popular in India. The broadband penetration is expected to go up with impending WiMax broadband in India. The markets for broadband that remain untapped are rural, high cost, low-income and high risk. Serving these markets has required large investments that have not been forthcoming from the private sector (Francis Porenza 2007). The deployment focus now is shifting towards broadband access services and in this regard, the Government of India has taken a number of proactive steps to boost the penetration and deployment of advanced telecommunications infrastructure in all parts of the country (Abhay Karandikar, Zainul Chabriwala 2006). Government of India has taken numerous efforts to increase the tele-density 25 in the coming years. The Telecom Regulatory Authority of India (TRAI), through its working papers, is promoting broadband internet access and asked the service providers to diversify their operations to rural India. Till 2004, the broadband market in India is dominated by 2 telecom public sector companies viz., BSNL (Bharat Sanchar Nigam Limited) and MTNL (Mahanagar Telephone Nigam Limited). Their market shares are witnessing sharp decline due to entry of many private players like Reliance, Tata Teleservices, and Vodafone etc. The users are shifting from one player to another due to various reasons like cost, speed, quality of service etc.

Major beneficiaries of broadband internet services are health, education, transport and banking sectors. In India, BSNL is the dominant player in broadband segment of telecommunication. BSNL is a state owned telecom operator and is offering almost all the telecommunications services from basic telephony to video conferencing. Besides, it has sizeable market share in rural India. From a conservative telecom operator controlled by Government of India, it has transformed into more corporatized entity resulting improved delivery, quality of service. BSNL has presence in almost all urban areas except New Delhi and Mumbai. By 2006, BSNL had over 450,000 kilometers of fibre, compared to about 65,000 kilometers of its nearest rival, Reliance (Malik Payal 2008 chap 9).

Broadband user adoption is affected by attitudinal constructs (relative advantage, utilitarian outcomes, hedonic outcomes, social outcomes and service quality), which represent consumers' favorable or unfavorable evaluations of the behavior in question (i.e. adoption of broadband) (Brown and Venkatesh, 2005; Dwivedi & others 2007; Rogers, 1995; Venkatesh and Brown, 2001); (2) normative constructs (primary influence, work referents' influence and secondary sources' influence), which represent the perceived social pressure upon consumers to perform the behavior in question (i.e. adoption of broadband) (Brown and Venkatesh, 2005; Dwivedi and others 2007; Venkatesh and Brown, 2001), and (3) control constructs (knowledge, self-efficacy, perceived ease of use, perceived ease of subscribing broadband, cost, declining cost, facilitating conditions resources and perceived lack of needs), which represent the perceived control over the personal or external factors that may facilitate or constrain the behavioral performance of consumers (Brown and Venkatesh, 2005; Dwivedi and others, 2007; Rogers, 1995; Venkatesh and Brown, 2001).

In this paper, author attempts to develop a normative model which is tested using structural equation modeling technique. From prior studies, author developed a mediating-mediating model capturing various factors influencing broadband (BB) consumer satisfaction. It is found that expectation constructs has a significant mediatory role again mediated by adoption constructs leading to consumer satisfaction.


Consumer perceptions are affected by many factors. When it comes to brand choice, it is affected by social, utility and other factors. Economic theory has contributed to brand choice research. This contribution is reflected in rational choice theory that postulates consumers seek to maximize utility of their decision. Utility is maximized through consumers assigning a value to each product/service based on an assessment of each product/serviceability to satisfy needs and desires (Marshall, 1890; Alchian, 1953; Strotz, 1953). According to Jacoby, rational choice theory argues buyers do not choose randomly and that rationality is the only reasonable explanation for their reactions to changes in relative prices (Jacoby, 2001). Accepting this theory would lead to rejection of psychological factors including past purchase experiences, current expectations, motives, mood, personality, attitudes, values, beliefs, memory etc. Brown and Venkatesh, 2005; Dwivedi and others, 2007; Rogers, 1995; Venkatesh and Brown, 2001 have listed various constructs affecting broadband adoption of Indian consumers.

Behavioral Intention (intention to subscribe), Relative Advantage (BB superiority), Utilitarian Outcomes (access to information), Service Quality (Fault clearance, speed etc), Primary Influence (friends, family etc), Social Needs (Symbol of possession), perceived ease of use and many other factors (Dwivedi et al 2007, Rogers et al 1995, Venkatesh and Brown et al 2001 &2005) significantly influence consumer behavior. Demographics (e.g., gender, age, income, race, etc.) did not predict differences in online buying, although males spent slightly more than female online shoppers. However, both the Wharton Virtual Test Market and other studies (Kehoe, Pitkow, and Rogers 1998; Kraut et al. 1997) have found that demographics were an important indicator of who is on the Internet in the first place. In their paper, they argue that the different types of cognitive processing, changes of preferences, the focus of the need for identity and social networks are determinants leading to product choice and explaining variations in market dynamics (Janssen Marco, Jager Wander, 2001). Deighton et al., (1994) examined switching and repeat purchase effects of advertising in well established and frequently purchased product categories. They found that advertising works through attracting switchers but did little in modifying the repeat purchase probabilities of those who have just purchased the brand (Deighton et al., 1994). Gary Madden, Michel Simpson and Scott Savage (2002) found NMNL model used by them suggest 65 per cent of separate Australian households passed are likely to subscribe to broadband delivered entertainment service in the twenty first century.

Bowa & Shoemaker found sales promotions to have positive effects for new customers only, with the likelihood of existing customers purchasing their existing brand not increasing. However, BB services promotions have positive effects for both new and existing customers and results in customer retention/brand loyalty. Gerald R. Faulhaber and Christiaan Hogendorn (2000) found that competition in the provision of interactive broadband infrastructure to metropolitan area households is likely if the market is left unfettered. While this infrastructure market is clearly not perfectly competitive, it would appear that two or even three firms can offer fiber infrastructure at higher demand levels and survive. Strategic dynamic behavior takes the form of dissipating rents by increasing network size relative to the static model, which is an unambiguous gain for consumers. Fang-Mei Tseng (2006) listed upload speed, connection stability, usage fees, download speed, service quality of provider, static ip address, brand of service provider, and awareness of the provider as the influencing factors of consumer choice of broadband services in Taiwan. He concluded that if the usage fees of broadband services falls, the dialup users in Taiwan will switch over to ASDL or Cable internet. But the fixed ASDL users will less likely to switch over to other options. The "India's Broadband Economy: Vision 2010", a study conducted by IBM Business Consulting Service on behalf of Confederation of Indian Industry (CII) and Ministry of Communications & Information Technology (MCIT) indicates that the present value (2004) benefit for the Indian Economy due to the growth in Broadband is expected to be USD 90 Billion for the period 2010-2020, with an 11 percent additional growth in the labor productivity. It further says that it is expected to launch new business lines and increased efficiency in existing businesses, leading to direct employment of 1.8 million and total employment of 62 million by 2020 (Pavan Kumar 2007).

The own-price elasticity of broadband demand is statistically significant and has a substantial coefficient value. The cross-price sensitivity of broadband demand with respect to dialup price is also statistically significant, and supports the notion of the two services being substitutes (Kenneth Flamm, Anindya Chaudhuri 2005). Broadband has developed as a type of high-speed Internet access that supports the transmission of data at speeds far greater than traditional dial-up access in US. While common-carrier requirements have now been cast aside for cable and DSL broadband providers, numerous regulations and statutes still remain in place that will prevent monopoly control of broadband service (Justin P.Hedge 2006). More information on prices could increase consumer price sensitivity for undifferentiated products. At the same time, having more information on non-price attributes could reduce price sensitivity for differentiated products (Alba & others 1997). In terms of Web behavior, the ease with which potential goal-directed shoppers can find the information they want, and the reliability they can place on that information, will be key determinants in their repeated use of the Web (Kathy Hammond, Gil McWilliam, Andrea Narholz Diaz, 1997). Key to the regulatory argument appears to be an assumption that local loop unbundling will promote the availability of new technologies based upon telecommunications networks (such as broadband services) - a supply-side argument based upon the premise that competing providers will put pressure upon incumbent operators to incorporate new technologies into their networks, thereby making the new technologies available to consumers (BOLES de BOER David, ENRIGHT Christina & EVANS Lewis 2000).

Previous studies suggested that the significant role of attitudinal factors such as relative advantage, utilitarian outcomes, hedonic outcomes and service quality on influencing consumers' behavioral intentions to adopt personal computers (Brown and Venkatesh, 2005) and broadband (Dwivedi, 2005). A total of 16 constructs were expected to be correlated to the BI of consumers when adopting broadband Internet in India. Of these 16 constructs three, including relative advantage, hedonic outcomes and cost, significantly correlated to the BI of consumers. In terms of the size of the effect of these three constructs that contributed significantly to behavioral intentions, the relative advantage construct had the largest impact in the explanation of variations of BI (Yogesh K. Dwivedi1, Michael D. Williams, Banita Lal, Vishanth Weerakkody and Sneha Bhatt.

A satisfaction dimension corresponds to a number of product attributes or features that together generate particular aspects of performance, such as price, perceived quality, ease of service, convenience in availability, variety of features, attractiveness of the product, and advertising of the product. The most important factors that will cause them to change are the perceived quality of the product and attractiveness of the product, while convenience in availability is not found to be of a great influence in brand switching. (Paurav Shukla, 2004). Since users fall into four categories: Students, Home users, Enterprise users, Government, the needs and wants are different for each type of users. For example enterprise users need higher bandwidth to support their commercial interests and require continuous servicing by the provider, whereas home users are contend with relatively low or medium bandwidth and support when needed (Venkata Praveen, Fotios C Harmantzis 2006). Product familiarity had a significant impact on Indian consumers' attitudes, subjective norms, intention to buy, and, ultimately, purchase behavior of the low innovator and high innovator groups (HoJung Choo, Jae-Eun Chung, and Dawn Thorndike Pysarchik 2004).



Based on the previous research into consumer perceptions, constructs affecting broadband choice and influencers of consumer satisfaction, a formative model capturing the mediating role of broadband adoption, expectation on the outcome of satisfaction is conceived by the author, which will be used in model testing under structural equation modeling. In the previous researches, effects of attitudinal constructs, normative constructs and/or control factors on the consumer behavior are extensively studied either in isolation or consolidated (Venkatesh, Brown 2005 and others). There has been relatively little research focus on mediatory role played by any of the subjective factors. However, it is felt that a model would be of immense use for the academics and the industry, if it captures the important factors that influence the satisfaction of broadband users available for practitioners. The author has focused the research in that direction and carried on the research to come up a complete and comprehensive model.

In figure 2, a formative model for structural equation modeling technique is presented for the academics, telecom industry managers. This model has been proved empirically in findings section. It is suggested that the academics can use this model with a fair amount of care as this designed for a particular geographical setting i.e., areas covered under Chennai BSNL and is limited to that metropolitan and adjoining areas located in a single state (Tamil Nadu). This particular area is chosen for study keeping in view of the economic diversity, cultural diversity and social divergence. Chennai is a metropolitan city (cosmopolitan in social setting) and has wide divergence in socio-economic life. It has vast majority of growing middle class and urban poor. The adjoining areas are semi urban in socio-economic setting and comprise even villages turning as towns. Chennai Telephones under BSNL represents an ideal ground for the purpose of study.

In order to capture the interdependence of adoption, perception constructs to achieve consumer satisfaction; the author has conceived the following model in fig 1. There are four independent dimension/constructs (Attitudes, Normative (awareness), Adoption and Expectation) and a dependent construct (Consumer Satisfaction). Normative constructs are connection, charges, rental, usage plans, fault repair procedure, customer care, value added services availability and payment facilities. Attitudinal constructs are provider service quality, cost, provisioning speed, connection speed, post provisioning, provider's plans, superior payment facilities and perceptions on other operators' service. Adoption constructs are usage, duration, usage of other service providers, switchover, purpose, user, payment mode, availability, service quality, speed, advertisement, influencers, and point of purchase and payment facilities. Expectation constructs are provisioning speed, price, service quality, utility, customer care, and fault attendance, post provisioning care, payment facilities and value added services. Consumer satisfaction is determined by provisioning speed, price, service quality, utility, customer care, and fault attendance, post provisioning care, payment facilities and value added services.



The research employed a cross-sectional methodological approach. Cross-sectional data "is one used to collect data on all variables at one point of time" (O'Sullivan & Rassel 1999). This approach is adopted in most econometric data collection. The survey design is regarded as the most appropriate research design to measure the perceptions of the respondents in this study. A survey is the most appropriate research design as it can enable the researcher to collect information from a large population. The current study is a relational survey that seeks to explore the relationship between Attitudes, Awareness & Perceptions (Normative), Expectation Factors as the mediating factor and Adoption factors, again, overall mediating factor on the outcome Consumer Satisfaction. Pilot study consisting of a questionnaire was carried out with 50 existing Broadband users of BSNL Chennai. Based on the feedback, the questionnaire was revised by either deleting some constructs or adding some constructs. Questionnaire consists of VI Parts. Part I relates demographics, II consists of questions on brand awareness, III has questions on attitudes & perceptions, IV concentrates on adoption, V on Expectation and concludes with VI questions on consumer satisfaction with a five point scale response. Questionnaires were handed over to over 1650 individuals who visited BSNL Chennai Customer Care Centers in its Service Area. Of these, 1574 individuals have responded to the questionnaires. Only 1520 responses were found valid with all questions' responses and remaining 54 responses were rejected either due to incompleteness or respondent own responses.

Chennai Metropolitan and adjoining areas covered by Chennai BSNL have a good mix of variety of commercial and economic units' viz., automobiles, information technology, banking and financial institutions. Besides, the education sector is making strident progress with new generation educational institutions in various faculties. Growing medical tourism and health care requires a great deal of investment in information technology including internet enabled services. As population of these areas represents a mix of all races of India, it is felt that the study could be representative of consumer perceptions of broadband services. Therefore, the findings of this study can be generalized to India as a whole and of course, with a fair amount of care. The findings can be applied at ease to all urban areas in India. As broadband penetration is quite high in urban areas compared to rural India, the study will be of immediate use to telecom companies either prospective or existing.

Respondents' characteristics based on demographics are listed out in table 1.

Table 1 Respondent Characteristics


The data collected were analyzed for the entire sample. Data analyses were performed with Statistical Package for Social Sciences (SPSS 17) using techniques that included cross tabulation and AMOS Package (AMOS 16) for Structural Equation Modeling and Bayesian Estimation and testing.


The main study used structural equation modeling (SEM) because of two advantages: "(1) estimation of multiple and interrelated dependence relationships, and (2) the ability to represent unobserved concepts in these relationships and account for measurement error in the estimation process" (Hair et al., 1998, p.584). In other words, a series of split but independent multiple regressions were simultaneously estimated by SEM. Therefore, the direct and indirect effects were identified (Tate 1998). However, a series of separate multiple regressions had to be established based on "theory, a priori experience, and the research objectives to distinguish which independent variables predict each dependent variable" (Hair et al, 1998, p.584). In addition, because SEM considers a measurement error, the reliability of the predictor variable was improved. Structural Equation Modeling was conducted with AMOS 16.0 (an upgraded version of AMOS 7.0). AMOS 7.0 (Arbuckle and Wothke, 2006), a computer program for formulating, fitting and testing structural equation models to observed data was used for SEM and the data preparation was conducted with SPSS 15.0.

Linear structural equation models (SEMs) are widely used in sociology, econometrics, management, biology, and other sciences. A SEM (without free parameters has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the "error" or "disturbance" terms), and an associated path diagram corresponding to the casual relations among variables specified by the structural equations and the correlations among the error terms. It is often thought that the path diagram is nothing more than a heuristic device for illustrating the assumptions of the model. However, in this research, the researcher will show how path diagrams can be used to solve a number of complex problems in structural equation modeling.

Structural equation models with latent variables (SEM) are more and more often used to analyze relationships among variables in marketing and consumer research. Some reasons for the widespread use of these models are their parsimony (they belong to the family of linear models), their ability to model complex systems (where simultaneous and reciprocal relationships may be present, such as relationship between satisfaction and various normative constructs). As is usually recommended, a confirmatory factor analysis (CFA) model is first specified to account for the measurement of relationships from latent to observable variables. In the present case, the attitudes & perceptions, awareness of brand, expectation are the observable variables and attitude being mediating factor on the internal endogenous variable adoption and adoption is the overall mediator on the endogenous variable, consumer satisfaction.


The author developed following path model to study the mediating effects of attitudes & perceptions and consumer satisfaction and their interdependencies. After specifying the model, the hypotheses were formulated for empirical analysis and testing.


Hypothesis are numbered from 1 to 8 and denoted by notation H as H1 to H8 in figure 2. Alternative hypothesis (Null Hypothesis) for all the above hypotheses is that the variables under study have no significant influence or no relationship with each other and/or the outcome.

H1: Awareness has significant influence on the mediator attitudes.

H2: Attitudes are significantly influenced by expectation constructs.

H3: Awareness has significant influence on the mediator adoption constructs.

H4: Adoption is significantly influenced by expectation constructs.

H5: Attitudes influenced by awareness and expectation factors plays a mediating role with regard to outcome Adoption.

H6: Awareness has positive influence on Consumer Satisfaction.

H7: Expectation factors significantly influence on Consumer Satisfaction.

H8: Attitude significant mediates Satisfaction alongwith awareness and expectation

H9: Adoption significantly mediates other normative and descriptive constructs and has a significant influence over Consumer        Satisfaction.

Figure 2 Path Diagram: Mediating - Mediating CONSAT Model

These hypotheses are to be tested using Structural Equation Model and its regression coefficients and are carried out in the succeeding section.


A. Mediating-Mediating CONSAT Structural Equation Model

In hierarchical regression, the predictor variables are entered in sets of variables according to a pre-determined order that may infer some causal or potentially mediating relationships between the predictors and the dependent variables. Such situations are frequently of interest in the social sciences. The logic involved in hypothesizing mediating relationships is that "the independent variable influences the mediator which, in turn, influences the outcome" (Holmbeck, 1997). However, an important pre-condition for examining mediated relationships is that the independent variable is significantly associated with the dependent variable prior to testing any model for mediating variables (Holmbeck, 1997). Of interest is the extent to which the introduction of the hypothesized mediating variable reduces the magnitude of any direct influence of the independent variable on the dependent variable. Hence, in this paper, the hierarchical regression model CONSAT is tested

Regression analysis reveals that awareness and expectation have influence over the attitudinal factors which mediates the mediator adoption. Individually, awareness is having positive influence on satisfaction whereas expectation has negative momentum over satisfaction. Higher the expectation, lower the level of satisfaction as seen from published works. Attitude has a positive mediator influence over satisfaction but its mediatory influence is not as strong as adoption over satisfaction. Finally, adoption of the broadband user is very crucial for overall satisfied customer. Therefore, the regression model is a good model within structural equation modeling environment.

B Posterior Diagnostic Plots of CONSAT Mediating-Mediating Model

Figure 4 Posterior Frequency Polygon Distributions

To check the convergence of the Bayesian MCMC method the posterior diagnostic plots are analyzed. The figure 4 shows the posterior frequency polygon of the distribution of the parameters across the 47,500 samples. The Bayesian MCMC diagnostic plots reveals that for all the figures the normality is achieved, so the structural equation model fit is accurately estimated.

Figure 5 Posterior Distribution of Autocorrelation

To determine how long it takes for the correlations among the samples to die down, autocorrelation plot which is the estimated correlation between the sampled value at any iteration and the sampled value k iterations later for k = 1, 2, 3,.... is analyzed for the CONSAT regression model. The figure 5 shows the correlation plot of the CONSAT model for the mediated factor External F with other dimensions across 45,000 samples. The three figures exhibits that at lag 90 and beyond, the correlation is effectively 0. This indicates that by 90 iterations, the MCMC procedure has essentially forgotten its starting position. Forgetting the starting position is equivalent to convergence in distribution. Hence, it is ensured that convergence in distribution was attained, and that the analysis samples are indeed samples from the true posterior distribution.

Figure 6 Posterior Trace Plot

The trace plot also called time series plot shows the sampled values of a parameter over time. This plot helps to judge how quickly the MCMC procedure converges in distribution. The following figure (Figure.6) shows the trace plot of CONSAT model for the mediated factor Adoption across 45,000 samples. The figure exhibits rapid up and down variation with no long-term trends or drifts.

With regard to conceived model CONSAT, while many management scientists are most familiar with the estimation of these models using software that analyses (e.g., LISREL, AMOS), this paper adopt Bayesian approach for estimation and inference in AMOS 16.0 environment. Since, it offers numerous methodological and substantive advantages over alternative approaches. The table in Appendix B shows the Bayesian convergence distribution of CONSAT mediated regression model. In this paper, the author has adopted the procedure of assessing convergence of MCMC (Markov Chain Monte Carlo) algorithm of maximum likelihood. To estimate the MCMC convergence, the author has adopted two methods namely, convergence in distribution, convergence of posterior summaries. The values of posterior mean accurately estimate the CONSAT mediated SEM model. From the above table the highest value of Convergence Statistics (C.S) is 1.000, which is less than the 1.002 conservative measures.


The CONSAT mediating-mediating model to understand the interdependencies between attitudinal, normative constructs and adoption of consumer is estimated and empirically tested. The model provides quantitative assessment of mediatory role played by attitudes and adoption over satisfaction in combination with other constructs. Uniqueness of this model is that it has mediating factor attitude which in turn mediated by adoption. Previous researches suggest that attitudinal constructs have significant influence over broadband adoption (Venkatesh, Brown and others). This model empirically and graphically proves that the adoption is controlled by attitudes. Successful adoption leads to satisfied broadband user provided other constructs expectation and awareness is well gauged. Negative association between expectation and satisfaction vindicates previous works. However, the limitation of the study is that it is confined to a particular telecom company operating in a service area. However, the service area has been chosen after careful consideration to withstand the rule of generality. Findings, therefore, suggest that broadband user attitudes need to be gauged for successful adoption of broadband usage.


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