Nigerian Consumers’ Online Retailing Evaluation and Behavioural Intentions Decision

Using Analytic Hierarchy Process (AHP)

A. G. Adekoya1* and E. O. Oyatoye1

Abstract: Recently, online retailing has maintained an attractive unique selling point in Nigeria.However, amidst the acclaimed growth, there are still some fundamental problems such aslack of trust, poor customer service, and inadequate infrastructures needed to enable onlineretailing. All these are a source of main concerns for the prevention of consumers fromconducting more online shopping and hampering the continuous online repurchase intention.In view of the gaps identified and suggestions for further studies by various scholars, thispaper conducted an investigation into the identification, analysing, and prioritizing of post-adoption factors used by online retailing consumers to evaluate the Nigerian online retailingindustry that would lead to repurchase behaviour. Respondent are asked to identify thedifferent post adoption factors used in evaluating online retailing service providers.Respondents are requested to indicate and compare the various post adoption factors used inevaluating online retailing service providers, according to their judgment, on how importantthey are when compared with one another. The sample size for the study is 380 participants.Responses is generated from MBA part time programme university students in Lagos state,Nigeria using a multi-stage sampling design technique by means of a structured questionnairecontaining dichotomous questions based on Saaty’s scale of preference. Consistency ratiosare to be computed to confirm how consistent the judgments of the respondents were.Composite priorities of the critical importance of the factors are to be computed, while thepooled average composite priorities are also computed. The results from the study wouldrevealed most important factor(s) used by consumers to evaluate online retailing serviceproviders that leads to repurchase behavioural intentions decision. The knowledge gainedfrom this study would go a long way in ensuring effective marketing policies towards theinterest of customers which would eventually facilitates positive customers attitude towardsthe organizations.

Keywords: Consumers, Online Retailing, Behavioural Intentions, Repurchase Behaviour, Evaluation, AHP, Nigeria

________________1 Faculty of Business Administration, University of Lagos. Address: Akoka –Yaba, Lagos, Nigeria,Tel.: +234 802 3304 912, Corresponding author: agadekoya@unilag.edu.ng; agadekoya@gmail.com1 Professor, PhD, Faculty of Business Administration, University of Lagos. Address: Akoka –Yaba,Lagos, Nigeria, Tel.: +234 805 2811 824, E-mail: eoyatoye@unilag.edu.ng; olateju_2006@yahoo.co.uk;

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IntroductionMarketing channels, as pointed out by Coughlan, Anderson, Stem, and El-Ansary (2001), aresets of interdependent organisations involved in the process of making a product or serviceavailable for use or consumption. They function to move goods from producers to consumersand overcome the time, place, and possession gaps that separate goods and services fromthose who want or need them (Kotler & Keller, 2009). Infact, retailing (a type of marketingchannel) account for more than $4.5 trillion sales to final consumers every year. However, inrecent years, there is a profound impact on the nature and design of retailing owing tochanges in technology and especially the internet and the explosive growth of onlinemarketing (Kotler & Armstrong, 2010). Online retailing is common in today’s world oftechnological development and e-commerce and it is mostly the young who are involved in it,which could be due to their increasing access to computer and internet. Online retailersprovide the platform for online shopping experience.

Online retailing is arguably one of the fastest growing retail markets in Europe and USA.According to Retailresearch.com (2015) sales in the UK, Germany, France, Sweden, TheNetherlands, Italy, Poland and Spain were expected to grow from £132.05 billion [€156.28billion] in 2014 to £156.67 billion [(€185.39 billion] in 2015 (+18.4%), reaching £185.44billion (€219.44 billion) in 2016. In 2015, overall online sales are expected to grow by 18.4%(same as 2014), but 13.8% in the U.S. on a much larger total. These figures relate onlyto retail spending which is the sales of merchandise to the final consumer. In the US, onlinesales were expected to rise from $306.85 billion [£189.26] in 2014 to $349.20 billion[£215.39 billion] in 2015 and $398.78 billion [£245.96 billion] 2016. Canada's online sectoris comparatively small, but was forecast to grow from US$20.82 billion [£12.84 billion] in2014, to reach $23.56 billion [£14.53 billion] in 2015 and $26.99 billion [£16.65 billion] in2016 (Retailresearch.org, 2015). In terms of market share, the US share of retail (that is salesof goods) in 2014 was 11.6% and 12.7% in 2015. The European market share was 7.2% in2014 and 8.4% was projected for 2015. With a similar population to the eight countriessurveyed, 57.4% of the US public were online shoppers compared to 46.7% in Europe. Everyonline shopper in Europe is expected to spend £820.05 [$1329.54 or €970.47] in 2015compared to £1119.79 [$1815.52 or €1325.20] in the US. Online retailing currently accountsfor 12% of the retail purchases in the U.S. Amazon.com is clearly the leading online retailerin terms of its reach, projected buyers and the unique users around the world. Although theUS is still the leader in online retailing compared to Europe, there has been a lot of discussionin the US about when the online share would break through the 10% barrier(Retailresearch.org, 2015). Consequently, as a result of this online sales growth and at such arate, it will inevitably reduce the market for offline shops.

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Despite the growth of online retailing as expressed above, online retailing had a slow start inthe African region especially Nigeria due to lack of appropriate infrastructure and trust issues.Aderibigbe (2014) affirmed that online retailing in Africa was still in its infancy. Only fiveyears ago the online retailing landscape was very different in Nigeria. In October 2011,Naspers’ Kalahari.com.ng website closed down after trading for less than two years. Theprimary reason was simply because there were such a small number of potential onlinecustomers. However, with the coming of Konga.com in June 2012 and Jumia.com a monthlater, the online retailing activities in Nigeria took a new dimension. As a result of the successof these two online retailing sites, online trade and retail in Nigeria maintains a positiveoutlook.

As a result of this, online retailing is gradually gaining momentum in Nigeria. More and morepeople are buying and selling online. With the increasing internet access, more affordabledata costs, mobile connectivity and the convenience of purchasing goods and services online,more Nigerians are beginning to embrace the culture of online retailing. (Zmyslowsk, 2014).2014 was a good year for the online retailing industry in Nigeria, new online retailing serviceproviders emerged such as Yudala, Dealdey, Fouani.com, Gloo.ng, Gidimall.com, Kara.com,Parktelonline.com, Taafoo.com, Ojashop.com, Slot.ng (Ezeamalu, 2014). These onlineretailers provides retail goods in niche areas like perfume, wrist watch, fashion, houseproducts, electronics and computers, auto parts, grocery, shoes, foods, furniture, and othersprovide everything (general) on their websites.

By end of June 2014, 63 per cent of Nigerian internet users had bought at least one itemonline (Ekpeke, 2015). Thus, by the end of 2014, Nigeria saw a resurgence in online retailingactivities, with the market forecast to total $1.3billionn in year 2015. Nigeria’s formerCommunications Minister Omobola Johnson pointed out that Nigeria’s online retailing has apotential worth of $10 billion with about 300,000 online orders currently being made on dailybasis as at December, 2014 (Adepetu, 2015; Ecom4all, 2014). Ekpeke (2015) pointed out thatNigeria’s online retailing market is developing rapidly, with an estimated growth rate of 25percent annually. In fact, Akintola (2015) affirmed that Nigerians are now very conscious ofonline retailing. Today, there are currently over hundred online retailing providers in Nigeria,however only 90% online shoppers patronize only a tiny fraction of them.

Ndiomewese (2016) remarked that with statistics revealing that Nigeria has thehighest internet penetration in Africa, one would by now expect that online retailing would bethe preferred choice for shopping and effecting payment for product and services. However,this is not the case even after adopting working strategies like the Black Friday sales amongothers. He added that why then is online retailing adoption yet to hold sway in Nigeria or itmay be the notion of Nigerians keeping to trend hovers only around their desire to feel newthings and then move on from there to the next new thing. Observation revealed that the typeof online retailing practiced in Nigeria today is focused on customers' disposable incomes;such disposable incomes are meagre except for the super-rich who are a minute minority.

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It is a known fact that customers are rational whenever alternatives are available in makingtheir choice for patronage. Zhang, Li, Gong and Wu (2002) affirmed that consumer decisionmaking process is considered a complex process. A decision is the selection of an option fromtwo or more alternatives, so in order for a person to make a decision, a choice of alternativesneeds to be available (Schiffman & Kanuk, 2007a). The generic model of consumer decisionmaking consists of five stages, namely need recognition, search, evaluation of alternatives,purchase and post-purchase (Dewey, 1910). The classification of the decision making processin stages is a rational approach to decision making (Punj & Srinivasan, 1992; D´Astous,Bensouda, & Guindon, 1989).

During decision making, Bozinoff (1982) remarked that consumers are involved in non-conscious behavior, referring that decision making can be a subconscious act. Hence,consumer decision making does not only involve what products or service do but also whatthey mean to consumers since they are driven by emotional needs and are limited in theoptions they are willing to consider (Bettman, 1993; Schiffman & Kanuk, 2000). Schiffmanand Kanuk (1991) stated that the extent of decision making is influenced by how wellestablished the consumers’ criteria for selection are. It should be noted that consumers mayhave limited knowledge and skills, and certain values could dominate goals and decisions(Erasmus, Boshoff, & Rousseau, 2001).

The evaluation of alternatives stage of the consumer decision making process consists ofconsumer decision strategies that are the procedures that consumers use to make choices andprovide guidelines that make the decision process less burdensome. Consumer decisionstrategies can be based on compensatory decision rules, where a product or service (in thiscase online retailing) is evaluated in terms of attributes that are weighted and can balance outa negative evaluation on another attribute. A consumer decision strategy can also be based onnon-compensatory rules, where a minimum acceptable level is selected for each attribute(conjunctive rule), or for all attributes that meet or exceed the minimum acceptable level ofany attribute (disjunctive rule) or by ranking the attributes in terms of relevance orimportance (lexicographic rule) (Schiffman & Kanuk, 2007 as cited in Uzan, 2014).

With the growing online retailing in every society, especially in recent years in thedeveloping countries like Nigeria, customer acquisition and retention have found animportant role in business success. Raising the number of reliable customers by as slight as5% can raise profitability by 30% to 50% depending upon the business (Reicheld & Schefter,2000). In virtual markets, customers are engaged to online retailers with value added features.Understanding the needs and demands of customers are preconditions for value creation, soeffective factors for customer satisfaction must be determined and then be improved.Identifying priority of the factors is another useful task for each company with resourcerestriction (Tabaei & Fathian, 2012). Furthermore, despite the impressive online purchasinggrowth rates in the past few years, compelling evidence indicates that many consumerssearching for different online retail sites, abandon their purchases, which make the online

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retailing market is still small. Likewise, how to persuade customer repurchase remains aconcern for online retailing vendors (Johnson & Hult, 2008 as cited in Wang & Wu, 2011).

Thus, according to Reichheld and Schefter (2000) this trend, along with the proliferation ofonline retailing activities, necessitate a better understanding among online retailing serviceproviders of the factors that encourage consumers to repurchase online as such continuanceaction (repurchase behavior) are critical, given the reasonably high cost of acquiring newcustomers and the economic value of reliable customers. A consumer will evaluate thepurchase they have made according to their expectation. This will allow them to clear theiruncertainty or anxiety about the informed pre-purchased decision, if satisfied they repeatpurchase or discontinue purchase from the retail provider. Failure to repurchase would haveserious consequences for the company’s reputation and the customers’ loyalty. Thus, thisstudy attempts to identify factors used by online retailing consumers in their evaluation of theNigerian online retailing industry in order to repurchase online, analyze and prioritize thesefactors using Analytic Hierarchy Process (AHP).

Statement of the Problem

Online retailing maintains a particularly attractive unique selling point in Nigeria: thegrowing urban population is more internet conscious and more likely to prefer onlineretailing to offline retailing due to the severe traffic congestion in Nigeria’s major cities,which makes home delivery a very attractive option. Nevertheless, online retailing is likely tocontinue accounting for a very small proportion of overall retailing sales value for theforeseeable future as the majority of Nigerians still prefer traditional (offline) modes ofshopping including haggling and the hustle and bustle of open markets and it is likely to takeconsiderable amount of time to convince them to change deeply ingrained habits.

The online market space is not as big as it is made to sound. Despite Nigeria’s online retailingsector being ranked fastest growing in Africa, many Nigerians are still skeptical about buyinggoods online. There are funny tales of returns and no refunds or replacements or no shippingat all. Also, most of the online retail platforms do not claim responsibility for loss of fund(Ekpeke (2015). Furthermore, for an average shopper, finding something they wanted mightinvolve a long journey, at the end of which there was no guarantee that the product theywanted would be in stock (AllAfrica.com, 2014)

In the offline channel, customers do not have to worry that they will be given the productthey paid for. This is because the customer goes to a shop, selects a product, pays for it andtakes it away. Customers also do not have to worry that their financial data will be revealed toa third party, as they make payment in cash. In addition to the above points, customers canremain anonymous and avoid merchants being able to trace their buying habits throughmaking their payments in cash. In online retailing, the factors mentioned above vis-à-visoffline channel can be major concern for customers. Through online payment, personal dataand financial information that are not encrypted might be revealed to fraudsters. There mustbe trust between the buyer and the seller but in online retailing, customers may be worriedthat dishonest dealers will send them the wrong product or not send it at all (Alotaibi, 2012).

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In fact, according to the study of Capital Partners (2013 as cited in Uzan, 2014) return rateof products bought online is higher than the return rate of products bought within a physicalstore.

Furthermore, discontent is rising among customers of online retailing. Several aggrievedcustomers who responded positively to the claims of the company and its persuasiveadvertising on Google, Yahoo and other popular sites on the beauty of shopping on the onlinestore are not pleased with the services of the online firm. From complaints of wrong goodsdelivered, bad electronics to lack of delivery of goods after online payments, the list isendless. An online retailer for instance may not come out to tell the customer that an itemdisplayed on their platform is not available or is out of stock, yet an order placement isprocessed on such good. The uninformed customer who is yet to have the goods delivered tohis doorstep after lengthy spells of waiting eventually has the order cancelled. The impressionsuch customer will hold about online retailing will be misinformed. Nigerians are now morecautious about making repurchases online (Wordpress.com, 2014; Ezeamalu, 2014) and as aresult a customer who may have been disappointed on more than one occasion would ratherprefer to shop with the next door retailer or agitate for offline sales; where they see the goodsphysically before anything else. Another roadblock in the online retail market is data security,which reduces frequency of online purchases. In fact, a large number of Nigerians still preferthe traditional retail channels because of this (Eromosele, 2015; Ezeamalu, 2014).

Moreover, one other challenge of online retailing in Nigeria, is the lack of adequateinfrastructure that will drive the business. As the name implies, online retailing rides onbroadband data infrastructure. The consumer must have access to the internet before shoppingonline. But a situation where huge broadband capacity still lies at the shores of the country,and cannot be transmitted to the hinterlands for easy access to offices and homes, poseserious challenge to online retail business in the country (Ezeamalu, 2014).

Consequently, amidst the acclaimed growth, there are still some fundamental problems andmost importantly, lack of trust, ease of use, cybercrime, technological innovation, deliverycosts, logistics presents a unique challenge, poor/delayed services, poor customer service,internet frauds and perceived lack of security with online payment. Some of theinfrastructures needed to enable online retailing are still lacking. All these are hampering thecontinuous online repurchase intention in Nigeria and are among main concerns for theprevention of consumers from conducting more online shopping (Adepetu, 2015; Ecom4all,2014). To this end, there is a need to attempt to identify factors used by online retailingconsumers in their evaluation of the Nigerian online retailing industry in order to repurchaseonline. This is the motivating drive for this study based on the personal experience of theresearcher.

Previous research on online retailing focuses on whether to adopt online retailing (e.g.,Soopramanien & Robertson, 2007) while prior studies in developed countries mostlyinvestigate initial shopping intention (Sajad, Muslim, Wan & Wan, 2014). Ayo (2006)

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investigated the ability of consumers to purchase online and the available motivation to do so;while Bigné Alcañiz‐ , Ruiz Mafé‐ , Aldás Manzano‐ & Sanz Blas‐ , (2008) analysed theinfluence of online shopping information dependency and innovativeness on the acceptanceof internet shopping (retailing). Also, several studies in developing countries have attemptedto identify factors influencing consumers’ attitude towards online retailing purchases andfuture intentions to buy online (e.g., Kempen, Kasambala & Torien, 2015;Jusoh & Ling,2012; Goldsmith, 2002).

This was rightly pointed out by Cheung et al., (2005) in presenting a review of recent workwithin the field of online retailing that while the area of online retailing adoption has beenwell researched, the area of online retention (that is, the continued use of the internet forshopping after the initial purchase -repurchase behavior) has been relatively underresearched. Thus, they suggested that further study of online retailing should consider internetshopping in terms, not only of intention to purchase and adoption, but in terms of factorswhich influence continued use (repurchase intentions) of the internet as a sales channel.

Furthermore, most of the previous online shopping research focused on one specific type ofproduct such as books (Gefen, Karahanra & Straub, 2003, Lin, 2007), clothing (fashion)(Kempen et al., 2015; Tong, 2010; Ha & Stoel, 2009) groceries (Hansen, Jensen & Solgaard,2004), music (Ho et al., 2013), however there is a lacuna in the existing online retailingstudies with regards to studies that involve more than one products.

Understanding and predicting human behavior has been of particular interest to marketers formany years. Prior research shows that there are numerous factors that affect online consumerbehavior, nonetheless there are mixed findings in literature and many factors that influenceonline consumer repurchasing behavior have yet to be explored, especially considering thedynamics of technology and consumer needs, which are constantly evolving, and as a resultsignificant factors five years ago may differ today as consumers become more experiencedinternet users.

But while researches on online shopping behaviour have been steadily increasing thesestudies had focused on consumers’ pre-purchase evaluation of online retailing activities, thereseems to be a paucity of studies on consumers’ post purchase evaluation of online retailingactivities especially in the developing countries. Moreover, a review of the studies on onlineretailing revealed that previous studies had focused on examining relationship betweenfactors and intention to purchase and future intention to purchase in order to predict onlinecustomers behavior, there is a paucity of studies of online customers’ switching behavior topredict their online repurchase behavior.

Also, most research into consumer online behavior has been descriptive in nature, yieldingstatistical information on what is purchased online and the demographic characteristics ofonline buyers (e.g., Modahl 2007; Murphy 2000). Some research has expanded beyondsimple descriptions to explicit hypothesis testing regarding factors that influence onlinebuying (e.g., Degeratu, Rangaswamy, & Wu 2000; Phau & Poon 2000). Finally, a fewattempts have been made to develop models of online buying (e.g., Limayen, khalifa & Frini,

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2000). The present study takes its place in this domain by proposing a model of somecharacteristics of consumers that predispose them to continually repurchase online.

Another stimulant for this study was the theoretical approach. Consumer behavior researchersand theorists need to study consumer online behavior in order to determine how well existingconsumer theory can be applied to ecommerce and where new theories and models need to bedeveloped (Cowles & Kiecker 2000; Phau & Poon 2000). In order to understand consumer’sonline behavior and determinants of online purchasing, researchers have relied on the theoryof reasoned action (TRA), technology acceptance model (TAM), the theory of plannedbehavior (TPB), expectancy-confirmatory theory (ECT), innovation diffusion theory (IDT),and transaction cost theory (TCT); however, Cheung et at. (2005) found out that in mostresearch studies the backbone for understanding online behavior was based mainly on TAMand TPB with the other theories integrated into these research models. Whilst models help usto understand many of the drivers of attitudes and behaviour, they remain relativelyundeveloped in the field of online retailing. Due to this, studies must largely rely on disparatedata from numerous sources to help build a clearer picture of the factors driving consumerattitudes, behaviours and experiences.

Also, despite their widespread popularity, some common theories such as the technologyacceptance model (TAM), expectation confirmation theory (ECT) and Delone and McleanInformation System Success Model have not been tested in some developing countries suchas Nigeria. The TAM, as expanded by Davis, Bagozzi & Warshaw (1992) and Gefen et al.,(2003), and the ECT (Bhattarcherjee,2001a; Oliver, 1980) have been used widely in researchin the industrialized world but they are less commonly applied to Nigeria, which is part of thedeveloping countries. Moreover, the TAM stops at intention and does not investigatecontinuance intentions or behavior. Therefore, the study considers that there is a need formore research into online retailing repurchase intentions, particularly in non-westerncontexts. This gap becomes especially problematic in the context of research questionregarding what factors are used by online customers to evaluate online retailing channels andtheir repurchase intentions towards online retailing.

Theoretically, explanations of online retailing repurchase intentions consider several factors.Rogers (1995) suggests that consumers re-evaluate acceptance decisions during a finalconfirmation stage and decide to continue or discontinue. Continuance may be an extensionof acceptance behavior that covaries with acceptance (Bhattercherjee, 2001a; Davis, Bagozzi;and Warshaw,1989; Karahanna, Straub and Chervany 1999). Researchers are confronted witha multitude of models and find that they choose construct, or choose a favoured model andlargely ignore the contributions from alternative models (Venkatesh, Morris, Davis & Davis2003). Given the complementary nature of TAM, ECT and Delone and Mclean InformationSystem Success Model, adopt the extended expectation confirmatory (ECT) (Bhattercherjee,2001a; Davis et al., 1989) and the Ho et al.(2013) extension of updated Delone and Macleanmodel as a theoretical basis, to propose a model of customers’ online evaluation of onlineretailing channels.

Finally, as noted previously existing research largely ignores online retailing in Nigeria andAfrican countries, many of which can be considered to be developing nations. This thesisfocuses on Nigeria, specifically, consumers’ online retailing evaluation and behaviouralintentions decision especially whether to repurchase online or offline. It goes further byexplicitly addressing potential differences on the online retailing behavior of Nigerians. In

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working to fill all these gaps, this research offers validated, conceptual model that integrate avariety of factors, and clarifies the theoretical problems of repurchase intentions in the uniquecontext of Nigeria.

In view of the above mentioned gaps and the suggestions for further studies by variousscholars this study explores whether established theories from developed (mostly western)countries also apply in less developed (often non-western) countries; in the case of thisresearch Nigeria and identify post purchase factors used by online consumers’ to evaluateonline retailing channels using AHP and their repurchase intentions towards online retailingin Nigeria.

The specific objectives of the study are to :

1. Identify factors used by online retailing consumers in their evaluation of the Nigerianonline retailing industry.

2. Analyze consumers’ online retailing service delivery evaluation using AnalyticHierarchy Process.

3. Prioritize identified factors used by online retailing consumers in the evaluation of theNigerian retailing industry for effective online retailing service delivery.

4. Assess the use of Markov Chain Process in modelling and predicting discrete andcontinuous online retailing consumers’ behavioural intentions decision in the Nigerianretail industry.

5. Profer AHP and Markov Chain Process effective strategies/alternative measures forimproving online retailing service delivery and higher level of consumers’ onlinerepurchase intention in Nigeria.

Theoretical Framework

There has been growing interest among researchers as pointed out by Luo, Ba and Zhang(2012) toward studying online retailing in developing countries. In order to understandconsumer’s online behavior and determinants of online purchasing, researchers have relied onthe theory of reasoned action (TRA), technology acceptance model (TAM), the theory ofplanned behavior (TPB), expectancy-confirmatory theory (ECT), innovation diffusion theory(IDT), and transaction cost theory (TCT). However, Cheung, Chan and Limayem (2005)found out that in most research studies the backbone for understanding online behavior wasbased mainly on TAM and TPB with the other theories integrated into these research models.Furthermore, online retailing as observed by Cao and Mokhtarian (2005) is a highly complexand complicated decision making process, incorporating economic and technical issues. Thus,they suggested that the use of not one, but a number of models to explore online retailingsince no single theory appears capable in capturing the complexities of online retailingbehavior.

Venkatesh, Morris, Davis & Davis (2003) remarked that since researchers are confronted witha multitude of models (theories), they choose construct, or a favoured model thus largelyignoring the contributions from alternative models. Also, despite their widespread popularity,some common theories such as the technology acceptance model (TAM), expectationconfirmation theory (ECT) and Delone and Mclean Information System Success Model have

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not been tested in some developing countries such as Nigeria. Thus, given the complementarynature of TAM, ECT and Delone and Mclean Information System Success Model, this studyadopts the extended updated expectation confirmatory theory (ECT) (Bhattercherjee, 2001a;Davis, Bagozzi & Warshaw, 1989) and the Ho, See-To, Chiu & Wu, (2013) extension ofupdated Delone and Maclean model as a theoretical basis, to propose a model of factors usedby online consumers to evaluate online retailing channels and their repurchase intentionstowards online retailing.

Technology Acceptance Model

People learn and refine their shopping skills throughout their lives. Learning to shop on theinternet however, means developing a very specific set of skills and competencies, withrelation to a specific set of technologies. Various theoretical models have emerged to exploreand explain factors that cause individuals to accept, reject or continue the use of newtechnology (Wong, Osman, Goh & Rahmat, 2016). One of the most common model used inthis regard has been the Technology Acceptance Model (Davis 1985, 1989).

The Technology Acceptance Model (TAM), which is among the models that have theirorigins in the disciplines of psychology, information systems and sociology (Venkatesh, et al.,2003), is one of the most commonly used theory in the information technology field (Lee,Kozar & Larsen, 2003). This model, which was introduced originally by Davis in 1986 in hisdissertation, was derived from the theory of reasoned action of Azjen and Fishbein (1975)and was originally designed to predict individual technology acceptance and usage in theworkforce. Since then it has been going into several evolution (Chuttur, 2009).

Although, the model was originally designed to predict user‘s acceptance of InformationTechnology and usage in an organizational context, it has even been applied to examinecontinuance and post-adoption behavior (Gefen, Karahanra, & Straub, 2003, Karahanna,Straub & Chervany, 1999; Taylor & Todd, 1995). The technology acceptance model consistsof six distinct yet causally related constructs, namely external variables, perceived ease ofuse, perceived usefulness, attitude towards using, behavioural intention to use and actualsystem use (Davis, Bagozzi & Warshaw, 1989; Koh, Prybutok, Ryan & Wu, 2010).Subsequent research of Venkatesh and Davis (1996) refined the TAM suggesting that themediating effect of attitude could be excluded as empirical evidence found that the attitudeelement did not fully mediate the effect of perceived usefulness on intention to use(Ramayah, Ma’ruf, Jantan & Mohamad, 2002).

The key variables in the TAM are perceived usefulness and perceived ease of use, and theseare used to predict an individual’s acceptance of information systems technology. In themodel, perceived usefulness (PU) is defined as the degree to which a person believes thatusing a particular system would enhance his or her job performance. The definition followsfrom the word useful that is capable of being used advantageously; while perceived ease ofuse (PEOU) is defined as the degree to which a person believes that using a particular systemwould be free of effort. This definition follows from the word ease that is freedom fromdifficulty or great effort (Davis, 1989; Nguyen, Nguyen & Singh, 2014). The model statesthat the likelihood of a technology being used is directly relat--ed to these two factors (Figure2.1).

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Figure 2.1: Technology Acceptance Model

Source: Manchanda and Mukherjee, 2012

Delafrooz (2009) observed that from an e-business perspective, understanding the TAMcould provide a valid basis for explaining and predicting consumers’ online shoppingbehavior. Such understanding according to him would enable e-commerce managers to gainbetter insights into the consumers’ online shopping motivation and facilitate them indeveloping effective strategies towards increasing website traffic flow. Moreover, Al-Maghrabi, Dennis, Al-Ghamdi, and Bukhari, (2011) adopted the extended TAM of Davies, etal. (1989) to point out its ability as a theoretical basis for online e-shopping continuanceintentions. Zhou, Dai and Zhang (2007) even developed a model based on TAM called onlineshopping acceptance model (OSAM) to study online shopping behavior.

Venkatesh et al. (2003) suggest that usage and continuance intentions may depend oncognitive beliefs about perceived usefulness. Gefen (2003) also indicates that perceivedusefulness reinforces online shoppers continuance intention, such that when a person acceptsa new information system, he or she is more willing to modify practices and expend time andeffort to use it (Succi & Walter 1999). Moreover, Imdadullah, Shamsul and Hamidon (2016)stated that TAM has been implemented in several researches to understand the users’continuation intention to use IS. Premkumar and Bhattacherjee (2008) noted that perceivedusefulness is found to be the strongest predictor of intention in TAM, and continues to be thestrongest predictor of continuance intention (over satisfaction) when TAM is combined withECT, the relative dominance of usefulness explains its role as critical driver in continuancedecisions (Premkumar and Bhattacherjee, 2008).

Hence, various researchers had used the TAM to identify factors that encourages continuousintentions to repurchase online. Delafrooz (2009) integrated the perceived enjoymentconstruct into the model in an attempt to enhance understanding of individuals e-shoppingcontinuance or revisit intention. Moon and Kim (2001) extend TAM, indicating thatenjoyment and playfulness were an intrinsic motivation factor in acceptance and continuanceintention. Furthermore, Childers, Carr, Peck & Carson (2001) also find that enjoyment canpredict attitude towards e-shopping, just as much as can usefulness. Infact, whilePavlou(2003) developed a model to predict the acceptance of e commerce by adding new‐variables trust and perceived risk, Ha and Stoel (2009) study integrates e-shopping quality,enjoyment, and trust into a technology acceptance model (TAM) to understand consumeracceptance of e-shopping.

Basgöze and Özer (2012) in their study of the purchase intention of technological productsintroduced the concept of credibility into TAM as an influence on the purchasing behaviors of

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PerceivedUsefulness

ExternalVariables Actual

SystemUsage

BehaviouralIntention

To UsePerceived

Ease of Use

consumers. Credibility, in marketing literature, according to Goldberg and Hartwick (1990) isproducer’s (in this case online retailing service provider) prestige perceived by the consumer.Based on their study, Lu, Zhou and Wang (2009) mention perceived reliance to be oneimportant factor when evaluating the acceptance of an e-commerce system. Here, perceivedreliance is defined to be the measure of how far the user of the system thinks that it ismandatory to use the specific system in order to be able to perform their work tasksefficiently. According to Roshanak (2009), e-commerce is no more an alternative but acompulsory option to most businesses and today it is called the most cost-effective way oftrading.

Site quality and good interface design enhance the formation of consumer trust, and if aconsumer perceives a vendor’s website to be of high quality, he or she should trust thevendor’s competence, integrity, and benevolence (McKnight, Choudhury & Kacmar, 2002a).Gefen et al. (2003) integrate trust into the TAM in a B2C shopping context and find that trustpositively affects consumers’ intention to use a website. Building trust with consumers is anessential mission for e-retailers, because purchasing decisions represent trust-relatedbehaviours (Jarvenpaa, Tractinsky & Vitale, 2000; McKnight, Choudhury & Kacmar, 2002b;Urban, Sultan & Qualls, 2000). One key reason why many consumers use the internet but donot purchase online is because of beliefs about the safety of conducting business over theinternet. This perception of risk is definitely related to trust, since if the risk element iseliminated or at least decreased, the trust factor will be in a higher level. Virtual businessenvironment has some unique characteristics compared to the normal business environment,for example the lack of face-to-face or by phone interactions. Due to this characteristic,customers feel easily greater uncertainty and heightened risk in their online buying decisions.This contributes to the fact that customers’ trust of e-tailers and internet technology isbelieved to play a pivotal role in customers’ e-tailing behaviors. If the user of the system doesnot trust that it is safe to perform a business tasks with the application, you may assume thatthe intention to use decreases.

Studies which employ the TAM work from the assumption that the online shoppingexperience is different to the offline shopping experience, in that it is mediated by a set oftechnologies and software (Kazia & Shah, 2013). Therefore, it is important in terms of therichness of the literature to adapt Technology Acceptance Model to the repurchasing behaviorof the online retailing consumers. Thus, this study identifies the underpinning theory andpotential application in a concise way and concludes that TAM has and will provideunderpinning for further identifying and understanding of the factors that encourages onlineretailing consumers’ repurchase behavior. For the purpose of this research, the TAM model(Davis, 1989) minus the external variables was used. The research model is as shown inFigure 2.2 .

The Technology Acceptance Model (TAM) (Davis et al., 1989) forms one of the foundationof the conceptual model for this study, and includes two specific beliefs that are relevant foronline consumers to repurchase from online retailing service providers, namely perceivedusefulness and perceive use of ease. Perceived usefulness (PU) is defined as being the degreeto which an online retailing consumer believes that the continuous repurchase from onlineretailing service provider will improve or enhance or increase his or her shopping ortransaction performance advantageously. In essence, this is the main determinant of theintention to continue to repurchase online irrespective of the level of experience of the onlineretailing consumer; and perceived ease of use. Perceive ease of use (PEOU) is defined as the

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degree to which an online retailing consumer believes that the continuous repurchase fromonline retailing service provider would be free from difficulty or great effort. Thus, influenceof PEOU on the attitude (behavioural intention) of the online retailing consumer towards theonline retailing service provider is substantially smaller as he or she repurchases. Behavioralintention (BI) is used to express the extent to which an online retailing consumer formulatesconscious plans to repurchase or not to repurchase from online retailing service provider.Behaviour (B) is determined by behaviour intention (BI), which is in turn jointly determinedby the individual’s attitude towards repurchasing online and perceived usefulness (U).Finally, perceived ease of use (PEOU) is a direct determinant of attitude towardsrepurchasing online and perceived usefulness (PU). The study added other relevant factorsidentified in the review of research work on the TAM model in the online retailing such asperceived enjoyment, trust, perceived risk, brand credibility, perceived reliance, site qualityinto the conceptual model.

Figure 2.2: TAM Research Model for the study

Updated DeLone and McLean Information Systems Success Model (D&M IS Model)

The successful implementation of information technology is considered a key issue in thediscipline of information systems (IS) of which online retailing is embedded. However,perhaps because of the imprecision and broadness of the concept, several authors haveoperationalized this success in dissimilar manners within their studies. As a way to integrateall these piecemeal, two decades ago the IS success model of DeLone and McLean (D&M)(DeLone & McLean, 1992) arises.

Motivated by DeLone and McLean’s call for further development and validation of theirmodel, many researchers have attempted to extend or re-specify the original model. Ten yearsafter the publication of their first model and based on further research conducted by DeLoneand McLean (2003), including the evaluation of the many contributions to it, DeLone andMcLean proposed an updated IS success model to include service quality as an extraindependent variable, and reducing the final dependent variable to net benefit of using theinformation system. (DeLone & McLean 2002, 2003). Nowadays, many IS studies use thisupdated model to investigate into various information systems (Cao, Zhang, & Seydel, 2005;Lin, 2007; Petter & McLean, 2009).

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Intention

To Continue toUse(Repurchas

e)

Perceived

Ease of Use

PerceivedUsefulness

The updated model of D&M consists of six interrelated dimensions of IS success:information, system quality, service quality, intention to use, user satisfaction, and netbenefits. It includes arrows to demonstrate proposed associations among success dimensionsin a process sense but does not show positive or negative signs for those associations in acausal sense. The arrows demonstrate proposed associations between the success dimensions(see Fig. 2.3).

Fig. 2.3: Updated Information Systems Success Model (DeLone & McLean 2002, 2003)

Source: Manchanda and Mukherjee, 2012

The idea behind the model is that a system such as online retailing can be evaluated in termsof information, system, and service quality. These criteria affect the subsequent use orintention to use and user satisfaction. As a result of using the system, certain benefits wouldbe achieved. The net benefits would (positively or negatively) influence user satisfaction andthe further use of the information system in this case online retailing. Moreover, if the onlineretailing service is to be continued, it is assumed that the net benefits from the perspective ofthe consumers of the system are positive, thus influencing and reinforcing subsequent use anduser satisfaction. These feedback loops are still valid, however, even if the net benefits arenegative. The lack of positive benefits is likely to lead to decreased use and possiblediscontinuance of the online retailing service provider which may lead to switching toanother service provider or offline retail system.

In the context of e-commerce, the primary system users are customers. According to Chuangand Fan (2011) customers use the system to make buying decisions and execute businesstransactions. As suggested by Ho, See-To, Chiu and Wu (2013) as well as in the UpdatedD&M Model, E-commerce success factors include information quality, service quality, andsystem quality as the major independent variables. Moreover, since online retailing is a typeof E-commerce, it was conjectured that the Updated D&M Model can also be applied toonline retailing research. In addition, trust between a consumer and the online retailing

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service provider in the online environment is crucial and it affects whether a consumerrepurchase from a particular online retailing service provider (Ahn, Ryu & Han, 2007).

DeLone and McLean (2003) suggested that net benefits indicate the ultimate influence of thesystem for its participants. Seddon (1997) insisted that diverse stakeholders may havedifferent opinions about what is beneficial to them. Thus, when determining the netadvantages of an IS, researchers need to define naturally and carefully the stakeholders andthe setting of the net benefits that are going to be determined (DeLone & McLean, 2003). Inthe online retailing system framework, numerous scholars have discussed the continuous use(repurchase behavior) as the measures of success (Zaremohzzabieh, et al., 2016).

Online retailing consumer shopping experience is based solely on online retailing informationbecause of a lack of physical contact (McKinney, Yoon & Zahedi, 2002). Therefore,information as well as system and service quality may influence customers’ satisfactionduring the information-search stage and shoppers’ purchase decisions (Lin, Wu & Chang,2011). Many scholars found that satisfaction is one of critical factors influencing thecontinued purchase intentions (Cenfetelli, Benbasat & Al-Natour, 2008; Devaraj, Fan &Kohli, 2002; Holloway, Wang & Parish, 2005; Hsu, Yen, Chiu & Chang, 2006; Khalifa &Liu, 2007; Molinari, Abratt & Dion, 2008; Tsai & Huang, 2007; Yen & Lu, 2008; Zboja &Voorhees, 2002). In e-commerce context, DeLone and McLone (2003) identified UserSatisfaction as an important means of measuring customers’ opinions of an e-commercesystem.

Online consumer repurchase behaviour has attracted considerable attention in recent years,partly because it is an indication of online customer retention and it serves as a means ofgaining competitive advantage (Tsai & Huang, 2007). When a customer is satisfied with aparticular online retailing service provider, he or she is more likely to shop there again(Khalifa & Liu, 2007). Therefore, concepts of both customer satisfaction and customerretention have become increasingly important to online and off-line businesses. It isimportant to understand the factors that drive consumers’ satisfaction and their choice of theonline retailing channels (Devaraj, Fan & Kohli, 2002 cited in Lin, et al., 2011)

Based on the IS and marketing literature, Wang (2007) re-specified and validated amultidimensional model for evaluating assessing e-commerce systems success of whichonline retailing system is a type. The validated model consists of (modified by thisresearcher) six dimensions: Information Quality, System Quality, Service Quality, PerceivedValue, Online Retailing Consumers Satisfaction and Intention to Repurchase. However,Wang’s (2008) study focused on combining the updated DeLone and McLean model withSeddon’s (1997) perceived usefulness, the marketing literature, and with Davis’s (1989)TAM. He then re-specified and validated a new modified model by replacing Seddon’s(1997) perceived usefulness with perceived value, explaining that perceived value is a morereliable and comprehensive measure of net benefits. In addition, Wang (2008) used intentionto reuse (that is repurchase) as a success measure of e-commerce systems, suggesting thatincreased user satisfaction would lead to increased intention to reuse. In addition, Wang(2008) stated that customer intent for future use should be a more precise measurement of e-commerce system success net benefits than current customer use or initial use of a system.

Ho, et al. (2013) in their own study investigated into factors affecting the success of E-serviceusing a research model grounded on the Updated DeLone and McLean Information SystemSuccess Model. They included fourteen factors originated from four constructs, that is,

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system quality, information quality, service quality, and vendor dimensions in the researchmodel. Using the online music subscription industry in Hong Kong as the platform ofinvestigation, they examine the associations between these four constructs and customerpreference in the online music subscription service industry in Hong Kong. Whereas, Lin, etal. (2011) pointed out that information quality, system quality, service quality, product quality,delivery quality and perceived price have been identified and taken as the antecedents of usersatisfaction.

Following the propositions of prior researchers, this study concentrates on the online retailingconsumers as the stakeholders, and therefore measures the net benefits from users’ (that isonline retailing consumers’) perspective. Consequently, the net benefits in this studyrepresent online retailing consumers repurchase behavior as a result of satisfaction derivedfrom purchasing online in the context of online retailing service providers. Also, followingthe same conceptualization method of Wang (2008), Lin, et al. (2011) and Ho, et al. (2013),this study applied the updated DeLone and McLean model of the year 2003 as a frameworkto identify factors for evaluating online retailing service providers by online retailingconsumers. Furthermore, the present study, too, holds the key to unraveling how these factorsmay influence online retailing consumers’ satisfaction with regards to repurchase andswitching behavior

It is suggested in this study that the quality of the information, system and service of theonline retailing service provider determine the consumers’ intention to use, their actual use,intention to continue to use and their satisfaction with the online retailing service provider.The more satisfied they are with the online retailing service provider, the more consumerswill continue to use it, and this determines the benefits that they obtain from using it. Thebenefits then reinforce the consumers’ intention to repurchase from the online retailingservice provider. Customer satisfaction is one of the main objectives in increasing sales percustomer. The aim is for consumer to continue to repurchase from online retailing serviceprovider based on consumer satisfaction surveys that would measure system quality, servicequality, and information accessibility of the consumers (Petter, DeLone & McLean, 2008).

2.1.3 Expectation-Confirmation Model (ECM)

Expectation Disconfirmation Theory (EDT) or Expectation Confirmation Theory (ECT) wasfirst articulated by Oliver (1980) in the context of marketing decision-making and was builtupon the basis of cognitive dissonance theory (Alawneh, Al-Refai & Batiha, 2013). Thistheory established the basic framework for evaluating the relationship between generalconsumer satisfaction and post-purchase behaviors and was widely used in different productpost purchase and service continuance contexts by researchers, to explain consumers'satisfaction, post purchase behavior (e.g. repurchase, complaining) and service marketing(Bhattacherjee, 2001a; Yang, Lu & Chau, 2013, Alanazi, 2013). ECT focuses in particular onhow and why user reactions change over time and involves four primary constructs:expectations, perceived performance, disconfirmation of beliefs, and satisfaction (Yahya,Arshad &Wahab, 2009)

Alanazi (2013) remarked that the predictive ability of this theory, in terms of consumerbehavior before, during, and after purchase, has been demonstrated and affirmed over a widerange of product re-purchase and service continuance contexts, including restaurant service(Swan &Trawick 1981), automobile repurchase (Oliver 1993), camcorder repurchase(Spreng, MacKenzie & Olshavsky, 1996), business professional services (Patterson, Johnson

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& Spreng, 1997), and institutional repurchase of photographic products (Dabholkar, Shepard& Thorpe, 2000). Wang, Zhao, Sun, Zheng and Qu (2016) applied the expectationconfirmation model in order to examine the factors that influence the intentions of users tocontinue using social networking site and found out that in continuance intention, perceivedusefulness seems to be an important factor.

Although ECT has been widely used to study consumer satisfaction, post-purchase behavior,and service marketing (Bhattacherjee, 2001a; Oliver, 1980; 1993), Bhattacherjee (2001a,cited in Hung, Chang & Hwang, 2011) highlighted three major issues of ECT. First,consumers’ consumption experience may change their expectations, and these changes mayimpact their subsequent cognitive processes. Second, ECT studies involved varying andconflicting conceptualizations of satisfaction constructs, which could reduce the predictiveability of ECT. Third, differing conceptualizations of expectation have emerged across ECTstudies. Some researchers explain expectations as pre-consumption beliefs about overallperformance and operationalize it as anticipated performance.

The assumption of ECT/EDT seems to be that satisfaction is derived from product or serviceperformance. The same holds in the IS literature where satisfaction is based on ease of use,usefulness, service quality, and information quality (Ong, Day & Hsu, 2009). Satisfaction is aperson’s attitude toward a variety of factors of a situation affecting the person’s subsequentre-purchase intention and behavior. The literature on IS user satisfaction follows the traditionof ECT/EDT and mostly describes object-based beliefs and attitudes (Wixom & Todd, 2005)such as content relevance, accuracy, and timeliness.

Sørebø, Andreassen and Karlsson (2005) remarked that according to ECT, consumers definetheir repurchase intentions by determining whether the product or service meets their initialexpectations. Shoppers’ comparisons of perceived usefulness versus their original expectationof usefulness influence their continuance intentions (Oliver 1980; Bhattacherjee, 2001a). Ifuse meets the initial expectation and leaves the consumer satisfied, the consumer experiencespositive intentions to repurchase (Oliver 1980; Anderson & Sullivan, 1993).

Venkatesh et al. (2003) suggest that usage and continuance intentions may depend oncognitive beliefs about perceived usefulness. Gefen (2003) also indicates that perceivedusefulness reinforces online shoppers continuance intention, such that when a person acceptsa new information system, he or she is more willing to modify practices and expend time andeffort to use it (Succi & Walter, 1999). However, consumers who are dissatisfied with prioruse may continue using an e-commerce service if they consider it useful (Bhattacherjee,2001a).

Thus, since ECT ignores potential changes in initial expectations following the consumptionexperience and the effect of these expectation changes on subsequent cognitive processes(Bhattacherjee, 2001a) and given the congruence between the continuance usage of an IS andthe repeat purchase behaviors of consumers, Bhattacherjee (2001a) suggested the use of ECTto explain the continuance intention of users in relation to IS but borrowed the concept ofperceived usefulness (because of its dominant influence) from the TAM to replace expectedusage and modified the ECT to suit the context of Information Systems continuance use andgenerated the Expectation Confirmation Model (ECM). Bhattacherjee (2001a) empiricallytested an ECM of e-banking service continuance and showed that the ECM could beapplicable in an IS context. Since then, the ECM has been widely applied to research IScontinuance. Bhattacherjee (2001a) states that user’s continuance decision in using an

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information system is similar to the consumer’s repurchase intentions (Praveena & Sam,2013).

Furthermore, in a recent study by Premkumar and Bhattacherjee (2008), perceived usefulnessis found to be the strongest predictor of intention in TAM, and continues to be the strongestpredictor of continuance intention (over satisfaction) when TAM is combined with ECT,whereas satisfaction was dominant in ECT (Premkumar & Bhattacherjee, 2008). The relativedominance of usefulness explains its role as critical driver in continuance decisions(Premkumar and Bhattacherjee, 2008). Bhattacherjee (2001a) put forward the ECM thatincludes four factors: confirmation of expectations, perceived usefulness, satisfaction andcontinuance intention. Confirmation of expectations refers to a cognitive evaluation of an ISat the post-adoption stage (Bhattacherjee, 2001a).

Figure 2.4 The Expectation-Disconfirmation Paradigm in IS Research

Source: Adapted from Bhattacherjee (2001a)

ECM replaces pre-consumption expectations with post-consumption expectations andpostulated that satisfaction is a function of expectations and confirmation (Sørebø,Andreassen & Karlsson, 2005; Liao, Palvia & Chen, 2009); thereby explaining the consumerconsumption decision in the post-purchase process (Spreng et al., 1996). Based on ECM, thefollowing three variables affect the intention of users to continue using an IS: Level of user’ssatisfaction, perceived usefulness which represents the post-adoption expectation and extentof confirmation of user’s initial expectation.

As noted by Hayashi, Chen, Ryan & Wu (2004) the ECM differs from ECT in three ways:First, while ECT examines both pre-consumption and post-consumption variables, the ECMfocuses only on post-acceptance variables. ECM regards effects of any pre-acceptancevariables as already captured in both (dis)confirmation and satisfaction constructs. Second,ECT only examines the effect of pre-consumption expectations rather than post-consumptionexpectations, while ECM amends the ECT to include ex-post expectation. Third, the ex-postexpectation is represented by perceived usefulness in the ECM. Thus, ECM focuses onfactors that influence retention and loyalty, as the long-term viability of an IS and successdepends on the continued use of the IS system (Bhattacherjee, 2001a).

Thus, in the context of this study, the following concepts: Expectation, Perceived Usefulness,Confirmation, Satisfaction, and Online Repurchase Intention (which replaces Continuanceintention) were adopted to identify the factors that drives the repurchase behavior of onlineretailing consumers. In essence, using the ECM, this study proposes that online retailing

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Expectation

Repurchase Intention

SatisfactionConfirmationPerceivedPerformance

consumer’s expectations of online retailing service provider before purchasing online and thesubsequent confirmation (or disconfirmation) of these expectations after purchasing has asignificant influence on online retailing consumer satisfaction. Online retailing consumersatisfaction, in turn, has a critical influence on the likelihood of repurchase intentions fromthe specific online retailing service provider.

In other words, when an online retailing consumer’s expectations of the online purchases areconfirmed (expectation confirmation) it leads to satisfaction, but if the online retailingconsumer’s expectations are non-confirmed (expectation disconfirmation) it promotesdissatisfaction. Satisfied online retailing consumers are more inclined to continue torepurchase with the specific online retailing service provider and dissatisfied online retailingconsumers are more inclined to switch to alternative online retailing service providers orpurchase offline. Since the long-term success of information systems (IS) depends oncontinued usage rather than mere acceptance (Bhattacherjee, 2001a), understanding thefactors that affect online retailing consumers’ intention to continue to repurchase onlinewould help online retailing service providers develop the best strategies to increasepatronage.

As Sørebø, Andreassen and Karlsson (2005) have noted that the Expectation-ConfirmationModel is promising in its explanation of a critical IS like online retailing research issue, thatis the users’ continuance intentions, further application and investigation of this framework isnecessary and moreover there has been limited activity in ECM, post-adoption behavior andIS continuance research (Bhattacherjee, 2001a; Bhattacherjee & Premkumar, 2004).Therefore, the current study is using ECM to identify factors used in post-adoption of onlineretailing by online retailing consumers’ to evaluate online retailing service providersespecially within the Nigerian online retailing industry. This study proposes the use ofextended expectation confirmatory theory (Bhattercherjee, 2001a) that integrates thetechnology acceptance model and extension of updated Delone and Maclean model (Ho etal., 2013) to identify factors used by online retailing consumers to evaluate online retailingservice providers with regard to repurchasing and switching behavior intentions in Nigeria.This research moves beyond online retailing adoption intentions and includes factorsaffecting online repurchasing continuance.

An overview of Analytic Hierarchy Process (AHP) AHP is framework of logic and problem solving that spans the spectrum from instantawareness to fully integrated consciousness by organizing perceptions, feelings, judgments,and memories into a hierarchy of forces that influence decision results; and is a structuredmethod to elicit preference opinion from decision makers (Saaty, 1983, 1988, 1990, 2006).This method is a systematic approach to complicated problem(s). It aims at quantifyingrelative priorities for a given set of alternatives on a ratio scale, based on the judgment of thedecision-maker, and stressing the importance of the intuitive judgments of a decision-makeras well as the consistency of the comparison of alternatives in the decision-making process(Saaty, 1980). Furthermore, it is a theory of relative measurement concerned with derivingdominance priorities from paired comparisons of homogeneous elements with respect to acommon criterion or attribute and these paired comparisons are used to derive normalizedabsolute scales of numbers whose elements are then used as priorities (Saaty, 1980, 2000,2006).

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Also, the AHP approach involves decomposing a complex and unstructured problem into aset of components organised in a multilevel hierarchical form and has been reputed to besuitable for analyzing complex systems, extracting several alternatives, and then comparingthe selected options (Saaty, 1971, 1982). It is often conducted with a small group of expertswho are capable of performing subjective pair-wise comparisons of decision criteria (Saaty,1980). Hence, Saaty (1977, 1994, 1996) pointed to the fact that it is a decision-makingmethod for prioritizing alternatives when multi-criteria must be considered and is capable ofincorporating all observed dependencies emphasizes and it uses elaborate structures torepresent a decision problem (Saaty, 2006). It is an approach for structuring a problem as ahierarchy or set of integrated levels.

Consequently, Saaty (1980) remarked that AHP is based on a hierarchy of evaluation criteria,and uses paired comparisons of alternatives with respect to these criteria. Gradations in thecomparisons are expressed using numerical values. The ranking of the alternatives is obtainedfrom an eigenvalue computation on a suitably aggregated matrix. It is a decision-makingmethod for prioritising alternatives when multiple criteria must be considered (Saaty, 1996;1977). This approach allows the decision maker to structure problems in the form of ahierarchy or a set of integrated levels, such as the goal, the criteria, sub-criteria and thealternatives while the set of alternatives are at the bottom level, the main goal at the top and anumber of the sets of criteria, subcriteria, factors, actors, et cetera at the intermediate levels(Saaty, 1980; Saaty, 1994a).

While Oluwafemi, Balogun and Adekoya (2012) saw AHP as an utility generation techniquewhich provides a systematic means to quantify decision makers perception in situationsinvolving primarily qualitative criteria; Reynolds and Jolly (1980) pointed out that AHPframes a decision as a hierarchy, an organisational framework many people are alreadyfamiliar with and easy to explain to those who are not. All inputs consist of comparisonsbetween just two decision elements at a time; pairwise comparisons like these are generallyconsidered to be one of the best ways to elicit judgments from people. Sato (2001) describedAHP as a support system for decision making model which deals on subjective issues,making it difficult to determine theoretically whether or not each decision-maker’ preferencefor alternative derived from the AHP accurately represents each decision-maker’s feeling.Taking into account that the AHP deals with not only objective issues that can be quantifiedbut also subjective issues that do not have theoretical values, the effectiveness of a scale mustbe verified empirically through actual applications to subjective issues.

The AHP according to Winston (1994) is a methodology and a group of methods for multi-criterion decision making method used to measure the relative importance weighting for eachcriterion and transforms the pairwise comparison scores into weights of different attributesand priorities of all alternatives on each attribute to obtain the overall ranking of alternatives.AHP is a powerful tool that can be used to decision-making in situations involving multipleobjectives (Winston, 1994). It enables decision makers to derive ratio scale priorities orweights from experience, insight, intuition, and quantitative data. In so doing, it not onlysupports decision makers by enabling them to incorporate both objective and subjectiveconsiderations in the decision analysis (Dye, Forman & Mustafa, 1992). Thus, AHP resultsof verifiable by a rigorous mathematical theory with judgments consistency ratio capacity(Chuang, 2001).

Schniederjans and Garvin (1997) referred to the AHP as a highly flexible decisionmethodology that can be applied in a wide variety of situations and is typically used in

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decision situations which involve selecting one (or more) decision alternatives from severalcandidate decision alternatives on the basis of multiple decision criteria of a competing orconflicting nature. Also, Alexander (2012) affirmed that the AHP was developed to optimizedecision making when one is faced with a mix of qualitative, quantitative, and sometimesconflicting factors that are taken into consideration; and that AHP has been very effective inmaking complicated, often irreversible decisions. In the eye of Khawaja (1999), the analytichierarchy process is a multi-criteria decision-making method allowing decision makers tomodel a complex problem in a hierarchical structure which consists of the goal, objectives(criteria), sub objectives, and alternatives.

AHP, according to Marufuzzaman and Ahsan (2009), is a decision tool used to solveunstructured problems and complicated situations in economics, social and managementsciences. AHP is also identified as a technique to rank a finite number of alternatives basedon a finite number of criteria (Okur, Nasibou, Kilic & Yavuz, 2009). Thus, it provides ameans of decomposing the problem into a hierarchy of sub problems which can more easilybe comprehended and subjectively evaluated. The subjective evaluations are converted intonumerical values with the use of a nine-point scale, reciprocal matrices, and the establishmentof ratio scale estimates through the solving of eigenvalue problems and processed to rankeach alternative on a numerical scale (Perera & Sutrisna, 2010).

Sekozawa, Mitsuashi and Ozawa (2011) identify AHP as a method of quantifying humanperception and taste. It structures a complex problem in a simpler hierarchical form andevaluates the quantitative and qualitative factors in the more systematic manner undermultiple criteria environment in confliction (Marufuzzaman & Ahsan, 2009). Chan and Chan(2010) imitate the hierarchical structure of AHP to the figure of the tree where the objectivesrefers to the root, the alternatives are the leaves. Golden (1989) even saw AHP as a suitabletool for group decisions.

Given that multiple factors and alternatives must be taken into account, the use of a multi-objective model is extremely useful. AHP is an adequate decision making model for this case.Thus, the AHP is used in this study because it is a well – established theoretically soundmethodology that online retailing service providers can easily adapt for the purpose ofidentifying and prioritizing the factors used by online retailing consumers in evaluating andrepurchasing on their platform. The successful application of AHP described in this paperdemonstrates the usefulness of the method and provides insight into the relative importanceof strategic objectives for the online retailing industry.One of the objectives of this thesis is to use AHP to identify which factors are important forconsumers for evaluating the online retailing services. Moreover, we are also interested instudying which online retailing service provider is preferred by consumers. Hence, as AHP isa multicriteria decision making tool, it seems relevant to use this tool for identifying the mostimportant factors. AHP is an appropriate approach for the current research, because itcombines all of the mentioned factors into a model and quantitatively measures theimportance of consumers’ preferences.

Also, there are several methods and models that can be used to measure consumer’sperceptions, preferences and behaviours; in the context of the current study, the AHP appearsto be an appropriate method to be used. Note that, customers' expectations are many timesunclear and ambiguous; moreover, human assessment and evaluation of qualitative attributesare always subjective and imprecise. Determining the correct importance weights for factors

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used in evaluating online retailing service providers based on consumers’ preferences isessential since they directly affect the consumers’ intention towards repurchasing from onlineretailing service providers. Determination of the importance of these factors also enablesonline retailing service providers and online retailing service/application developers to designand develop services that fit consumer preference.

Hence, the reason for choosing AHP is that it seeks to translate subjective value judgments ofonline consumers into qualitative measures that can be used to prioritize factors used inevaluating online retailing service providers in order to assist these providers in meeting theirconsumers expectation, increase repurchase and reduce switching behaviours. Also, theprocess yielded by the AHP can be used to stimulate ideas for creative course of action and toevaluate their effectiveness.

Considering the characteristics and applications of the AHP, the technique is foundconvenient in prioritizing the factors used by online retailing consumers’ to evaluate onlineretailing service providers in the Nigerian online retailing industry that induced therepurchasing or switching behavior among these online retailing service providers. Thus,using the AHP, the study, therefore, contributes not only to the acceptance and adoption but torepurchase research by prioritizing factors used in evaluating and repurchasing online byconsumers.

The Conceptual Framework for the Study

Understanding and predicting consumers’ behavior has been of particular interest tomarketers for many years. Consumers are psychological beings who become very rationalwhenever it has to do with making choices that lead to patronising products or services thatprovide chain of alternatives (Gbadeyan et al., 2015). This decision behavior, as affirmed byKim, LaVetter & Lee (2006), is more evident in post-purchase situations since the consumershave consumption experience and are already familiar with those indications so theconsumers are less likely to make repurchase decision under the influence of extrinsicindications. Ye et al. (2006) indicated that user switching behaviour represents a form of post-adoption behaviour.

While online customer retention is particularly difficult as current customers have variousonline and offline options from which to choose, customer loyalty is critical to the onlineretailing service providers’ survival and success. For this reason, it is important for onlinebusinesses in the online market to understand not only their consumers’ perceptions, onlineand offline, but also which factors influence their repurchasing decisions. With a betterunderstanding of the factors that play into the consumer’s decision making process whilecompleting transactions online or offline, retailers and businesses can better preparethemselves to serve their customers in either of these shopping venues.

Based on the complementary nature of TAM, ECT and Delone and Mclean InformationSystem Success Model, the extended expectation confirmatory (ECT) (Bhattercherjee, 2001a;Davis et al., 1989) and the Ho et al. (2013) extension of updated Delone and Maclean modelwere used to identify factors used by online retailing consumers to evaluate online retailingservice providers after purchases in the post-adoption of online retailing. These factors,perceived ease of use, perceived usefulness, perceived enjoyment, trust, perceived risk, brandcredibility, perceived reliance, system quality, service quality, and information accessibility of

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the consumers, are likely not to be of equal importance to online retailing customers'decision. As it is important to meet customer expectations on factors that are important tothem if the company wants to remain in business since customers are the essence of onlineretailing service providers business.

This study proposed a combined application of two operations research models (AHP andMarkov Chain Process) to identify, weigh and prioritize the various factors used by onlineretailing consumers to evaluate online retailing service providers as well as model and predicttheir behavioural intentions decisions in the Nigerian online retailing industry. The factors forevaluating online retailing service providers were elicited from both literature andpreliminary interviews granted to the consumers of online retailing in Nigeria. These werepresented for the respondents to make a pairwise comparison regarding the extent to whicheach of the factors in comparison to its pairs would lead them to a repurchase or switchbehavioural intention decisions. While the AHP evaluation process was able to assess post-purchase expectation of the online retailing consumers with regards to the identified factorswhether the online retailing service provider meets the expectation of the customer in termsof their satisfaction or dissatisfaction as the case may be; the combination of both the MarkovChain (Discrete) and Markov Jump (Continuous) processes predicts the behavioral intentiondecision of online retailing consumers after their post-purchase evaluation of online retailingservice provider. The implication of the decision taken by majority of the online retailingconsumers subsequently determines what happens to online retailing service providers. Thus,a switching behavioural intention decisions will bring about decline in patronage of onlineretail channels, decline in revenue, decline in profit, discouragement in online retail channels,switch to offline and referral (badmouth). All these may affect the company's image andreputation and ultimately reduces its market share in the long run. Whereas, a repurchasebehaviuoral intention decision will lead to an increase in patronage of online retail channels,increase in revenue, increase in profit and confidence in online retail channels. All these willeventually improve the company's image and reputation and ultimately increases its marketshare in the long run. As subscribers who retain a network will encourage others to be on thesame platform without being paid directly by the telecommunication operator to do so. Theeventual outcome of these interactions would lead to the development and performance of theplayers (online retail service providers) in the Nigerian online retailing industry. Thus, figure2.4 shows the pictorial representation of the conceptual framework.

23

Consumer Online Retailing Evaluation

Repurchase online BehaviorIncrease in Patronage online channelsIncrease in Revenue.Increase in profitConfidence in online channels

Decline in Patronage online channelsDecline in Revenue.Decline in profitDiscouragement in online channelsSwitch to Offline Referral (badmouth)

Purchase Offline Behavior

Development and performance of the players in the Nigerian online Retailing industryNegative outcome dissatisfied with Evaluation

Behavioural Intentions DecisionMarkov Chain and Markov Jump Process

Markov Jump and

Application of Operations Research Models

AHP

Examining Predict

Evaluate, weigh prioritize

Conceptual framework

Figure 2.4: Conceptual Framework.Source: Author conceptualization (2017)

24

Positive outcome satisfied withEvaluation

Methodology

Online retailing consumers in Lagos State were taken as the study sample. As noted above,since the population is large, there is a need to identify a readily available area (clusters)where these set of Millennials can be readily accessible because online shopping is mostlycarried out in confers of their homes or offices (Jusoh & Ling, 2012). Thus, MBA part timestudents were selected for this study since to be on a MBA part time programme, a studentwill definitely fall within the age bracket above since he/she would have spent at least 4 yearsin the university after entering the university at the minimum age of 16 years(Exced.ucoz.com, n. d.) and work for at least 3 years before applying for the MBAprogramme (Lagos Business School, n. d.; University of Lagos, 2009) amounting to aminimum of 23 years. Also, since the study intent to use self-administered questionnaires fordata collection and one of the demerits of questionnaire is that it is for only literates (Kothari,2004).

Thus, the purpose of using the MBA part time students is that it is a readily accessible clusterof millennials and to collect empirical data conveniently and on time, who have diverse socialeconomic background (different working experience, organizations, social status, income,age). Hence, they are in a position to give meaningful responses to the questionnaires. Moreso as time and resources are also kept in mind.

The study, in order to select the sampled universities in Lagos state, used the fiveadministrative divisions format namely Ikeja, Badagry, Ikorodu, Lagos Island and Epedivision. A multi-stage sampling design was employed to select four out of five universitiesin Lagos state. The first stage was the selection of all the universities in Lagos irrespective oftheir ownership structure. In the second stage, four universities were selected from the earlieridentified five across the five divisions in the state.

For the purpose of this study, the sample size of the target group who participated in thisstudy was calculated by using the model developed by Israel (2009) to determine the sample

size if the population is large which is expressed as:n0=

z2 pq

e2

Where: n0 = sample size, Z = value of the normal curve that cuts off an area α at the tails (1 –α equals the desired confidence level, e.g., 95%), e = the desired level of precision, p = theestimated proportion of an attribute that is present in the population, and q = 1-p. Therefore,the online retailing coosumer’s sample size for the study at 95% confidence level and 1%precision is denoted by; Z = 1.96, p = (0.5 maximum variability assumed) since actualvariability in the proportion is not known), q = 0.5. e = 0.05. Therefore, the sample size forthe customers become

0=¿(1.96 )2 (0.5 )(0.5)

(0.05)2

n¿

= 380.25 = 380 customers of online retailing service providers

must at least be sampled.

25

Results and Discussion

Please kindly note that this paper is still a work in progress. The researcher have not finallycollected the data but it is in progress and might be ready before the final submission of the conference.

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