While e-commerce has been widely cited as the new marketing frontier, thus necessitating the need to deliver seamless shopping experiences across various online channels to achieve success, very few firms have the well withal to clearly tie customer experience investments to marketing outcomes. Theoretically speaking, the understanding of the drivers and outcomes of online shopping experience especially group behavior is imprecise. Therefore, this paper aims to investigate the drivers and outcomes of online shopping experience (OSE).
A combination of netnography and conversation analysis was used on a pool of qualitative data generated from the Facebook page of a leading online retailer that has online presence in 11 African countries.
Two broad categories of OSE under seven drivers and five distinct behavioral outcomes of OSE emerged from the study. The two categories of OSE drivers, though unique, widely fit into the existing frameworks of OSE. The study also indicates that shoppers seize other shoppers’ reviews as a suitable platform to engage in a wide range of behaviors.
The main theoretical implications include the following: complaint handling is not only a behavioral construct but also a stimulator/driver of online shopping experience; consumer behavior is stimulated more by cognitive drivers; trust is an outcome of OSE which leads to not only electronic word of mouth but also external response to service failure; and shoppers perceive external response to service failure as the last resort and this last resort can be activated by regrets and poor internal response to service failure. The major limitation of this study is that the proposed conceptual model was not empirically tested. Future research is required to validate the model.
The managerial implications of the findings are that in addition to providing superior shopping experience through enhancing the drivers of OSE identified in this study, online retailers must work assiduously to reduce incidents leading to service failures and promptly undertake service recovery actions whenever service failure occurs. Online retailers especially those operating in emerging markets will therefore benefit from their service recovery investments if they proactively install processes that enable them to promptly and satisfactorily recover failed services.
This paper contributes to service science research by proposing a unique belief-attitude-intention model of the drivers and outcomes of OSE on a relatively underexplored field. The proposed conceptual model advances the stimulus-organism-response framework, theory of planned behavior, satisfaction theories and shopping behavior literature in several directions.
Izogo, E. and Jayawardhena, C. (2018), "Online shopping experience in an emerging e-retailing market", Journal of Research in Interactive Marketing, Vol. 12 No. 2, pp. 193-214. https://doi.org/10.1108/JRIM-02-2017-0015Download as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited
The retail sector has undergone and will continue to undergo transformation in the coming years, especially as multichannel retailing has become the dominant operating business model for most retailers (Doherty and Ellis-Chadwick, 2010). E-commerce grew at a rate that has even outpaced traditional channels of retailing. In the USA, for instance, Forrester research predicted that online retail sales will hit $370bn by 2017, up from $231bn in 2013 (Lomas, 2013). In South Africa, 51 per cent of individuals with internet access shop online; in Kenya, 18-24 per cent individuals shop online (Internet Business Statistics, 2016). In Nigeria, online shopping expenditure grew from N50 to N78bn between 2010 and 2012 (Phillips Consulting, 2014). With digital tools rapidly redefining the way value is created and delivered (Evans and Cothrel, 2014), the best way to exploit the burgeoning opportunities which online retailing offers is to enhance customer experience (Hoffman and Novak, 1996). Thus, retailers that want to keep their current customers and attract future ones are therefore challenged to explore the potentials of the virtual storefront (Tiago et al., 2015).
Unfortunately, 45 per cent of leading-edge companies “find tying customer experience investments to business outcomes very difficult” (Harvard Business Review, 2014). Whereas facts show that negative shopping experiences result in negative outcomes (Afshar, 2015), previous shopping research focused mainly on studying patronage behavior and developing shopper typologies (Compeau et al., 2016). Little research simultaneously investigated how online shoppers behave individually and in group environments. Although customer experience and its behavioral outcomes have been studied through interview methods (Trevinal and Stenger, 2014), emerging consensus technique (Klaus, 2013), survey-based approaches (Gentile et al., 2007; Ahmad, 2002) and experimental methods (Cyr et al., 2007), these approaches in one way or the other suffer from respondents’ inhibition. Accordingly, they may not reflect the true state of customer experience and behavior in group situations. But the consumption experience theory emphasizes the utility of group behavior (Verhoef et al., 2009). Although research abounds in this area, economic and socio-cultural realities differ across countries (Flambard-Ruaud, 2005 cited in Izogo et al., 2016). Thus, it is difficult if not impossible to rigorously apply evidences from developed markets in emerging markets. The adaptation of existing models of customer experience to meet the needs of emerging markets is therefore becoming urgent and can largely serve to advance services science literature in general and experiential consumption literature in particular. We selected the African continent because within the past five years, the growth of internet penetration and online shopping has been geometric leading to retail format blurring. Thus, firms operating within the continent need to be guided on how to strategically respond to this shift. Additionally, although research has long emphasized the importance of customer dissatisfaction management and comprehensive complaint response strategy in stabilizing markets, satisfying customers, attracting long-term loyalty and profitability in the online environment (Cho et al., 2002), Cho (2011) indicated that few studies examined the negative aspects of satisfaction such as dissatisfaction and complaint behavior especially in the online context. Drawing on these evident gaps, this paper therefore sets out to investigate the drivers and the outcomes of online shopping experience (OSE) while also providing deeper insights on how individual consumers behave in group situations through a naturalistic and unobtrusive qualitative research method. The paper seeks to contribute to service science literature by exploring the unique drivers of OSE and broadening its outcomes to include individual and group behavior through a combination of two unique qualitative research methods. The rest of this paper is organized as follows. We first explored the meaning and dimensions of OSE and thereafter examined the theoretical foundations of the paper which led to the research questions. This was followed by the methodology, results, summary and discussions. The paper concludes with contributions, limitation and future research directions.
Meaning and dimensions of online shopping experience
It has become imperative for academics and practitioners to explore customer experience in the online context (Verhoef et al., 2009) because current conceptualizations of the construct remain disjointed. Given the depth of customer experience research, definitions of OSE abound. OSE is variously perceived as the frequency of online purchases (Chen et al., 2009), summation of all the clues that customers detect in the buying process (Meyer and Schwager, 2007), activities spanning prior, during and post-purchases (Verhoef et al., 2009), engaging customers in a personal way (Pine and Gilmore, 1999) while some definitions emphasize the customer service perspective (Klaus, 2013). One apparent shortfall in the above perspectives of OSE is that they all view customers as passive receptors of value. The dominant position in the literature is that experience is co-created. Thus, recent definitions of shopping experience are explicit about value and have gone further to perceive customers as participants in value creation and by implication experience co-creation (Bolton et al., 2014; Gentile et al., 2007). We draw on the foregoing to define OSE as a holistic, internal and subjective responses that ensue when a customer dynamically engages with a firm through a variety of its online channels such as company websites, online community sites, blogs, chat rooms and so on as well as interacting with other customers to co-create value.
Just as it is nearly impossible to find a dominant definition of OSE, its components have also been widely investigated. But whilst a wide range of perspectives abound, there tends to be an agreed structure of the components of OSE. Three related but varying perspectives can be identified in the literature. The first perspective is the conceptualization of OSE as “flow” (Hoffman and Novak, 1996). This perspective has found wide application in previous research (Ding et al., 2010; Bridges and Florsheim, 2008) but its greatest shortfalls are that flow is the most elusive construct to measure and it tends to be dominantly cognitive, whereas shopping experience comprises both cognitive and affective elements. The second perspective is the conceptualization of shopping experience as comprising both cognitive and affective elements (Rose et al., 2012) which respectively correspond to utilitarian and hedonic experience in both earlier and more recent studies (Trevinal and Stenger, 2014; Babin et al., 1994). While this is an improvement over the first perspective, it fails to account for customer-to-customer interactions. The third and apparently the broadest conceptualization of OSE highlights the multi-dimensional nature of the construct. Gentile et al. (2007) proposed six components of customer experience including sensorial, emotional, cognitive, pragmatic, lifestyle and relational experiences. This is very similar to Verhoef et al.’s (2009) conceptual portrayal of the components of customer experience. Pine and Gilmore’s (1999) experiential realms have been successfully applied in the online context (Jeong et al., 2009). Hsu and Tsou (2011) also applied Schmitt’s (1999) model in the online blog context. The expanded conceptualization of OSE unlike the first two perspectives, emphasized the role of the customer himself/herself and other customers in an individual customer’s shopping experience. Thus, it remains at present, the most insightful and appropriate conceptualization of OSE. Adopting this perspective, we argue that OSE is a multidimensional concept comprising five elements which according to Schmitt (1999), include sensory, emotional, cognitive, behavioral and relational components. As the components of OSE is multifaceted, its drivers also ought to be diverse. However, because of the context-specific peculiarities that we highlighted earlier, it is pertinent to undertake context-specific studies of the drivers and outcomes of OSE, especially from new theoretical perspectives. In the section that immediately follows, we explore the theoretical foundation of the paper and deduce the research questions.
Theoretical foundation and research questions
Some of the influential theories of consumer behavior include the theory of reasoned action (TRA) (Fishbein and Azjen, 1975), its successor, the theory of planned behavior (TPB) (Ajzen, 1985, 1991), and the technology acceptance model (TAM) (Davis et al., 1989). The first two theories posit that customers’ attitude toward an episode is a function of customers’ beliefs or rational cognitive assessment of the episode. TAM is also similar to the belief-intention theories but its perspective aligns more suitably with the stimulus-organism-response (SOR) framework which suggests that the shopping environment contains stimuli (S) that affect organisms (consumers; O) and result in approach or avoidance response (R) behaviors toward the store and in behaviors like store searching, intention to purchase, and repurchase intention (Mehrabian and Russell, 1974). The central idea that ties these theories together is that they generally fit into the belief-attitude-intention (B-A-I) framework proposed by (Froehle and Roth, 2004). In the paragraphs below, we link shopping experience literature to the aforementioned theories and postulate the research questions from an in-depth review of extant gaps found within this link.
When customers shop online, they experience brands, companies, services and other customers. Drawing on the definition of OSE put forward earlier, we argue that shoppers’ experiential responses have activators or what can also be referred to as drivers or stimulators. Jones (1999) identified a broad range of retailer and consumer factors that trigger utilitarian and hedonic shopping experiences. Jeong et al. (2009) found that product presentation features (e.g. zoom feature) has a significant positive effect on OSE. The drivers of experiential/hedonic and utilitarian value have been suggested (Bridges and Florsheim, 2008), whilst previous research (Vieira, 2013) specifically indicate that website features such as color, crowding, fragrance, shopping value and layout are activators of internal responses. Thus, consistent with our formerly enunciated definition of OSE, OSE is what characterizes the internal responses that ensue as shoppers navigate a website and process its features such as color, fragrance, crowding, layout and so on because these features drive value. According to the service-dominant logic of marketing, value is actor-centered, experiential, systemic in nature and co-created within a social context (Bolton et al., 2014; Vargo and Lusch, 2008). Consistent with the B-A-I and the SOR framework, therefore, factors that could possibly shape OSE such as the ones identified above can be perceived as stimulators because they influence shoppers’ belief in the shopping activity. Thus, as shoppers navigate through the websites of a company, their cognitive assessment and emotions can be altered by the stimulating effects of the website attributes or the belief they develop about that website.
OSE has been linked to a variety of consumers’ behavioral outcomes. For instance, regrets have been supported as one of the outcomes of consumer experience (Oliver, 1997; Simonson, 1992). Consumers regret some of their choices and the amount of regret associated with different kinds of decisions whilst the regret and responsibility that people feel become greater when they deviate from the norm and consequently discover that the choice they made was suboptimal (Simonson, 1992). None of these can occur without brand experience. In a compelling systematic literature review of the business-to-consumer e-retailing market, Rose et al. (2011) indicate that satisfaction and repurchase intention are the two most widely cited consequences of online customer experience. Within the context of online shopping, this link is widely supported (Rose et al., 2012; Hsu and Tsou, 2011; Ding et al., 2010). In line with the SOR framework, B-A-I model and Rose et al.’s (2011) framework, OSE does influence marketing outcome variables especially satisfaction and repurchase intention. However, while studies investigating satisfaction is numerous, few investigated the negative sides of the construct and complaint behavior especially in the online shopping context (Cho, 2011). In this paper, analysis of consumer reviews will cover both the positive and the negative aspects of the consequences of OSE. Additionally, as it has been argued that customer experience is holistic (Verhoef et al., 2009) whilst online consumer reviews provide shoppers with indirect product experiences (Chu and Kim, 2011), the recruited narratives are a holistic portrayal of shoppers’ overall experience.
Other customers’ influences are part of what Meyer and Schwager (2007) described as indirect contacts that consumers initiate during purchase and takes the form of negative or positive word of mouth, online reviews and so on. Such indirect contacts influence consumers’ behavior either positively or negatively depending on whether the experience is positive or negative, and the potency of this link is widely supported in the consumption experience literature (Compeau et al., 2016; Klaus, 2013; Cyr et al., 2007; Jones, 1999). Drawing on the SOR framework, relational experience can be elicited through clues like photographs, speech, texts, personalized greetings, human audios and human videos. When these features are positively perceived by the online shopper, OSE is enhanced and this consequently leads to positive consumer behavior. Unfortunately, few studies investigated this all-important construct with evidence of lack of substantive literature in emerging markets. Additionally, a deeper understanding of how customers behave in group environments/situations is becoming urgent because most previous OSE studies adopted methods that are prone to respondents’ inhibition and non-naturalistic.
An important component of OSE outcome that has not gone unnoticed in the consumer behavior literature is electronic word of mouth (eWOM). eWOM refers to any comment made by a customer for a brand or service that is available to other customers and/or organizations through the internet (Lee et al., 2012). Chu and Kim (2011) classified eWOM into three categories: opinion-seeking, opinion-giving and opinion-passing, while Tiago et al. (2015) included opinion content as the fourth component of eWOM. Previous research (Lee et al., 2008) noted that eWOM especially negatively framed messages are more diagnostic for decision-making purposes than firms’ marketing communications. Thus, for firms to understand how this information should be managed to their advantage, they must be aware of how individual consumers behave in group situations. It is also important to note that firms will be better placed to recover service failures if they are conversant with individual behavior in group situations. Given that Ahmad (2002) found that recovered customers will be willing to continue patronizing the online shop and even recommend it to others if employees make sincere efforts to recover failed services, customers will be dissatisfied if their problems are not resolved and will even be outraged if service representatives refuse to listen and understand their complaints. Unfortunately, previous research explored this theme through less innovative methods. Additionally, in exception of the categorizations of eWOM pointed out above, an in-depth exploration of the components of eWOM especially from the group behavior perspective rarely exists in the eWOM literature. Consistent with previous research on the need for a deeper understanding of OSE (Klaus, 2013), as well as the gaps identified in this theoretical framework, this paper proceeds with the following key research questions:
What are the context-specific drivers of OSE in an emerging market?
What are the positive and negative outcomes of OSE?
The netnographic qualitative research method was used alongside a conversation analysis. Netnography which derives from research approaches used to investigate the field of social anthropology (Quinton and Harridge‐March, 2010) is the “written account resulting from fieldwork studying the cultures and communities that emerge from on-line […] communications, where both the field work and the textual account are methodologically informed by the traditions and techniques of cultural anthropology” (Kozinets, 1998, p. 366). Kozinets (1998) who pioneered and fronts the line of thought in this research approach argued that it is the best suited for the study of consumers’ behavior of internet cultures and communities. Compared to ethnography, interviews and focus groups, netnography is, simpler, faster, less expensive, unobtrusive and more naturalistic (Kozinets, 2002) because the researcher observes emerging cultures and communities in computer-mediated communications wearing “invisibility jacket”. Thus, offering significant insights on the consumption patterns of online consumer groups (Kozinets, 2002). Additionally, with netnographic research design, it is far simpler to recruit participants’ undiluted experiences. These advantages of netnography over traditional interviews method and ethnography informs our choice of the method for the study of the drivers and outcomes of OSE. Because of the textual discourse-oriented nature of netnographic observations, data were coded and analyzed using content analysis (Kozinets, 2002) and conversation analysis. According to Quinton and Harridge‐March (2010), the textual data used is usually large and collected from the field notes and artefacts of the online community that the researcher is investigating. Our objective here is to explore the underlying drivers and outcomes of OSE using the reviews published by customers about their experiences with online shopping, and not to observe the cultural behavior of the members of the community within the online community as might be the objective in a typical ethnographic research. The approach therefore mimics non-participant method of observation.
As one of the methods under the family of discourse analysis (Taylor, 2001 in O’Sullivan, 2010) which is grounded in ethnomethodology – the demonstration that everyday behavior is a function of the rules that the actors assume more or less unconsciously – conversation analysis is “an approach to qualitative data analysis which pays close attention to the details of language-in-use as a form of activity by and between speakers” (O’Sullivan, 2010, p. 20). Although conversation analysis is limited to the extent that it disregards out of context evidence whilst it is obsessed with form rather than content, its fundamental strength resides in the considerable investment of time and efforts that yields fresh perspectives on data and insights which might otherwise be unavailable (O’Sullivan, 2010). As defined by Wooffitt (2001, p. 49), conversation analysis is “a method for the analysis of naturally occurring interaction”. Thus, conversation analysis begins with data rather than preconceived assumptions of what the data should contain or mean as in experimental forms of data (O’Sullivan, 2010). The primary concern of conversation analysis “is to see language as an activity” (O’Sullivan, 2010, p. 21). As one of our research objectives is to investigate online group shopping behavior as they naturally occur in the social networking context, a conversation analysis was used. This technique was also adopted because of its efficacy in making sense of spoken conversations (Bryman and Bell, 2015) where participants possess the capability to create and manage meaning amongst themselves (O’Sullivan, 2010). The combination of both netnography and conversation analysis in this research is informed by three key reasons. First, both techniques study behavior as they occur in their natural settings and therefore deals with naturalistic and unobtrusive data. Second, the latter’s ability to enact meaning from naturally occurring spoken conversations makes it a powerful technique for uncovering group behaviors embedded in group conversations. Finally, in line with previous studies that used netnography (Quinton and Harridge‐March, 2010; Boulaire et al., 2008) and studies that used a combination of netnography and other qualitative methods (Pathak and Pathak-Shelat, 2017; Yuksel and Labrecque, 2016) to investigate the behavior of consumers in virtual communities, we argue that a combination of the approaches will yield deeper insights than both can do individually. This is because netnography helps to offset the overemphasis of conversation analysis in “form” by focusing also in “content” of conversations.
To develop the shopping experience drivers and outcomes, Kozinets’ (2002) five-step research protocol which reflects an adequate adaptation of the traditional ethnographic research process was thoroughly observed. First, a choice was made as to the most appropriate online community based on the research questions, activities in some online community sites, appropriateness of discussion topics and postings “traffic”. Facebook was thereafter selected because it is a relevant platform for investigating the research questions, as it is the most popular online community where customers publish their consumption experience reviews whilst as the traffic and the relevance of the journal postings were adequate to provide answers to the research questions. Compared to other similar sites, Facebook by far attracts more discrete posters and as such offers data that are rich and more descriptive. This might be because Facebook offers wide opportunities in terms of page creation, opinion posting or opinion evaluation, popularity and inclusion of Web 2.0 features that facilitate more collaboration and information sharing among users (Ladhari and Michaud, 2015). Thus, it is not uncommon to learn that shoppers are more likely to vent their shopping experiences on Facebook. Second, data were obtained and analyzed. All the obtained data were thoroughly screened following a well set out rigorous process to qualify the most relevant reviews. This process which falls into Kozinets’ (2002) second step involved observing three criteria in recruiting customer reviews to be included in the final database. These include:
ensuring that the selected review is experiential in nature and contain a comprehensive description of the experience;
discarding irrelevant messages based on thorough examination, as overwhelming data are anticipated; and
recruiting diverse (both positive and negative) journal postings to focus on greater analytic depth of the topic through a sound sampling strategy (Brown et al., 2003 in Rageh et al., 2013).
In the paragraph that immediately follows, how these three selection criteria were implemented was discussed. The reasoning behind this level of rigor was to ensure that the final data generation technique sufficiently accounted for the criteria set in step three “providing trustworthy interpretation” by Kozinets (2002).
A total of 192 journal postings (consumer reviews) acclaimed most helpful reviews published between November 26, 2015 and March 3, 2016 were obtained from the Facebook page of a leading online retailer that maintains a virtual presence in 11 African countries including Algeria, Cameroon, Egypt, Ghana, Ivory Coast, Kenya, Morocco, Nigeria, Senegal, Tanzania and Uganda. The selected sampling period is unique because it covers the time when some African countries such as South Africa, Egypt, Kenya, Ghana, Nigeria and so on leapfrogged into higher positions in the business to consumer e-commerce index ranking (UNCTAD, 2016). Most of the reviews were posted by Nigerian online shoppers. Out of the 192 consumer reviews, 97 were very short and uninformative and were all discarded, while 17 and 5 consumer reviews were also discounted because they were adverts and sales consultants’ reviews, respectively. Thus, a total of 73 useful consumer reviews formed the final database (please note that the discarded adverts and sales consultants’ reviews do not include the ones posted as comments on other customers’ reviews extracted for the final analysis). The above recruited 73 useful reviews contained greater information in terms of the depth of descriptions and were also experiential in nature whilst the reviews were also polarized into positive and negative reviews (Table I). This sample is considered adequate because it is similar to the sample used in previous research with similar aim (Rageh et al., 2013). The name of the online retailer and the reviewers were kept anonymous to ensure that ethical guidelines as stipulated by Kozinets (2002) are not violated. The journal postings were read severally, OSE drivers/belief factors and outcomes were identified and categorized based on central keywords (Miles and Huberman, 1994). Validity issues were remedied in two ways. First, by generating data from a highly credible social networking site (i.e. Facebook). According to Ladhari and Michaud (2015), comments generated from Facebook is often more credible than those generated in other social media platforms because Facebook forms a network of friends. Additionally, Facebook users can help their social connections with product-related information when they share product information and purchase experiences (Chu and Kim, 2011). Second, validity was put in check by continuously maintaining close touch with the data and constantly comparing identified themes with themes identified in previous research.
A combination of quantitative (descriptive) and qualitative (conversation) analyses was conducted on the data. The conversation analytical procedure partly adopted, notwithstanding, the analysis; interpretation of data were largely informed by Kozinets’ (2002) analytical procedure. The coding and analysis of the narratives were informed mainly by previous themes identified in shopping experience literature and theories of consumer behavior. As the amount of data collected was overwhelming, the Nvivo 10 software was used to facilitate the management of data. Finally, as the research was based on online shoppers’ reviews, the analysis and interpretation meant the final stage in this research. In line with the approach adopted by Rageh et al. (2013), the last stage in Kozinets’ (2002) adaptation – member check – was further confirmed through a back and forth comparison with the themes identified in the literature. This was because the research was unobtrusively conducted, thus making the analysis and interpretation of the results the end of the research (Rageh et al., 2013). It is also worth noting that this study began with a grand theory in mind. Thus, as noted before, themes identified in previous research guided the coding of the narratives in the present study. According to Miles and Huberman (1994, p. 58), research outputs can be adjudged valid if the coding of themes is based on a “conceptual framework, list of research questions, hypotheses, problem areas, and/or key variables that the researcher brings to the study”.
Overall, out of the 73 usable reviews that formed the final database, only 23 paralleling 31.5 per cent were positive while the remaining 50 reviews equaling 68.5 per cent were negatively framed. Table I shows a breakdown of the reviews into OSE drivers and its behavioral outcomes. The frequency of negative experiential drivers [61(77.4 per cent)] far outnumbered the positive experiential drivers [21 (25.6 per cent)]. The dominance of negative reviews implies two possibilities. First, it might be an indication of gross underperformance of the case organization. Second, it might imply that customers who experience service failure are more likely to share their experience than customers who had positive experience. The outputs shown in Table I indicate that product/service experience quality drivers such as delivery quality, product quality and customer service quality are, by far, the most cited drivers of OSE followed by retail prices. All the seven OSE drivers converge into two broad categories (i.e. product and service-related experiential drivers and website-related experiential drivers). Shopping enjoyment was the only hedonic driver of OSE that emerged from the study while every other driver is cognitive based on Klaus’s (2013) framework of online customer service experience. As shown in Table I, indicators of shopping enjoyment were mentioned only four (4.9 per cent) times by the shoppers (Tables I and II).
Internal response to service failure is the most reoccurring outcome of OSE followed by trust, advisory eWOM and regrets (Table I). There were 81 (90 per cent) negatively framed OSE outcome variables, whereas a meagre 9 (10 per cent) OSE outcomes were positively framed. Five distinct categories of OSE outcome variables including internal response to service failure, trust, external response to service failure, eWOM and regrets emerged from the analysis. Three components of eWOM (i.e. advisory, inquiry and recommendation/dissuasion) were uncovered. Advisory eWOM were directed to both retailers and other customers but the retailer-oriented advisory eWOM far dominated (Table III).
Consumer group behavior in the social networking sites context
First, it was found that shoppers can still express willingness to repurchase in the future if they receive good explanations for the service failures experienced. Other customers also seize this avenue to complain or launch their own queries. Their behavioral response will greatly depend on whether they received satisfactory response from the company. Here is an instance:
Customer’s Experience Review: Your black Friday sales should start midnight and the server should be active but what do we get, total black out just like NEPA. [I] called the customer service only to be answered by a machine that all agents are busy.
Company’s Response: Hi […], apologies for your experience. However, we reached the maximum number of visitors it can take at once, hence the inability to access our website. More so, we may be unable to take calls today as pre-informed across our social media platforms. You can still get a great deal before 12 midnights when the Black Friday ends, you know smile emoticon.
Customer’s Reply: Thanks for your reply. Since l missed out on the black Friday l hope to do my shopping other days.
Customer 2’s comment on the first customer’s post: […] sold me a FAKE SanDISK that spoilt my phone and refuse to respond to my complaint.
Customer 3’s comment on the first customer’s post: I ordered for itel smart phone but have not received it. How can I call their line?
Customer 4’s comment on the first customer’s post: The same is applicable to me. You order, they don’t notify you on when the product will be ready so you can get the cash ready. This is way low the performance I expected.
Customer 2’s comment after the company contacted him: […] finally contacted me after 6 days which to me is a really poor response time.
Customer 4’s query after customer 2 reported that he was contacted by the company: How did day contact you? Via phone or via text?
All these clearly indicate that customers are always actively seeking solutions to their purchase problems especially when service failure occurs. Thus, recovered customers will be willing to repurchase and vice versa if companies can provide satisfactory responses which enhance trusting intention.
Online shoppers also find other customers’ experience journal posts as good avenues to promote their negative experiences. Some customers continuously promote their negative service experiences on other customers’ journal posts until they receive convincing/meaningful response from the company. Shoppers that receive company’s response such as the shopper referred to in the preceding analysis stop such promotion. Some customers that receive unsatisfactory or fail to receive any response from the company perceive external response to service failure such as legal actions, opening a negative campaign fan page and so on as the last resort. Here are few examples:
Customer 1: I ordered a Nikon D5200 camera since 6th of December and up till now I have not received it. I have sent them multiple emails and none of their replies have been successful. […] I can’t take this from […] anymore. I’m now talking with my lawyer to see how we can claim damages.
Customer 2: Hi […] fans […] I am looking at starting a page for irate customers like myself. And promoting it with adverts. […] if you will love to be part of the page please type “YES” below this message.
Thus, failure to respond to customers’ internal responses to service failure leads to customers’ external response to service failures.
Customers also find other customers’ journal posts as avenues to post queries about perplexing issues, as well as a platform to query firm’s credibility. Here are instances:
Customer experience review: I am not happy sending this message because […] Nigeria has decline and change from what it used to be. In short, I can now boldly say that […] is fast turning to a haven of counterfeit goods[…].
Other customers’ queries:
A: What is the price of infinix hot note 2 note and how can I get one in Anambra State
B: Please who has the idea of TechnoP9??? Am in dire need of the phone. Who has any idea of the amount?
Here is another instance where negative experience reviews generated scores of credibility queries:
Customer experience review: […] I have had lots of dealings with you people and I must say, I am not pleased. Your customer care reps are good in covering up their tracks with lies, your warehouse front desk people are very rude and your security guards are exceptionally not it. Kindly step up your game as you have LOTS of competition now.
Other customers’ comments:
A: […] is indeed a fraud! And like you said they have trained all their representatives to tell lies and cover their tracks.
B: They are only doing their work……. But the fraud…. I can’t really agree to that…… I have shopped over 10 items and I think they are still functioning […].
A: […] you have been lucky. I am not the only person anyway. If it is not a fraud, why would they make it virtually impossible to reach any of their customer service representatives? 20 days now and they have not refunded my money? It was until I went on Twitter that they responded.
C: Fraud indeed is an understatement […]
Customers also find other customers’ journal posts as avenues to play persuasion or dissuasion role. Here are instances:
(A) Customer experience review: I can’t imagine how a big company like this should fail to be organized. I ordered for a simple thing like a memory card but they ended up sending twinkle eye brow sharpener. Till date no memory card, no refund of money.
Another customer’s comment/advice: I would have advised you to allow […] improve first […]
(B) Customer experience review: … infinix hot note of 2g ram has been 28k since they started selling it in June in anywhere in Nigeria, then all of a sudden […]- 2hrs to black Friday […] increase the price to #35300, then after the supposed 15% discount, the price was now #28500 (500-naira profit), who are they fooling?
Other customers’ comments:
A: I got mine at a walk-in store at a cost of #26000. So what’re we talking about?
B: It’s now 23K in manual market store.
Customers who had extremely positive experience can go out of their way to stand in for a firm when other customers post negative experiences, whereas other customers who also had negative experiences launch a counter defense reemphasizing poor performance. Even if the company intervenes in such discussions, negative comments will still dominate especially if many customers have had negative experiences. Here is a good example:
Customer experience review (Customer 1): The worst shop in Africa and the worst deals I have ever come across in my life […] Please don’t ever think or go there they will deceive you and they will scam you […] I even regretted staying awake and believing such rubbish from this stupid site.
Defending customer (customer 2): […] the Friday sale is real, already placed orders for infinix hot 2 for 9,430 plus shipping, the black Friday discounted sales wasn’t on the home page, if you’re using a pc (that’s what I used) and the black Friday icon column shows on the screen after loading the page, click on it, then you’ll see a list of the discounted products with the flash sale option at the bottom right corner of it, click on it, then you’ll see the products there, sadly the flash sale for hot 2 ended like an hour after 12, because people already ordered them all, anyways there are still other products up for sale like the tecno c8.
Customer 2: The injoo halo went for 6,750 that too was sold out some minutes after 10am which was the time allocated for it.
Customer 1: […] I am using the app I slept 1:30am with one of my colleague that phone did not reduce […]. I stayed awake when I got to the office today all of us stood awake and not one person saw that price […] Please am not giving you shoot bird but sharing my experience.
Customer 2: Here’s a screenshot.
Customer 2: Then this other was up for 10am today.
Customer 3: […] what is not good is not good. The site didn’t even load on laptops
Customer 2: […] laptops is till 8pm according to the information on this page today. Just do it the way outlined above and you need to be fast too, they have limited stock.
Customer 4: I placed my orders already. An infinix, Nokia Lumia and a powerbank. All for 33,700.
Customer 5: Their site didn’t show any of those times. God knows I was awake. I reloaded the page over and over on android and pc, it didn’t show, was just blank, even now it’s blank.
Company: […], sorry you feel this way. However, we reached the maximum number of visitors it can take at once, hence the inability to access our website. Please try again. Thank you!
Customer 5: […] is a fraud!
Customer 6: 12:02am-Flash deal offer; 12:03am to 01:10am- Website not available on browser. […]. Android app unable to retrieve data. And yet, they keep bombarding me with emails and SMS offers.
Customer 7: […] sold me a FAKE SanDISK that spoilt my phone and refuse to respond on my complaint.
Customer 8: If you guys still value for your consumers and which to keep and still want to treat them as an asset, it would be advisable if you guys sort this problem out. It’s getting to much.
In sum, services firms especially online retailers should endeavor to avoid service failures, as this remains the most appropriate way to get shoppers’ conversations about their product and services dominantly positive. As it appears that service failure is inevitable, it pays to not only minimize the frequency of its occurrence, but also satisfactorily respond to customers’ complaints about service failures.
Summary and discussion
This paper initially sets out to investigate OSE drivers and its behavioral outcomes. Drawing on a wide range of consumer behavior theories and OSE literature, some key constructs that fall into the drivers/belief factors and outcomes of OSE were uncovered. A B-A-I conceptual model (Figure 1) was proposed. The study identified seven belief factors of OSE broadly classified into product and service-related and websites-related experiential drivers. The three product- and service-related experiential drivers include retail prices, complaint handling and product/service experience quality, while the four website-related experiential determinants include convenience, website functionality, relational experience and shopping enjoyment. As indicated in Figure 1, the above seven enlisted drivers of OSE (i.e. retail prices, complaint handling, product/service experience quality, convenience, website functionality, relational experience and shopping enjoyment) which are broadly classified into product- and service-related and websites-related experiential drivers are belief factors. As framed in Figure 1, these belief factors were modelled as drivers of OSE because they have inducing effect. While the two broad categories of experiential drivers are unique, its elements, though distinct in its behaviors because of context-specific factors, fit into the general framework of OSE found within the customer experience literature. Thus, even though some drivers of OSE (for instance, relational experience, retail prices, and shopping enjoyment) were uncovered in previous research (Klaus, 2013; Jones, 1999), the categorization adopted in the present study is novel to the extent that it delineates drivers of OSE based on product- and service-related attributes and website-related attributes.
Drivers of online shopping experience
The most important driver of OSE which can also be described as a belief factor because of its ability to shape shoppers’ belief in online shopping is product/service experience quality followed by retail prices. Online shoppers perceived delivery-related services as the most important aspect of product/service experience quality followed by customer service. Regarding retail prices, online shoppers construed the online retailer’s prices to be fair and affordable and were also able to spot price hikes during promos. This finding is consistent with Jones (1999) who identified retail prices as the most reported retailer factor that characterize entertaining shopping experiences. The outputs from this study indicate that overall shopping experiences’ quality falls when shoppers perceive high prices and vice versa.
While previous studies (Cho, 2011; Ahmad, 2002) conceptualized customer complaints as a behavioral construct and an outcome of shopping experience because it is a post-purchase construct, complaint handling was a unique driver of OSE that emerged from this study. Complaint handling is also a belief factor (Figure 1) because shoppers’ engagement in future online shopping is shaped by the way their previous complaints were handled. The conceptualization of complaint handling as a belief factor that drives OSE emerged from Verhoef et al.’s (2009) conceptual portrayal of customer experience determinants and dynamics. The framework suggests that the determinants of customer experience include prior, during and post-purchase activities. Complaint handling involves communication which was identified as a post-purchase experience sub-component of functionality dimension of online customer service experience in Klaus’s (2013) dynamic framework of online customer service experience. Thus, drawing on the dynamic character of customer experience, experience at time t (complaint handing quality) is a function of experience at time t−1 (prior and during purchase experiences). Depending on how shoppers’ complaints are handled, their perception of post-purchase experiences will lie in a positive–negative continuum. Complaint handling is, therefore, an important driver of OSE.
Previous research identified convenience and website functionality as important determinants of OSE (Klaus, 2013; Bridges and Florsheim, 2008; Ahmad, 2002). Literature also supports relational experience and shopping enjoyment as important drivers of OSE (Klaus, 2013; Jones, 1999). “Shopping acts as a mechanism for consumers to define and negotiate their relationships with others” (Compeau et al., 2016, p. 1035). Thus, the influence of customer-to-customer interactions on purchase decision is unquestionable. Overall, in exception of convenience which is dominantly positive because of the nature of online shopping, the six remaining drivers of OSE portend opinion valence that lie in a positive–negative continuum. Generally, complaint handling is a unique and new driver of OSE identified in this study. In line with previous research (Ding et al., 2010; Bridges and Florsheim, 2008; Cyr et al., 2007), it can be argued that the seven belief factors are drivers of OSE because not only does Froehle and Roth’s (2004) B-A-I framework portend that belief factors enhance attitude but also it has been shown that OSE can be stimulated by product-associated attributes (Jones, 1999) and website features (Vieira, 2013; Jeong et al., 2009). Thus, the terms “drivers” and “belief factors” can be interchangeably used.
Behavioral outcomes of online shopping experience
Five key behavioral outcomes of OSE (i.e. internal response to service failure, external response to service failure, trust, eWOM and regrets) emerged from the analysis. The dominance of negatively framed over positively framed behavioral responses reinforces the conventional evidence that customers are more likely to tell others about their negative experiences compared to their positive experiences. Internal and external response to service failure experiences are new outcomes of OSE that emerged from this study. Internal response to service failure is the most frequently reported behavioral outcomes of OSE, whereas external response to service failure is the least frequently reported. The low frequency of external response as opposed to internal response to service failure may be because shoppers perceive it as the last resort. External response to service failure is the result of the firm’s inability to recover failed service and respond to customers’ unsatisfactory complaints handling experiences. Zeithaml et al. (1996) identified internal response to problem and external response to problem as the two main unfavorable behavioral outcomes of service quality. Drawing on this finding, internal response to service failure and external response to service failure are all indicators of poor performance and reflect tendencies that the shopper is poised to reduce or even discontinue patronizing the online retailer. Additionally, shoppers’ willingness to advise retailers on areas that need improvement actions supports the co-creation role of customers emphasized in the service-dominant logic of marketing (Vargo and Lusch, 2008). According to Vargo and Lusch (2008), when consumers and firms collaborate with one another through consumer–firm interactions to create value, value co-creation is the result. Thus, as firms and shoppers work together to avoid occasions leading to service failure and consequently improve quality of experiences, they are co-creating value. One way to reduce unfavorable behavioral outcomes is to reduce incidents leading to service failure and make sincere and prompt efforts to recover failed services.
Trust, which is defined here as the ability of online retailers to keep their promises, is the second most frequently acknowledged behavioral outcome of OSE. This finding is largely consistent with previous studies (Klaus, 2013) that emphasized the importance of trust especially in the online context. However, previous trust research shows that the position of the construct in experience models is inconsistent. Some academics (Trevinal and Stenger, 2014; Klaus, 2013; Rose et al., 2011) conceptualized trust as a driver of shopping experiences. In this study, however, it is established that trust is a consequence of OSE. Although Klaus’ (2013) dynamic proposal of online customer service experience indicate that trust is present across all the consumer decision-making phases, the decision to trust or not to trust an online vendor follows from accumulated previous experiences. In other words, for an online shopper to exhibit trusting behavior, he must have experienced the company or services in one way or another (e.g. through company promotion, or recommendations by other customers). Given that trust evolves over time and varies depending on shopping experience (Beldad et al., 2010), it is logical to argue that OSE does not only leads to trust; shoppers who trust an online vendor based on their previous positive experiences can engage in positive eWOM, whereas shoppers who do not trust an online vendor because they have previously had negative experiences can choose to engage in negative eWOM or external response to service failure.
Consistent with eWOM literature (Tiago et al., 2015; Chu and Kim, 2011) the three sub-components of eWOM (i.e. advisory, inquiry and recommendation/dissuasion) that emerged from this study broadly falls into two categories of eWOM such as opinion-seeking and opinion-giving, while opinion-passing and opinion content were not visible. This might be because of the emerging nature of review writing in Africa, given that online shopping in the continent is less than a decade old. While inquiry aligns to opinion-seeking, customers’ advisory role and recommendation/dissuasion correspond to opinion-giving. Advisory eWOM is the most frequently reported followed by inquiry. Although customers’ advisory role reflected in their eWOM is oriented towards the firm and fellow customers, the former was more frequently reported. The customers’ advisory role is a double-edged sword that can swing in either positive or negative direction. First, firms can use it as basis for improvement actions. Second, other customers especially new ones can boycott purchase if the advice is a negative comment or dissuades other customers. Given that firms are encouraged to solicit feedback from customers especially those with low information control (Mittal et al., 2008); if customers willingly point out areas where improvement is needed, such practice is to the advantage of the retailers. Thus, online retailers must be willing to undertake improvement actions based on customers’ recommendations.
The findings also indicate that regret is the third most frequently reported outcomes of OSE. Consistent with the theory of cognitive dissonance (Festinger, 1957) and Simonson (1992), shoppers clearly exhibited regret behavior in some of their purchase decisions and the responsibility for their wrong choices increased, as they realized that their assumptions about the firm was wrong whilst purchasing from a competing firm would have been a better option. Thus, in addition to Oliver (1997) who pointed out difficulty in making choice, negative consequences and decisions that deviate from the convention as enhancers of regrets, a fall in shoppers’ expectations arising from accumulated previous shopping experience (experience at time t−1) can also contribute to enhancing regret. Regrets therefore have the capacity to activate advisory and dissuasion eWOM intentions and external response to service failure. Regrets can also have a negative effect on trust because when actual experience falls short of the expected experience, a negative feeling about the firm’s credentials is bound to develop. This is because the theory of cognitive dissonance (Festinger, 1957) holds that behaviors are at odds with each other.
Conclusion and implications
This study contributes to services science research in several directions. First, the proposed B-A-I framework advances Rose et al.’s (2012) framework through the identification of relational experience which accounts for customer-to-customer interactions as pointed out in other previous shopping experience literature (Klaus, 2013; Gentile et al., 2007). The study also advances the TRA and TPB by evolving belief attributes such as relational experience and shopping enjoyment that not only reflect shoppers’ cognitive evaluation but also encompass shoppers’ emotion, as well as customer-to-customer interactions. Relational experience which comprises customer-to-customer interactions and has also been conceptualized as social presence in the online context reflects the social context and its influence (Klaus, 2013), as well as the extent to which a computer-mediated medium allows users to experience others as being psychologically present (Gefen and Straub, 2003). While relational experience reflects a social driver of experience because of its emergence from a broader social system, shopping enjoyment reflects consumer emotion. Thus, as drivers of OSE which, respectively, reflect customer-to-customer interactions and consumer emotions, the identification of relational experience and shopping enjoyment in the present study furthers the current understanding of the TRA and TPB whose belief factors are dominantly cognitive. The study also contributes to the SOR framework by identifying some unique belief factors that drive the organism component (i.e. OSE). The most notable is complaint handling. Contrary to previous research where customer complaints were conceptualized as a behavioral construct, this study demonstrates that complaint handling drives OSE. By identifying two unique broad categories of the drivers of OSE, this study contributes to the online shopping literature that claims that the drivers of OSE are diverse (Bridges and Florsheim, 2008). While some of the components of these two broad categories of OSE drivers are consistent with previous research findings, this categorization is unique. It not only portrays shopping experience from an emerging market viewpoint but also demonstrates that cognitive factors are by far the most dominant drivers of OSE.
This study advances satisfaction theories and the B-A-I framework by proposing a set of behavioral outcome variables that support the service-dominant logic of marketing and lay out the steps that consumers take to resolve service failures internally before resorting to external response to service failures. For instance, the willingness of shoppers to offer suggestions on the areas where improvement actions should be directed is a typical reflection of the co-creation role of customers in their own consumption experience. The study also furthers Zeithaml et al.’s (1996) study on the behavioral outcomes of service quality by identifying and proposing regrets, internal and external responses to service failure as behavioral outcomes of OSE. In addition to Oliver’s (1997) postulations, this study demonstrated that a decrease in shoppers’ expectations arising from accumulated previous shopping experience can also contribute to enhancing regret.
While the understanding of the behavior of consumers in group contexts has till date remained imprecise, this study advances services marketing theory by demonstrating how other shoppers exploit the online review of fellow shoppers as a suitable platform to launch their own complaints, make inquiries, promote their negative experiences and query the firm’s credibility. Additionally, although a wide range of literatures support the impact of shoppers’ opinions on other shoppers’ behavior especially as it has to do with defending the firm (see for instance Chu and Kim, 2011), we show through a conversation analysis of consumers’ group behavior in the social networking sites context that no amount of persuasion from the firm or other customers can make up for a bad service, especially if the firm fails to make sincere effort to recover the failed services or completely fail to respond to customers’ complaints. Contrary to the previous evidences surrounding the position of trust in online shopping models (Trevinal and Stenger, 2014; Klaus, 2013), this study demonstrates that trust is a behavioral outcome of OSE. Additionally, the study demonstrates that regrets may likely weaken trust.
The study also provides methodological contribution. It investigated the outcomes of OSE and by implication, consumer group behavior by combining two unique and uncommonly used qualitative analytical techniques (i.e. netnography and conversation analysis). While a wide range of studies that examined customer experience and its outcome factors abound (Trevinal and Stenger, 2014; Klaus, 2013; Cyr et al., 2007), very few explored OSE using a naturalistic and unobtrusive qualitative research that is free from respondents’ inhibition. Additionally, till date, no research examined OSE and its behavioral outcomes by using a combination of netnography and conversation analysis to explore shoppers’ group behavior arising from their shopping experience in the virtual environment. This study advances consumption behavior literature by demonstrating how these two unique methods can be used to uncover drivers and outcomes of behavior.
The managerial implications of the findings are that in addition to providing superior shopping experience through enhancing the drivers of OSE identified in this study, online retailers must work assiduously to reduce incidents leading to service failures and promptly undertake service recovery actions whenever service failure occurs. Online retailers especially those operating in emerging markets will therefore benefit from their service recovery investments if they proactively install processes that enable them to promptly and satisfactorily recover failed services.
Limitation and further research
The study poses several investigative research directions. First, the proposed research model is only conceptual. Validating the research framework in both emerging and developed markets with quantitative research approaches is a viable avenue for future researchers. It might also be insightful if future researchers can attempt categorizing the outcomes of OSE identified in this study into overt (obvious) and covert (secret/closed) behavior. Future studies can also expand our understanding of customers’ defense behavior in group environments. Specifically, the impact of such behavior on constructs such as loyalty and regrets will be particularly interesting to explore in future research. Except convenience, all the drivers of OSE display opinion valence that lie in a positive-negative continuum. Till date, literature has been relatively quiet on the level of negativity of the OSE drivers that will activate unfavorable behavior nor the level of positivity beyond which the OSE drivers will fail to make significant improvement to shoppers’ favorable behavior. Future researchers may take on this evident gap. Additionally, it might be important to further investigate OSE and its behavioral outcomes in emerging markets in other services sectors such as banking and hospitality contexts, especially in Africa to see if consistent findings will emerge. Finally, it has been noted that one of the striking limitations of conversation analysis is that it jettisons external considerations and presuppositions in its enactment of meanings (O’Sullivan, 2010). The implicit proposal is that it ignores local variation in conversations which ought to be acknowledged by conversation analysts as a possible influencer of research results. Additionally, conversation analysis has been accused of overemphasis on interactions while excluding content even though detailed consideration should be given to both form and content (O’Sullivan, 2010). Although O’Sullivan (2010) identified these limitations from the perspective of interview data whilst the naturalistic and unobtrusive data used in this article, as well as a combination of two qualitative research techniques, serve to overcome some of these limitations, the issue of local variation was an aspect we ignored in this article. Thus, future research should seek to establish the extent to which local variation such as gender and culture influence the nature of the findings reported in this paper.
Numeric tallies of drivers/determinants and outcomes of OSE
|Construct||Type of review|
|Product and service-related drivers|
|Product/service experience quality (delivery/product/customer service quality)||3/3/0||3.7/3.7/0||20/7/16||24.4/8.5/19.5||49||59.8|
|Internal response to service failure||0||0.0||25||27.8||25||27.8|
|External response to service failure||0||0.0||3||3.3||3||3.3|
|Advisory (customer/retailer-oriented advice)||0/2||0.0/2.2||2/10||2.2/11.1||14||15.6|
Samples of consumer reviews for OSE drivers
|Construct||Online shopping experiences|
|Retail prices||“I bought an item from … last December, though the delivery was delayed which I think was due to Christmas rush but the item was delivered. The price is relatively fair and the convenience can’t be compared”
“Why the hike in almost all your products? Was planning on getting infinix zero 2. It was N36.400 a few days ago and now, its 46k”
|Complaint handling||“This group may be out to tactically exploit Nigerians by delivering bad broken but sealed items for which complaints are not entertained”
“I have had lots of dealings with you people and I must say, I am not pleased. Your customer care reps are good in covering up their tracks with lies, your warehouse front desk people are very rude and your security guards are exceptionally not it”
|Product/service experience quality (delivery/product/customer service quality)||“Worst service ever. Order not delivered since November 27 (black Friday). I ask for refund, they refund some and no response on the remaining item refund process. I mailed them several time but just whack response”
“I paid online for a washing machine since 22/12/2015. Up till now …, I am yet to get delivery of the machine. I send them a mail, no response”
“My Led Samsung television was delivered to my house three days after I placed an order, I am so much convinced with the services of …”
|Convenience||“I like …. Makes shopping easier for me, no need to stress myself”
“… is the best place to do your online shopping, it is convenient”
|Website functionality||“Good to use always, made easy, wonderful appearances and keeps one abreast of prices”
“The mobile app does not work. It’s just too painful”
|Social presence/relational experience||“I have not used it before but from what I heard that from friends and associate, I rate … 4 stars”
“The comments I just read about … is not encouraging I actually want to order for laptop android but am scared”
|Shopping enjoyment||“Good to use always, made easy, wonderful appearances”
“… is the best place to do your online shopping, it is … fun filled”
Samples of consumer reviews for the outcomes of OSE
|Construct||Outcomes of OSE|
|Internal response to service failure||“… This continued for another 1 week. I called the customer care repeatedly and sent a number of mails all to no avail. All I got were only empty promises of “we will escalate this complain and resolve it”
“… I tried calling the customer care line several time but my calls where being aborted despite the fact that my airtime has being exhausted by your official jingle”
|Trust||“With this … mode of operation, I deemed it fit to conclude that … is no longer reliable, trust worthy”|
|External response to service failure||“I ordered a Nikon D5200 camera since 6th of December and up till now I have not received it. I have sent them multiple emails and none of their replies have been successful. … I can’t take this from … anymore. I’m now talking with my lawyer to see how we can claim damages”
“Hi … fans … I am looking at starting a page for irate customers like myself. And promoting it with adverts. I believe people should know what scam … Nigeria is as a whole. If you will love to be part of the page, please type “YES” below this message. In fact, I’m creating the page whether I get any supporters or not. So if you see the page on your feed timeline just share”
|Advisory (customer/retailer-oriented advice)||“Please don’t ever [make] the mistake of paying online on … . If you want to buy something and it is not pay on delivery forget it… … You will cry out and waste card in calling Customer Care before you are refunded. Your money will be used for business, your order will be cancelled and you will be stock and no one will reply you”
“You guys need to get your act together because I don’t understand why you call to confirm orders and go ahead to send completely different items … if an item isn’t available, remove it from your website or let the customer know it’s no longer available when your rep calls to confirm the order …”
|Inquiry||“Please how can l redeem and use the voucher given to me?”
“I haven’t received what I ordered for. What’s causing the delay?”
|Recommendation/dissuasion||“… the best place to do your shopping at your convenient time and comfortable prices with quality product. You will never regret it”
“Please don’t ever make the mistake of paying online on …, if you want to buy something and if it is not pay on delivery, forget it. You will cry out and waste card in calling Customer Care before you are refunded. …, your order will be cancelled and you will be stocked and no one will reply you…”
|Regrets||“… If I had known it would not arrive on time, I could have asked my friend who arrives from the US tomorrow morning to bring it with her. … . Now, I am heartbroken”
“This is a transaction I will always regret. … is a company I used to hold to high esteem, but it’s obvious I was wrong. I am very disappointed. This typifies fraud!!!”
Afshar, V. (2015), “50 important customer experience stats for business leaders”, available at: www.huffingtonpost.com/vala-afshar/50-important-customer-exp_b_8295772.html (accessed 17 November 2015).
Ahmad, S. (2002), “Service failures and customer defection: a closer look at online shopping experiences”, Managing Service Quality: An International Journal, Vol. 12 No. 1, pp. 19-29.
Ajzen, I. (1985), “From intentions to actions: a theory of planned behavior”, in Kuhl, J. and Beckman, J. (Eds), Action-Control: From Cognition to Behavior, Springer, Heidelberg, pp. 11-39.
Ajzen, I. (1991), “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, Vol. 50 No. 2, pp. 179-211.
Babin, B.J., Darden, W.R. and Griffin, M. (1994), “Work and/or fun: measuring hedonic and utilitarian shopping value”, Journal of Consumer Research, Vol. 20 No. 4, pp. 644-656.
Beldad, A., de Jong, M. and Steehouder, M. (2010), “How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust”, Computers in Human Behavior, Vol. 26 No. 5, pp. 857-869.
Bolton, R.N., Gustafsson, A., McColl-Kennedy, J., Sirianni, N.J. and Tse, D.K. (2014), “Small details that make big differences”, Journal of Service Management, Vol. 25 No. 2, pp. 253-274.
Boulaire, C., Graf, R. and Guelmami, R. (2008), “Online communities serving the consumer/producer: observations from the study of a fantasy world”, Direct Marketing: An International Journal, Vol. 2 No. 4, pp. 199-220.
Bridges, E. and Florsheim, R. (2008), “Hedonic and utilitarian shopping goals: the online experience”, Journal of Business Research, Vol. 61 No. 4, pp. 309-314.
Bryman, A. and Bell, E. (2015), Business Research Methods, 4th edition, Oxford University Press, Oxford.
Chen, Y.C., Shang, R.A. and Kao, C.Y. (2009), “The effects of information overload on consumers’ subjective state towards buying decision in the internet shopping environment”, Electronic Commerce Research and Applications, Vol. 8 No. 1, pp. 48-58.
Cho, Y.C. (2011), “Analyzing online customer dissatisfaction toward perishable goods”, Journal of Business Research, Vol. 64 No. 11, pp. 1245-1250.
Cho, Y., Im, I., Hiltz, S. and Fjermestad, J. (2002), “The effects of post-purchase evaluation factors on online vs. offline customer complaining behavior: implications for customer loyalty”, Advances in Consumer Research, Vol. 29, pp. 318-326.
Chu, S.C. and Kim, Y. (2011), “Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites”, International Journal of Advertising, Vol. 30 No. 1, pp. 47-75.
Compeau, L.D., Monroe, K.B., Grewal, D. and Reynolds, K. (2016), “Expressing and defining self and relationships through everyday shopping experiences”, Journal of Business Research, Vol. 69 No. 3, pp. 1035-1042.
Cyr, D., Hassanein, K., Head, M. and Ivanov, A. (2007), “The role of social presence in establishing loyalty in e-services environments”, Interacting with Computers, Vol. 19 No. 1, pp. 43-56.
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1003.
Ding, D.X., Hu, P.J.H., Verma, R. and Wardell, D.G. (2010), “The impact of service system design and flow experience on customer satisfaction in online financial services”, Journal of Service Research, Vol. 13 No. 1, pp. 96-110.
Doherty, N.F. and Ellis-Chadwick, F. (2010), “Evaluating the role of electronic commerce in transforming the retail sector”, International Review of Retail, Distribution and Consumer Research, Vol. 20 No. 4, pp. 375-378.
Evans, D. and Cothrel, J. (2014), Social Customer Experience: Engage and Retain Customers through Social Media, Wiley, New York, NY.
Festinger, L. (1957), A Theory of Cognitive Dissonance, Stanford University Press, Stanford, CA.
Fishbein, M. and Azjen, I. (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA.
Froehle, C.M. and Roth, A.V. (2004), “New measurement scales for evaluating perceptions of the technology-mediated customer service experience”, Journal of Operations Management, Vol. 22 No. 1, pp. 1-21.
Gefen, D. and Straub, D. (2003), “Managing user trust in B2C e-services”, E-Service Journal, Vol. 2 No. 2, pp. 7-24.
Gentile, C., Spiller, N. and Noci, G. (2007), “How to sustain the customer experience: an overview of experience components that co-create value with the customer”, European Management Journal, Vol. 25 No. 5, pp. 395-410.
Harvard Business Review (2014), Lessons from the Leading Edge of Customer Experience Management, Harvard Business School Publishing, Boston, MA.
Hoffman, D.L. and Novak, T.P. (1996), “Marketing in hypermedia computer-mediated environments: conceptual foundations”, Journal of Marketing, Vol. 60 No. 3, pp. 50-68.
Hsu, H.Y. and Tsou, H.T. (2011), “Understanding customer experiences in online blog environment”, International Journal of Information Management, Vol. 31 No. 6, pp. 510-523.
Internet Business Statistics (2016), “The growth of online shopping”, available at: http://wwwmetrics.com/shopping.htm (accessed 23 December 2016).
Izogo, E.E., Ogba, I.E. and Nwekpa, K.C. (2016), “Correlates of the determinants of relationship marketing in the retail sector: the nigerian case”, African Journal of Economic and Management Studies, Vol. 7 No. 1, pp. 30-53.
Jeong, S.W., Fiore, A.M., Niehm, L.S. and Lorenz, F.O. (2009), “The role of experiential value in online shopping: the impacts of product presentation on consumer responses towards an apparel web site”, Internet Research, Vol. 19 No. 1, pp. 105-124.
Jones, M.A. (1999), “Entertaining shopping experiences: an exploratory investigation”, Journal of Retailing and Consumer Services, Vol. 6 No. 3, pp. 129-139.
Klaus, P. (2013), “The case of amazon.com: towards a conceptual framework of online customer service experience (OCSE) using the emerging consensus technique (ECT)”, Journal of Services Marketing, Vol. 27 No. 6, pp. 443-457.
Kozinets, R.V. (1998), “On netnography: Initial reflections on consumer research investigations of cyberculture”, in Alba, J.W. and Hutchinson, J.W. (Eds), Advances in Consumer Research, Association for Consumer Research, Provo, UT, Vol. 25, pp. 366-371.
Kozinets, R.V. (2002), “The field behind the screen: using netnography for marketing research in online communities”, Journal of Marketing Research, Vol. 39 No. 1, pp. 61-72.
Ladhari, R. and Michaud, M. (2015), “eWOM effects on hotel booking intentions, attitudes, trust, and website perceptions”, International Journal of Hospitality Management, Vol. 46, pp. 36-45.
Lee, D., Kim, H.S. and Kim, J.K. (2012), “The role of self-construal in consumers’ electronic word of mouth (eWOM) in social networking sites: a social cognitive approach”, Computers in Human Behavior, Vol. 28 No. 3, pp. 1054-1062.
Lee, J., Park, D.H. and Han, I. (2008), “The effect of negative online consumer reviews on product attitude: an information processing view”, Electronic Commerce Research and Applications, Vol. 7 No. 3, pp. 341-352.
Lomas, N. (2013), “Forrester: US online retail sales to rise to $370BN by 2017 (10% CAGR) As Ecommerce Motors on with help from tablets & phones”, available at: http://techcrunch.com/2013/03/13/forrester-2012-2017-ecommerce-forecast/ (accessed 17 November 2015).
Mehrabian, A. and Russell, J.A. (1974), An Approach to Environmental Psychology, MIT Press, Cambridge, MA.
Meyer, C. and Schwager, A. (2007), “Understanding customer experience”, Harvard Business Review, Vol. 85 No. 2, pp. 117-126.
Miles, M.B. and Huberman, A.M. (1994), Qualitative Data Analysis: An Expanded Sourcebook, Sage Publications, Thousand Oaks, CA.
Mittal, V., Huppertz, J.W. and Khare, A. (2008), “Customer complaining: the role of tie strength and information control”, Journal of Retailing, Vol. 84 No. 2, pp. 195-204.
Oliver, R.L. (1997), Satisfaction: A Behavioral Perspective on the Consumer, McGraw-Hill, Irwin.
O’Sullivan, T. (2010), “More than words? Conversation analysis in arts marketing research”, International Journal of Culture, Tourism and Hospitality Research, Vol. 4 No. 1, pp. 20-32.
Pathak, X. and Pathak-Shelat, M. (2017), “Sentiment analysis of virtual Brand communities for effective tribal marketing”, Journal of Research in Interactive Marketing, Vol. 11 No. 1, pp. 16-38.
Phillips Consulting (2014), “Online shopping report: a study of current trends in online shopping in Nigeria”, available at: www.phillipsconsulting.net/files/online_shopping_report.pdf (accessed 3 October 2015).
Pine, B.J. and Gilmore, J.H. (1999), The Experience Economy: Work Is Theatre and Every Business a Stage, Harvard Business School Press, Boston, MA.
Quinton, S. and Harridge‐March, S. (2010), “Relationships in online communities: the potential for marketers”, Journal of Research in Interactive Marketing, Vol. 4 No. 1, pp. 59-73.
Rageh, A., Melewar, T.C. and Woodside, A. (2013), “Using netnography research method to reveal the underlying dimensions of the customer/tourist experience”, Qualitative Market Research: An International Journal, Vol. 16 No. 2, pp. 126-149.
Rose, S., Hair, N. and Clark, M. (2011), “Online customer experience: a review of the business-to-consumer online purchase context”, International Journal of Management Reviews, Vol. 13 No. 1, pp. 24-39.
Rose, S., Clark, M., Samouel, P. and Hair, N. (2012), “Online customer experience in e-retailing: an empirical model of antecedents and outcomes”, Journal of Retailing, Vol. 88 No. 2, pp. 308-322.
Schmitt, B. (1999), “Experiential marketing”, Journal of Marketing Management, Vol. 15 No. 1-3, pp. 53-67.
Simonson, I. (1992), “The influence of anticipating regret and responsibility on purchase decisions”, Journal of Consumer Research, Vol. 19 No. 1, pp. 105-119.
Tiago, T., Amaral, F. and Tiago, F. (2015), “The good, the bad and the ugly: food quality in UGC”, Proceedings of the International Conference on Strategic Innovative Marketing (IC-SIM 2014), Madrid, 1-4 September, Vol. 175, pp. 162-169.
Trevinal, A.M. and Stenger, T. (2014), “Toward a conceptualization of the online shopping experience”, Journal of Retailing and Consumer Services, Vol. 21 No. 3, pp. 314-326.
UNCTAD (2016), “The UNCTAD B2C e-commerce index 2016”, available at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d07_en.pdf (accessed 13 December 2016).
Vargo, S.L. and Lusch, R.F. (2008), “Service-dominant logic: continuing the evolution”, Journal of the Academy of Marketing Science, Vol. 36 No. 1, pp. 1-10.
Verhoef, P.C., Lemon, K.N., Parasuraman, A., Roggeveen, A., Tsiros, M. and Schlesinger, L.A. (2009), “Customer experience creation: determinants, dynamics and management strategies”, Journal of Retailing, Vol. 85 No. 1, pp. 31-41.
Vieira, V.A. (2013), “Stimuli-organism-response framework: a Meta-analytic review in the store environment”, Journal of Business Research, Vol. 66 No. 9, pp. 1420-1426.
Wooffitt, R. (2001), “Researching psychic practitioners: conversation analysis”, in Wetherell, M., Taylor, S. and Yates, S.J. (Eds), Discourse as Data: A Guide for Analysis, Sage with the Open University, London, pp. 49-92.
Yuksel, M. and Labrecque, L.I. (2016), “Digital buddies: parasocial interactions in social media”, Journal of Research in Interactive Marketing, Vol. 10 No. 4, pp. 305-320.
Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1996), “The behavioral consequences of service quality”, Journal of Marketing, Vol. 60 No. 2, pp. 31-46.
Klaus, P. (2014), “Towards practical relevance – delivering superior firm performance through digital customer experience strategies”, Journal of Direct, Data and Digital Marketing Practice, Vol. 15 No. 4, pp. 306-316.