Customer satisfaction with complaint responses under the moderation of involvement

Abstract

Organizations build competitive advantage when they design recovery framework with recourse to disgusted customers, given that no two failure experiences are the same. This paper proposed a framework that links user-involvement to customer satisfaction with five complaint response instruments, and specifically, provides insight into how the relational and interactive effects of personal involvement in service-failure encounters create post-recovery satisfaction. Unstructured and semi-structured interviews were conducted with mobile-telephone subscribers/teachers from Federal Government Colleges (FGCs) in the South-Eastern, Nigeria. The findings show that regularity and significance of felt ordeals, and the service-officer’s willingness to interface with disgusted customers were antecedents of social interactions, socio-economic satisfaction, and positive word-of-mouth. However, the findings affirm the proposed framework, conform to the expectations of socio-emotional selectivity theory, and show that customer characteristics, user-involvement, failure-contexts, and providers’ interface to influence satisfaction with failure/recovery experiences. Based on the decay-time of the effects of recovery instruments, the paper recommended proactive and/or reactive approaches, especially on the recognition that failures driven by low-involvement features demand affective and non-pecuniary recoveries, as well as immediate and cumulative satisfaction; and those driven by high-involvement go for a hybrid of utilitarian and symbolic response interventions.

Keywords:

• complaint responses
• customer satisfaction
• compensation

PUBLIC INTEREST STATEMENT

Notwithstanding the consistency and proficiency in managing complaints, service-mishaps exponentially surge even amongst the best providers, due to human and non-human errors. The inevitable corrections amidst competition involve superior values by co-designing recovery framework that captures the disgusted customers’ experiences. This paper proposes a framework that links user-interface and customer satisfaction and, provides insights into how the relational effects in service-failure encounters create post-complaint satisfaction. Data were collected via unstructured and semi-structured interviews with mobile-telephone subscribers/teachers from FGCs in the South-Eastern, Nigeria. The result shows that social interactions are shaped by the regularity and significance of the felt ordeals, and the service-officer’s willingness to interface. However, the findings confirm the proposed framework, conform to socio-emotional selectivity theory, and show that user-characteristics and involvement, failure-contexts, and providers’ interface influence post-complaint experiences. The paper suggests proactive and/or reactive actions, given the recognition that failures driven by low-involvement demand affective, non-pecuniary recoveries, and immediate and cumulative satisfaction; whereas those driven by high-involvement go for a hybrid responses.

1. Introduction

The Nigerian economy provides humongous market potentials for mobile-telephony, because subscriptions surged from less than 500,000 in (De Wulf et al., 2001) to almost 100 million in about a decade; and operators pay over N600 billion taxes annually, provide well-over 3 million employments (Okeleke, 2011), and offer real-time and cost-saving social interactions. As the market evolves intense competition especially in developing economies, players aggressively attempt meeting emerging needs, and offering customer-endorsed remedies amidst service-failures. Although firms consistently improve services or evolve proficient complaint management, service mishaps and complaints exponentially surge even amongst the best providers, due to human and non-human errors; thus, they inevitably lead to displeasure and possible correctional actions. Sharifi et al. (2017) and Sengupta et al. (2015) posit that contemporary subscribers are better informed, and their demand for increasing post-complaint values informs understanding the antecedents of consumption experiences and expectations. However, because service breakdown attracts complaints, negative publicity, and switching; providers critically learn from post-consumption experiences in attempt to minimize such detrimental actions (Delafrooz et al., 2019; Cai & Qu, 2018; Christy et al., 1996; Tang et al., 2017). Approximately, 90 percent of dissatisfied customers avoid the provider (Business Week, 1984) and share their experiences with 10 to 20 others; whereas satisfied customers share their experiences with only 4 or 5 individuals (Komunda & Osarenkhoe , 2012; Abd-Rashid et al., 2014). Other studies (Awa et al., 2016a; Kau & Loh, 2006) show that customers need as many as 12 positive recovery experiences to overcome the negative effects of one ugly experience.

Moreover, it costs more to attract (than to retain) customers, and customers themselves prefer on-going and event-driven engagement to switching and defection (Dechawatanapaisal, 2019;Istanbulluoglu et al., 2017; Sharifi et al., 2017). Awa et al. (2016a) posit that competitive advantage suffices when operators genuinely evolve the creative culture of learning from mistakes, and transform complaints into service-innovation. Studies in mobile-telephony and other services (Tombs et al.,2014; Christy et al., 1996; Ibrahim & Abdallahamed, 2014; Smith et al., 2012; Rosenmayer et al., 2018; Sharifi et al., 2017; McQuilken et al., 2017; Tsarenko & Strizhakova, 2013; Awa et al., 2016b; Bernritter et al., 2017; Chen, 2018; Kau & Loh, 2006; Rosenmayer et al., 2018) link recovery experiences to loyalty, satisfaction, motivation, and customer-lifetime-value. They emphasize classification schemes and adopted deductive approach to provide correlational or anecdotal support on the effect of recovery on satisfaction. Again they focus on product-class, purchase-decision, and task/activity/event; and according to Lee and Lee (2013), they rarely recognize that consumer animosities may be temporally unstable. Arguably, if the influence of complaint-handling on satisfaction is indeed temporally unstable; then, the generalization of results of the growing single-shot of cross-sectional and deductive studies turns questionable, given their mixed and inconclusiveness that span environmental differences. Such methodological choice tests traditional theories, whose regularity often depends on the instruments, sample and sample size, and rarely provides updated consumption information (Reynolds & Gutman, 1988; Silva, 2007).

Organizations sustain competitive positions when they seek deeper probes into the consumer’s perceptual and cognitive structures, as well as the factors that underlie purchase behaviour, customer satisfaction, and customer engagement beyond the explanatory theories and methods required of deductive traditions. Studies associate the criticality of customer satisfaction to user-involvement and corporate reputation (Kau & Loh, 2006; Sengupta et al., 2015; Sharifi et al., 2017; Varki & Wong, 2003), affirming the need for user-developer mutual interactions to heighten confidence, and to aid decision on social and special treatments (Gwinner et al., 1998; Liljander & Roos, 2002). Given the preponderance of cross-sectional studies on recovery, scholars called for regular re-examination of its perceptual dynamics via user-involvement (Maxham & Netemeyer, 2002), and the use of inductive tradition to get updated information (Reynolds & Gutman, 1988; Schiffman & Kanuk, 2009) since customer demeanor may also be unfolded in some social interactions. Further, studies (Bernritter et al., 2017; Cai & Qu, 2018; Tsarenko & Strizhakova, 2013) least integrate marketing and social psychology to the study of satisfaction with complaint responses even when Rust and Metters (1996) emphasized interdisciplinary models, and other scholars (Kinard & Capella, 2006; Dholakia, 2001; Xu et al., 2014; Christy et al., 1996; McQuilken et al., 2017; Reynolds & Gutman, 1988) propose that user-involvement and/or socio-emotional selectivity critically explain and moderate facets of consumer behaviour, and provides the impetus for further inquiries. However, insight into user-involvement and/or dimensionality of risk in disgusts influences developers’ expected benefits, unveil strategic details that underlie customer satisfaction (Varki & Wong, 2003; Ledikwe et al., 2020), narrows or extends social interaction and information processing, and assists in differential manipulation of marketing apparatus to reward behaviours.

Kinard and Capella (2006) and Schiffman and Kanuk (2009) posit that effective recovery and/or competitive advantage is crafted when disgusted customers’ worlds are integrated into the decision-making process. Such interface captures the hemispheral lateralization and rational and irrational decision-making processes; and provides reflective commonalities and mutually beneficial exchanges when qualitative data is used. Therefore, the purpose of this paper is to link the theory of user-involvement to customer satisfaction with five complaint response instruments, and/or to specifically provide insight into how the relational and interactive effects of personal involvement in a service encounter create customer satisfaction. The approach involves updating customer-database and perceived benefits (benefits from consumer viewpoint) amidst environmental dynamics, and extending customer-satisfaction and complaint-response research by proposing a theory-driven framework that captures the proactive initiatives of recovery via advancing the moderation of user-involvement on failure-context and recovery and ultimately satisfaction. Recovery is a bundle of resources used to address failures; and so, the proposed framework examines the antecedents of customer evaluations, and the specific determinants of proficient recovery and the relative significance of each recovery instrument in having higher and quicker build-up in post-complaint satisfaction. The framework compares build-up intensity and duration with respect to post-complaint satisfaction, given that Del-río-lanza et al. (2013) suggest that build-up magnitude and timing are not the same for different recoveries.

Whereas the expectancy disconfirmation theories predict a highest build-up for quality improvement; the social justice and relationship marketing theories emphasize the greater potential of utilitarian and symbolic instruments in salvaging disgusts (Xu et al., 2014). Further, this framework examines the relative significance of five service-rescue instruments (generated from extant studies) and scaled the relative build-up effects, and clarify how well different theoretical perspectives predict the timing and significance of recoveries in handling satisfaction. We examine the relationship amongst failure-context, user-involvement, recoveries, and customer satisfaction using the inductive approach. The rest of the paper covers theoretical review, methods and analysis, proposed framework and results, and conclusion and implications.

2. Past insights

Ample studies (Awa et al., 2016a; Tsarenko & Strizhakova, 2013; Xu et al., 2014) on customer satisfaction and complaint management test theories. Failure/recovery is an exchange process, where complaints are reported, and the developer constructively addresses it to eschew potential damage(s). Complaint-response studies (Bernritter et al., 2017; Rosenmayer et al., 2018; Ibrahim & Abdallahamed, 2014; Sharifi et al., 2017; McQuilken et al., 2017; Tsarenko & Strizhakova, 2013; Awa et al., 2016a; Chen, 2018; Kau & Loh, 2006) are predominantly underpinned by ethical egoism, social exchange, golden rule, and equity theories, and espouse a mix of utilitarian and symbolic dimensions. Ethical relativism considers one universal standard that judge(s) actions; ethical egoism promotes long-run greatest possible balance of good over evil; golden rule emphasizes do onto others like you would want them do onto you; and distributive justice discourages too much richness at the expense of the poor. Whereas utilitarian exchanges literarily emphasize one’s action making the greatest good for the greatest number of people and involves pecuniary impacts; the symbolic exchanges involve socio-psychological resources. Based on these theories, strategists judge recoveries as they affect post-complaint satisfaction. Nevertheless, there is still need for further insights into the interactions between satisfaction and customer-complaints under the moderation of user-involvement, given consumer and competitive dynamics; and the constantly evolving/innovating, iterative, and continuous nature of service failure; as well as the need for proactive and/or reactive redress actions.

There is also the need to complement knowledge by studying how overtime failed recovery affects customer behaviour using the life-span theory of motivation as embedded in socio-emotional selectivity theory (SST). The SST posits that the motivational shifts associated with the shrinking of time-horizons or age influence cognitive processing and memory—when service failures age or repeatedly occur, the selection of emotionally meaningful activities increasingly surges. That is, continual disgust experiences emphasize selective narrowing of social interaction, as complainants favour and recall familiar recoveries that were emotionally rewarding (positivity-effects) and promise minimal emotional risks (Awa et al., 2016b; Balaji et al., 2017; Cai & Qu, 2018; Chen, 2018). SST suggests that the cause of such motivational shifts is not age or passage of time; rather age-associated shift in time perspective. However, the perceived relevance of failure regulates emotions, the quest for emotionally gratifying social interactions and other quests whose benefits are presently realizable. The theory emphasizes high-value on emotional satisfaction; thus, tension seems reduced when complainants feel listened to, and there exist honest interactions that deal with their emotions even before the actual ordeals are fixed. Acknowledging and/or accepting responsibility for complaints and giving empathy, timely and unambiguous explanation, as well as being fair in investigating and analyzing complaints have positive impact on customers’ emotions even when the desired service-standard is delayed (Bernritter et al., 2017; Sharifi et al., 2017).

Complainants want reliable and credible psychological calm-downs that express what went wrong, why, and what the provider will do (or has done) to reintroduce trust, to compensate and/or to avert re-occurrence. Further, the theory focuses on the types of goals to achieve—knowledge-related goals of SST relate to knowledge acquisition and the development of new social interactions and other endeavours that pay-off in future. When complainants have established social interaction with the providers, they seek for emotionally gratifying knowledge, as well as new learning and switching behaviour; whereas when they feel time is short and the opportunity to reap rewards from future-oriented goals is dwindling, they shift to present-oriented and emotion- or pleasure-related goals.

3. Failure-context

3.1. Type and magnitude of failure

Service failures are categorized into two dimensions: core-services/provider-errors/outcomes—actual value-receipt, and product/policy/process failures attributable to delivery manners. Outcome-failures involve utilitarian exchanges, and occur when the basic service-needs were not proficiently performed (e.g., delivery short-of-order); and process-failures span symbolic exchanges and result from impolite actions of salespersons. The dearth of scholarship on the influence of type of failure on satisfaction resorts to the use of resource exchange and mental accounting, which suggest classifying the various types of resources lost due to failure into different categories (Awa et al., 2016b; Chen, 2018; Chuang et al., 2012; Singhal et al., 2013; Tsarenko & Strizhakova, 2013). Supposedly, customer evaluations differ by failure-type because outcome and process failures differ in categories of losses. Service failure is assessed in perceived judgment, given its magnitude, severity, or criticality (Del-río-lanza et al., 2013; McQuilken et al., 2017; Rosenmayer et al., 2018; Sharifi et al., 2017) and not predominantly in conformance-sense. For instance, delays in delivery attract either high (urgency is of essence and delay attracts major consequences) or low (urgency is not of essence) perceived criticalities. Amongst others, the social exchange and equity, prospect, and mental accounting theories emphasize how severity and magnitude of failures affect satisfaction based on customers’ subjective and context-specific view of the harms (Bernritter et al., 2017; Sharifi et al., 2017).

The social exchange and equity theories emphasize exchange of equivalent magnitude of resources (Kau & Loh, 2006; Bernritter et al., 2017; Koc et al., 2017), given that the consequences of failures attract perceived differentiated criticality and/or responses. The prospect theory assumes complainants are more sensitive to losses than to gains, and are more inclined to differences relative to reference points than absolute amounts. Mental accounting suggests that failure and recovery differ and occur sequentially; failure losses are greater than recovery’s gains (Singhal et al., 2013; Varki & Wong, 2003; Chuang et al., 2012). Consistent with these, interaction effects between failure-magnitude and recovery are expected to occur because customers prefer differentiated recovery based on perceived criticality; they prefer discount for less critical scenarios and re-performance for high criticality (Gwinner et al., 1998; Liljander & Roos, 2002; Singhal et al., 2013; Smith et al., 2012). Based on the magnitude of perceived ordeals and given that advances in communication technologies precipitate surge in recoveries’ criticality, the complainant is exposed to many alternative actions: boycotting the product/producer, warning friends and acquaintances to beware, seeking redress via advocacy groups, consumer affairs, law-court, or regulatory agencies. The choice of actions depends largely on established social interactions, severity of the failures, and ability to develop agility to timely react.

3.2. Complaint responses

Attention on complaint-responses is on surge with the emergence of service economies and customer-focused strategies. The recovery paradox supports the inevitability of service-errors and suggests recovery programmes that at least underscore the utilitarian and symbolic dimensions of social exchange and equity theories. Experience shows that “the moments-of-truth” in service encounters may still be vulnerable to breakdowns. Service-error is an antecedent of complaint-response; it represents correctional moment and intention to turn/return complainants satisfied. Complaint-response defines efforts to identify and address perceived errors; to limit their harms, re-establish reputation and promote retention; to reposition trust during the service-encounter before complaints and after the encounter when something had gone wrong; and to discourage sharing of negative experiences, litigation and sanction by consumer activists and consumer right organizations. The recovery team vigorously seeks out, deals with, and learns from the problems even when unreported. Providers rarely respond constructively to ordeals when they do not encourage complaint attitude. Non-complainants may be discouraged by the emotional stress and past disappointments (Vazquez et al., 2010; Kim & Tang, 2016); they deny providers the opportunity to learn and build experiences (Chen, 2018; Ibrahim & Abdallahamed, 2014), and often pose economic burden when complainants boycott the product and spread negative publicity (Sharifi et al., 2017; Zeithaml et al., 2009). In today’s breakthrough in communications, complainants easily share their experiences in the websites/social media to potentially generate ripple effects. However, efficient complaint-response fosters a corporate culture that trains employees to proactively rectify failures before complaints (Koc et al., 2017; Rosenmayer et al., 2018), and attracts greater satisfaction than did initial experience (Battaglia et al., 2012; Zeithaml et al., 2009). The established social interactions, failure-severity, and response-manner define the effectiveness of complaint-response. Given double deviations, employee’s response itself causes more discontent, and narrows social interactions.

3.3. Customer satisfaction

Relational epoch reflects satisfaction, repurchases, word-of-mouth, cannibalization, and data-mining. Although the first three were treated as the indices of post-complaint behaviour, satisfaction drives every element of competitive advantage. Satisfaction, as a latter-type-of-outcome, is an ex-post evaluation or a cognitive and subjective judgment of pre-purchase expectations against post-purchase/actual outcomes. Such comparison normally appears in bipolar measurement continuum bounded at the lower-end by low-satisfaction (expectations exceed perceived outcomes), middle-end by satisfaction (expectations equal perceived outcomes), and at the higher-end by high-satisfaction (perceived outcomes surpass expectations). Often customers make such comparisons for each or some part(s) of the offer (domain-specific-satisfaction) or for the entire offer (global satisfaction). These suggest that satisfaction is a complex relational and economic activity spanning inputs from vendors and other independent institutions, and continues even by the manner complaints and doubts are handled. Thus, inducement of feelings and emotional states through respects, courtesy, warmth, empathy, and assistance impact on post-complaint satisfaction.

Measuring satisfaction assists to identify the most profitable customers and to increase loyalty amidst competition; to calculate customer-lifetime-value; to increase sales per-customer/customer group; to increase switching costs, customer retention, and viral rates; to reduce marketing costs; and to build meaningful dialogue that fosters relationship(s) and genuine loyalty (Cantor & Li, 2019; Kim & Tang, 2016). In recovery sense, Maxham and Netemeyer (2002) emphasize satisfaction with transactional recovery and overall-firm satisfaction: accumulation of prior experiences, including recent ones. Satisfaction with transactional recovery influences word-of-mouth more than the overall-firm satisfaction, and long-term satisfactory experiences hinder recalls of recent ones that often prompt referral (Ibrahim & Abdallahamed, 2014; Sharifi et al., 2017). The latter takes more premiums because repurchase intention is more associated with customer loyalty than is word-of-mouth, and mere satisfaction is not sufficient to generate repurchase intentions.

3.4. User-involvement

Contemporary firms shun solo-operations, win-lose, or zero-sum thoughts; they embrace Alvin Toffler’s (1981) great vision of do-it-yourself. User-involvement suggests uninterrupted social interactions amongst core-stakeholders/actors to dispel the myths that art-works were unilaterally designed by developer’s intrinsic ingenuities, and to minimize investment in recovery (Pressey & Mathews, 2000; Vargo & Lusch, 2004; De Wulf et al., 2001). Service failure-induced behaviours involve passive, voicer, irate, and activist, which are likened to public or private actions: the passive, less likely takes action; the voicer registers complaints and show less likelihood to switch and/or share his experiences; the irate switches and diffuses his experiences; and the activist is much more vocal, complains and takes counter-actions (Zeithaml et al., 2009). Competitive advantage speaks when social interaction with service-agents identifies the sources of negative emotions and defuses them before compensations (Del-río-lanza et al., 2013; Engen & Magnusson, 2015). Studies (Christy et al., 1996; Kinard & Capella, 2006; Pressey & Mathews, 2000; De Wulf et al., 2001) show that greater perceived benefits and strengthened relationships arise from developers that require high levels of social interactions than those that demand moderate to low levels of user–developer interactions. The customers exhibit physical and psychological motivations to invest in disgust experiences, and to seek possible alternatives, and dissonance-reducing and problem-solving information, capable of reducing perceived risks of future decisions.

However, user-involvement defines the level of personal-relevance a disgust encounter holds on a customer (Han & Kim, 2017; Rokonuzzaman et al., 2020). It measures consumer’s subjective evaluation of the perceived value of service offerings, and the different levels of involvement, which is based on the assessment of an individual’s needs and motives, affect purchase and repurchase decisions. Subject to its dimensionality, user-involvement mediates behaviour and measures the social interactions to make decisions and the degree to which reason vs. impulse, desire vs. logic or passion vs. prudence influence decisions (Awa et al., 2016b; Chen, 2018; Kim & Tang, 2016). Consumers aggressively seek information and complaint on disgust-experiences that are expensive, infrequent, risky, expressive and ego-involving, unlike when failures require impulse and routinized behaviour, low perceived-risks, less thoughts and less aggressive information search; and decision based on environmental cues, in-store stimuli, and emotion-focused coping. When failures are severe and recurrent, even strong interfaced recoveries may get customers more upset. Less commitment to providers assumes enlarged social interactions, more transaction-focused and immediate recovery expectations amidst service failures; whereas high commitment to providers narrows social interactions and assumes overtime accords settle the ordeals and create even more customer satisfaction (Del-río-lanza et al., 2013; Kim & Tang, 2016; Zeithaml et al., 2009). Subject to situations, decision on mobile-telephone network demands both high and low involvements, given that purchases may be made on impulse and at the same time risky because of network fluctuation. Studies found that the level of products’ perceived-relevance influences satisfaction evaluations during disconfirmation process, and involvement mediates consumer behaviour and plays significant roles in service-context (Cai & Qu, 2018; Chen, 2018; Koc et al., 2017). On the effect of personal-relevance on product evaluation and translating this effect to service-industry, involvement mediates failure-context and recovery expectations (see figure 1).

Figure 1. A framework of customer satisfaction with service recovery experiences

4. Methods

We define behaviour(s) of interest, as well as the discriminating ways to know, observe and measure such behaviours. The growing inquiries are criticized for using confirmatory statistical techniques and traditional theories; however, positivist/measurement-model-statistic is good, given the absence of irregularities in the underpinning theories, which may be attributable to other factors: instruments, sample, and sample size (Silva, 2007). However, the differences between people, institutions and objects studied require actors’ social interaction to understand the subjective meaning of the phenomena. Recovery studies require not just explanatory theories but methods that offer broader explanations; thus, inductive/qualitative approach serves as a useful alternative. In mobile-telephone inquiries, social actors’ active role and deeper-sense of knowledge are necessary to make informed decisions since the market contributes significantly to different sectors’ developments. The first protocol relates to research objectives and conceptual framework; and the second, discovery and explanation of people’s experiences by adopting two-rounds of one-on-one in-depth interviews to boost confidentiality, to compare opinions on the state of affairs, and to provide insight into the validity and reliability of instruments. Heavy-users of mobile-telephone services drawn from FGCs in South-Eastern Nigeria formed the population and the mode of sampling was purposive and snow ball; experiences and judgment were used to identify the first few cases whose opinions best represented that of the community, and provided insights into identifying other heavy-users.

Purposive sampling is suitable for exploratory study, because it involves subjects with rich knowledge of the subject matter. The selected cases in Table 1 permit reasonable comparison in relations to the research objectives and not for statistical generalization. This mix was informed by Miles & Huberman’s (1994) categorization, which affirms answers from one-group/section alone rarely develop in-depth understanding in complex studies. The mobile-phone subscribers were chosen on the strength of the 21^st century explosion of mobile-telephony in Nigeria, where GSM and CDMA operators maintain predatory and cut-throat maneuverability. Each of the five South-eastern states has one FGC (co-education) and one Federal Government Girls’ College (FGGS); thus, for gender sensitivity, the opinions of subscribers from FGCs located in states where GSM and CDMA networks interfaced were studied. By this, FGC, Okposi was dropped because as at the time of this study, only GSM operated. The schools were assessed equally, because they are funded by federal government and opinions of 10 full-time teachers in each school (40 on the whole) were sampled without emphasis on disciplines rather on the possibility of getting authenticated information.

Table 1. Participants’ profile

	FGCs	Position	Cases
1.	FGC, Nise (Cases A1 to A10) A1–2 A3–4 A5–7 A8–9 A9–10	Computer-operator Science-teacher Head-teacher Hall-warden Admin officer	10
2.	FGC, Enugu (A11 to A20) A11–12 A13–14 A15–16 A17–18 A19–20	Accounts officer Senior Science teacher Staff-adviser Games teacher Labour-master	10
3.	FGC, Okigwe (A21 to A30) A21–22 A23–24 A25–26 A27–28 A29–30	Technical-officer Guidance & Counselor Home-Economics instructor Laboratory assistant Fine Art instructor	10
4.	FGC, Ohafia (A31 to A40) A31–32 A33–34 A35–36 A37–38 A39–40	Welfare-officer Literary art instructor Library-officer Security-officer House-master	10
	Total		40

4.1. Cases/interviews

We adopted fairly unstructured and semi-structured interviews. The unstructured interview(s) offers open, flexible, experimental, and revealing pattern of studying complex social interaction, and is suitable for small population (Miles & Huberman, 1994). This improved the grounds for semi-structured interviews, broadened knowledge on the subject-matter, and assisted to identify initial concepts/codes and to propose a framework. The questions asked though triggered other probes were “what recovery factors were considered critical in mobile-telephone services? What is the link between failure-type and magnitude, recovery factors, and satisfaction and how did user-involvement moderate such link?” In the narratives, the critical concepts/codes identified were personalized care, explanations, compensations, timeliness, and atonement; and immediate and cumulative effects were the consequences of satisfaction. For failure-context, the round identified that some disgusts in mobile-telephony attract utilitarian and symbolic implications (type), and regularly or irregularly occur with severity (magnitude)—they influence subscribers’ evaluations of failure/recovery experiences and serve as reference points to judge recovery’s fairness (McQuilken et al., 2017; Smith et al., 2012). The interviewees were 8; two from each school. Semi-structured interviews involved 40 out of 60 subscribers originally invited. E-mail contacts of the potential interviewees were collected and a formal letter of introduction/invitation mailed ahead of time, including the explanation of the research purpose and promise of confidentiality.

The bulk of the questions centred on the factors, failure-context, user-involvement and satisfaction; and depending on each interviewee’s disposition, the exercise on the average lasted at most 30 minutes. The interview was conversational to motivate interviewees to recall, reveal and construct aspects of subjective experiences and interpretations that make the discussions coherent and meaningful. Conversational style was supported by some semi-structured questions intended to keep the interview flows and to keep the participants focused and to time. Semi-structured interviews validate and/or confirm unstructured interview’s outcomes. Although convergence of opinions started with unstructured interviews, semi-structured interview reduced missing new and validated information, and ensured in-depth explanation of participants’ social world. The responses were transcribed verbatim to minimize superficial meanings of transcripts, and these were stored in Word document for easy retrieval. With respondents’ consent, tape-recorder records the interviews to defuse reliance on memories.

5. Coding and analysis

The sequence of data analysis assists to develop models, and to interpret findings. The information for testing the framework results from generation of codes and the initial coding sheet that contained four categories of codes—overall complainants’ feelings about past experiences as mediated by failure-context and involvement; perception of individual recoveries; what leads to post-complaint satisfaction; and the relationship between failure-context, user-involvement, recoveries, and satisfaction. Coding attracted refinement and further break-down of categories where needed, and concepts/codes drawn from theories form the bases for categorizing raw-data. Table 2 reports simplified definitions and characteristics of theoretical codes using code names; definition of what the codes stand; and description of how to know when themes associated with each code occurs. Coding was done by two coders using NVivo software for data reduction, thematic coding and weight. The coding process imported textual data as documents into NVivo, applied the theoretical pre-defined and post-defined (data-driven) codes into raw-data, connected codes and identified themes, and developed model. Certain precautions were taken to ensure relative concordance of coding between coders.

Table 2. Details of codes

Concept/code	Code definition	Descriptive characteristics
Type-of-failure	Outcome and process failure encounters; outcome answers “what” and process answers “how” questions	Outcome—unavailability or partial/fluctuating networks. Process—salespeople’s rudeness.
Magnitude-of-failure	Size and regularity of losses due to service-failure.	Complaints frequency and their seriousness and importance to the complainants.
User-involvement	User-developer interface to shape recovery by the relevance of failures.	Whether accord is transactional and relational.
Personalized care	Psychological attention to address disgusts and emotions.	Courtesy, politeness, empathy, respect, and willingness to listen.
Explanation	One-to-one sharing of rescue tips and reassurance that everything is intact or will soon be, to address the ordeals completely.	Simplified details of what went wrong, why, and provider’s rescue actions.
Compensation	Administering utilitarian and/or symbolic cues amidst failures.	Discounts, free merchandise, refunds, coupons, etc.
Atonement	“Am sorry” and assurance of non-repeat of the disgust situations.	Admission of mishaps and guilt, apologetic, concern, courtesy, respect, and regrets.
Timeliness	Speedy response to customer issues; subject to many factors, the sooner the better.	Promptness, responsiveness, and absence of waiting.
Customer-satisfaction	Subjective judgment of expectations against actual outcomes.	Repurchase, advocacy, and product-supports. Outcomes above or equal ideals

Both coders use the same initial coding sheet; and a pre-coding test was done with 8 cases, which were coded independently by the coders, who then compared and discussed their findings. The exercise strengthened the terminologies and developed common understanding of the different codes. Cases were randomly divided into two groups of 20 each, and each group was independently coded and analyzed. To ensure codes generated from theories apply to the raw-data, the transcribed results in the unstructured interviews were cross-checked for face validity and manually coded into pre-defined and post-defined categories, and reliability was subsequently measured to ensure the theoretical codes were credible and would apply to subsequent raw-data (see table 3table 4 above). table 3 reports that four informed judges related the preliminary quotes to the categories and inter-rater reliability involving percentage agreement with additional two colleagues, given that the data coded were nominal and required little or no judgments by the coders.

Table 3. Reliability analysis

Scope	Judges	Reliability
		First-two judges	Second-two judges
Complaint-responses	4	0.82	0.87
Customer satisfaction	4	0.84	0. 80
Failure-context	4	0.80	0.74
User-involvement	4	0.78	0. 88

Table 4. Concepts/codes and supporting themes

Theme	Case evidence	Cross-case support
Personalized care	“When I suffer service ordeals, the service-agents show me courtesy. They listen politely and patiently to what I had to say; they show concern and understanding about the problem I had. These are the most important recoveries because first impression matters. But the entire exercise turns meaningless to me if the same problems keep re-occurring” (A7). “Sometimes, the service-agents handle the problems with some bit of expertise that dissuade me from switching completely; I subscribe to more than one network because I do not want problems when I make my important calls. The only issue I take very seriously is when I perceive the employees displaying non-neutrality in resolving problems and when the recovery protocol is time consuming” (A36, A10). “When my service ordeals are regularly addressed proficiently, I rarely switch because further relationships will sort-out my issues. To further show my satisfaction; I show loyalty, use more of the provider’s service today and tomorrow, and refer others” (A3). “When am disappointed with recovery, I discourage others.” (A15, A3). “I subscribe to many networks because none perfectly solves my problems and so, am loyal to all but not completely loyal to any” (A40).	A1, A2, A4, A5, A6, A8, A9, A11, A12, A13, A14, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34, A35, A38, A39, A16, A17, A18, A19, A20, A21, A37.
Explanation	“When I suffer service-failure, the service-agents often tell me in a friendly manner why the service failed in the first instance; they seem to be interested in helping me out” (A3, A14). “Why the original problem occurred was reasonably explained in a manner that it rarely occurs again.” (A5, A13). “Those reps that are well-experienced spend less time and energy to depict my ordeals and ways to resolve them and use more friendly approach” (A2, A15). “The service-agents were attentive and tried hard to resolve my issues though no reasons were given to cause the ordeals.” (A9, A12). “Whether they explain to me or not, once a failure is encountered, everything ceases to be same again; am also in businesses and so, I need to be in touch with my associates always. I will not want to get the normal unprofessional excuses of blaming network instead I go for many networks” (A10). In fact any networks that address my ordeals well and keep to it win(s) me (A20).	A18, A16, A17, A19, A1, A4, A6, A8, A7, A15, A11, A21, A22, A23, A24, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34, A35, A36, A37, A38, A39, A40.
Compensation	“At least I need reinstatement when I suffer service-failure except if the provider does not want me again. They are many others out there looking for me.” (A12). “Aside showing me concern, the provider should give me something to fall back on or something that will drive me back to them.” (A25). Because of the nature of mobile-telephony, I prefer extra service (in MB for browsing) to money refunds, discounts, and firm promises“(A20).	A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A13, A14, A15, A16, A17, A18, A19, A21, A22, A23, A24, A26, A27, A28, A29, A30, A31, A32, A33, A34, A35, A36, A37, A38, A39, A40.
Atonement	“When I suffer service-failure, I feel psychologically calmed down when the provider expresses concern and regrets and goes further to assist me resolve the issues.” (A11) “Often saying am sorry does it all and when I see signs of concern, I feel more relaxed.” (A18). “Because of the low involvement nature of some disgusts in mobile-telephony, when the provider says ‘am sorry,’ every other things after get me satisfied” (A24).	A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A12, A13, A14, A15, A16, A17, A19, A21, A22, A23, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34, A35, A36, A37, A38, A39, A40, A20.
Timeliness	“I feel well-comfortable when my ordeals are attended to appropriately and on time; delays in the pre-process and post-process phases of response get me off.” (A14). “If the call centre addresses my issue immediately and tells me what to do to avert reoccurrence or what to do when next same problem occurs, I get satisfied” (A28) “Whether delayed or prompt response borders on prevailing situations; you may rush responses and end-up causing more mess” (A6). “The provider is expected to take minimal time to study sensitive ordeals to avoid messing it up” (A11, A6).	A1, A2, A3, A4, A5, A7, A8, A9, A10, A12, A13, A15, A16, A17, A18, A19, A21, A22, A23, A24, A25, A26, A27, A29, A30, A31, A32, A33, A34, A35, A36, A37, A38, A39, A40, A20.
Type of failure, magnitude of failure customer involvement and customer satisfaction	“All my calls are important and affect my life and so, type and magnitude of failure may not really matter much. When I want to make calls (especially urgent ones) and the network is bad, I feel terribly mad and any recovery infuriates me” (A18). But I will probably be ok if in resolving the issue, the provider involves me in reducing the magnitude and reoccurrence, as well as in choosing the right instruments that will properly reinstate me (A16). The nature and magnitude of failure define my post-recovery satisfaction, especially when am not involved in the recovery exercise; when the core-service or the delivery process fails, my satisfaction immediately reduces but my adverse action may be delayed if am called for input to get the issue resolved (A14). I can say that competition in the mobile-telephony necessitates failure context and user-involvement working together to enrich customer satisfaction (A22).	A1, A2, A3, A4, A5, A6, A8, A9, A11, A12, A13, A17, A19, A20, A21, A23, A24, A25, A26, A27, A28, A29, A30, A31, A32, A33, A34, A36,, A37, A35, A40, A38, A39.

The instruments were reliable since they internally relate to the factors at levels above 0.70. Thematic content analysis provides the core-skills to explore and transform themes and issues emerging from the data. The approach is theory-and-data-driven/inductive; data reveal themes and relevant constructs and associations amongst constructs. Constant comparative technique was used to facilitate more rigorous analysis; this involved assessing and re-assessing transcripts along with emerging new data in order to identify and refine the themes and/or categories in the context of what is shared by complainants. The “chain of evidence” built in the course of analysis facilitated sense-making and identification of similarities across cases. The textual segments from the case evidence were selected according to their closeness to customer-responses and satisfaction, and then assigned to the relevant categories.

6. Framework and results

In SST context, the length of user-developer interaction matters. Often developers adopt social justice theory, because consumption expectations are pre-purchase/pre-trial beliefs about product-delivery attributes, which serve as subjective reference against which actual and ideal performances are judged (Bernritter et al., 2017). Recovery involves interrelated instruments and processes; thus, to understand how complainants perceive them involves unfolding situations that shape the entire process through respondents’ own narratives. The proposed framework (see Figure 1 below) infers propositions and direct social interaction, and shows the capabilities of each instrument (and its factors) in reinstating complainants. It spans proactive and reactive social interactions, and aids customer evaluations based on how failure-contexts in type and magnitude, as well as how user-interface moderates recoveries and post-complaint satisfaction. The themes were theory-driven while the factors were data-driven and clustered conceptually based on participants’ opinions. From theoretical and empirical insights on handling perceived inequities and links between complaint-handling and corporate performance (Ibrahim & Abdallahamed, 2014; Osarenkhoe & Komunda, 2012), the framework explains the build-up and decay nature of the latent factors, and recognized robust citations in business press for such factors in different studies involving measurement-model-statistic: personalized care (Battaglia et al., 2012; Bernritter et al., 2017; Smith et al., 2012), explanation (Awa et al., 2016a; McQuilken et al., 2017; Sharifi et al., 2017), compensation (Bernritter et al., 2017; Ibrahim & Abdallahamed, 2014; Rosenmayer et al., 2018), atonement and timeliness (Chen, 2018; Kim & Tang, 2016; Zhou et al., 2012).

Personalized care refers to the psychological attention intended to calm failure-induced anger. Studies (Battaglia et al., 2012; Koc et al., 2017) found courtesy, empathy, respect, politeness, extra-efforts, neutrality, and willingness to listen have significantly stronger impact on complaint-handling, post-complaint satisfaction, positive publicity, and repurchase behaviour than redress. Edmondson (2011) found that handling health complaints is at the mercy of frontline officers’ approach; thus, encouraging open discussion, welcoming queries, and displaying humility and curiosity to assist significantly affect customer satisfaction, referrals, and profitability. Explanation blends communication devices properly to account for the complaints and/or to change customers’ negative beliefs in order to regain trusts and close-up loops. It involves dissatisfied customers getting further details to sustain or re-establish peace of mind. Such details relate to why the disgusts and the providers’ commitment to resolving them. Ample studies (Komunda & Osarenkhoe, 2012; Edmondson, 2011; McQuilken et al., 2017) show that knowledge and picture of the ordeals, as well as professionalism and tone of response significantly impact on post-complaint satisfaction. Further to re-establish equity amidst dissatisfaction, complainants want the realities of justice theory to avert detrimental actions. Compensation redresses the complainants for the problems caused by the failure in order to reach equity situation.

Justice theory (Andaleeb et al., 2012; Casado et al., 2011) propose that at least the complainants need re-established equity (putting things right or eliminating the causes of the ordeals perhaps through reverting the transaction and changing the product, refunds, additional services, or discounts) otherwise they remain dissatisfied and perhaps engage in counter-productive actions. The justice theory emphasizes valuable reward that redistributes social resources—all things being equal, atonement believes in admission of guilt, default, and expression of concern, regret(s) and other psychological calm-downs associated with disgusts. “We are sorry; we will make-up and we assure you it won’t re-occur” offers psychological redress to some disgusts (Walster et al., 1973; Awa et al., 2016a). The social justice theory suggests that one of the significant elements for evaluating recovery lies on procedural justice; subject to many factors and situations, this refers to the promptness with which complaint resolution process is executed (Al-Foqahaa, 2010; Cho et al., 2003). Aside the studies correlating these recoveries with post-complaint satisfaction (Smith et al., 2012; Kansal & Singh, 2013), the respondents’ narratives revealed these factors as critical. They are particularly salient to mobile-telephony and are easily acted upon by managers, and can be manipulated through written scenarios in experimental contexts. User-involvement was captured because it has received less empirical attention in recovery and satisfaction contexts though it has ample citations in business press. It links the choice of recoveries and failure-context and highlights their potential chain-influence on satisfaction. The a priori expectation is that the interaction between failure-context, recoveries and satisfaction is moderated by user-involvement.

Similarly, immediate and cumulative effects were the consequences of satisfaction; they are central to marketing and customer–relationship studies since studies (Chen, 2018; Zeithaml et al., 2009) promoted their significance along advocacy, repurchase intentions, and product-support. Customers evaluate failure/recovery based on type and exchange outcomes (Smith et al., 2012). Type and magnitude of recovery in the framework do not depend solely on failure-severity and the principles of mental accounting; rather, it conceptualizes value-driven approach to the extent that optimal recovery is based on post-complaint value delivery. Studies (Chen, 2018; Komunda & Komunda, 2012) show that resource exchanges are determined by failure-type and magnitude, as well as the recoveries’ attributes in forming justice perceptions. Thus, the perceived value(s) delivered depends on customer-lifetime-value and other criteria that influence customer evaluation of firms. However, the proposed framework may not be too new but the use of inductive approach to test it provides contrast; it shows that failures result to socio-economic losses (e.g., money, time, status) and the providers attempt to proficiently recover complainants. The framework recognizes that such relationship demands user-involvement as a moderator and/or driver of further studies. Failure-context defines the individualized state of mind, and user-involvement shapes how customers seek, process, and transmit information, and make decisions, post-purchase and post-recovery evaluations (Cai & Qu, 2018; Schiffman & Kanuk, 2009).

The moderation of user-involvement in failure-context, recoveries, and satisfaction reinforces marketing programmes that activate favourable involvement process and marketing communication (Koc et al., 2017), given that complainants exhibit motivation to gather, comprehend, elaborate, and assimilate information, and enjoy satisfaction when their levels of physical and/or mental involvements in developing recovery packages surge. For same disgusts, different recoveries rarely attract the same levels of satisfaction; thus, the need for user-involvement to have further insight into satisfaction. In the loyalty ladder, active customer progresses from loyal to product-supporters and ultimately to advocates when he interfaces in resolving failure-experiences that require high rationality and a lot of thought and information. Network failure, for instances, is a serious issue that needs user-developer interface to ensure mutual exchanges.

6.1. Theme 1: personalized care

A senior science teacher (A7) affirmed that courtesy, politeness, patience, concern, and understanding are critical recovery strategies. Studies (Del-río-lanza et al., 2013; Engen & Magnusson, 2015) report that employees’ response manners influence post-complaint satisfaction since failure to deliver core-services is not always the cause of disgusts. A40, A10 and A36 noted that they are not completely loyal to any network, because service-agents sometimes display unprofessionalism in handling ordeals and engage mostly in attributions; we do not switch completely rather favour more than one network to avoid serious problems. Although courtesy and respect are critical, they pair with knowledge (Zhou et al., 2012), professionalism (Battaglia et al., 2012a), and concern, extra efforts, procedural issues, and employee neutrality (Awa et al., 2016b) to impact significantly on satisfaction. However, more cumulative post-complaint satisfaction emerges when attentiveness pairs with credibility (Zhou et al., 2012; Komunda & Osarenkhoe, 2012).

Show of feelings and concerns over disgusts is affective and attracts significant immediate satisfaction, though the persistence of same ordeals generate decay and divided loyalty and negative word-of-mouth, given that subscribers rarely afford to displease themselves always (Engen & Magnusson, 2015; Zhou et al., 2012). Other respondents shared similar views as Del-río-lanza et al. (2013) show that indicators of personalized care calm emotions and avoid double deviations. Further, inequity and long protocol in resolving issues are perceived critical (A10, A36)—with over 80 percent cross-case support, minimized favoritism and compressed processes interest dissatisfied customers. The respondents want mobile-network providers that address issues fast regardless of situations in order to build immediate and cumulative effects on satisfaction. Edmondson (2011) shows that encouraging open discussion, welcoming queries, display of humility, extra efforts, procedural simplicity, fairness and curiosity impact significantly on indices of customer satisfaction.

6.2. Theme 2: explanation

32 cases differently imply that providers’ willingness to help-out by high-lighting, in friendly and solution-specific manners, failure sources/reasons and resolutions, as well as ways to avert re-occurrence is key to post-complaint satisfaction but regular attribution suggests subscribing to more than one networks. Such cognitive and intangible rescue processes have significant immediate and cumulative effects on satisfaction, especially when the ordeals’ root causes are identified and attribution reduced. Complainants desire reliable and credible explanation and re-assurance (Koc et al., 2017; Smith et al., 2012); they want simplified details of what went wrong, why, and compensation plans and/or plans to avert re-occurrence (Kansal & Singh, 2013; Battaglia et al., 2012). However, because mobile-telephone subscribers suffer many ordeals and receive many explanations, they display less cumulative satisfaction and more immediate and transactional satisfaction—recent and salient explanations amidst many ordeals and responses influence recalls and referrals. Attribution decreases user-involvement and satisfaction (Engen & Magnusson, 2015); rather acknowledging and accepting responsibility and giving timely, credible, and unambiguous explanation, as well as being fair in investigating complaints significantly impact on satisfaction (Battaglia et al., 2012; Zhou et al., 2012). Other studies (Chen, 2018; Sengupta et al., 2015) found service-agents’ experience and knowledge to affect clarity and manner of explanation, and ultimately exacerbate the situation or reinforce post-complaint satisfaction.

6.3. Theme 3: compensations

Participants (A12, A20, A25) and 39 cross-case supporters believe that compensation-driven satisfaction builds up with a hybrid of psychological and economic calm-down for immediate and cumulative satisfaction. Such hybrid delays decay effects of compensation to the longest possible time after recovery, and the cumulative effects of MBs reflect on product-support and advocacy behaviours. Studies (Battaglia et al., 2012; Smith et al., 2012) show that service delays (no direct financial loss), discounts and free-gift (compensation) amidst no explicit loss impact positively on satisfaction. Further, mere build-up of psychologically-driven satisfaction is not sufficient, given that more premiums are attached to cumulative than immediate satisfactions when forming repurchase intentions. Studies (Chen, 2018; Koc et al., 2017) affirm that psychological affection, discounts, free-services, and cash refunds significantly impact on post-complaint satisfaction; and others (Balaji et al., 2017; Kansal & Singh, 2013) found significant impacts between percentage of financial loss reimbursed and satisfaction with recovery; thus, concluding that partial compensation rarely creates full satisfaction.

6.4. Theme 4: atonement

A11, A18, and A24 with 38 cross-case supporters alluded to these assertions. All elements of atonement are effective; they have significant immediate impact and short-lives and less enduring impact on satisfaction. Thus, the effects of atonement on satisfaction show the shortest decay-time, given that they reach the peak and then begin to drop quickly to zero, especially when the issues pleaded upon kept reoccurring. There is a mixed scholarly support to these; atonement significantly affects post-complaint satisfaction (Istanbulluoglu et al., 2017; Tsarenko & Strizhakova, 2013); whereas, cross-context studies (Cantor & Li, 2019; McQuilken et al., 2017) reported otherwise. Further, pairing atonement with others is more effective (Smith et al., 2012; Barger et al., 2017) and directly and/or indirectly affects interactional justice and complaint-handling satisfaction.

6.5. Theme 5: timeliness

Procedural justice suggests that subject to environment, complainants need prompt response. A6, A11, A14 and A28 with 36 cross-context support explained timeliness as a multi-dimensional instrument whose usefulness depends on the problems’ complexity. Its immediate and cumulative effects on satisfaction build up when the entire process is effectively compressed; and the process emphasizes the sooner the better and is simple and rarely allows the same and/or related problems to re-occur. Scholars battle between quick and delayed resolutions (Battaglia et al., 2012; Zhou et al., 2012); undue delays lower service evaluation (Balaji et al., 2017; Koc et al., 2017), corporate image (Istanbulluoglu et al., 2017), and customer satisfaction (Del-río-lanza et al., 2013). Though late responses are significantly inferior to slightly delayed responses (Cantor & Li, 2019; Smith et al., 2012); immediate responses may be less effective than slightly delayed responses (Tsarenko & Strizhakova, 2013). Fast-food customers appreciate response-speed whereas less speed associates with complex situations (Gurney, 1990). Timeliness rarely correlates significantly with satisfaction, rather what counts most is what providers responded with and how it was expressed (Cantor & Li, 2019; Del-río-lanza et al., 2013). Awa et al. (2016a) proposed three fronts of timeliness: timeliness is important in non-financial losses, after unreasonable delays, and response-time is context-and-mode-specific.

6.6. Theme 6: moderators

The relationship between satisfaction and recovery was shaped by failure type and magnitude, and user-interface. “For minor problems, I expect affective responses but when I make serious/urgent calls and the network is bad, I feel terribly bad and expect prompt responses. I get mad when this persists” (A18). This goes with wide cross-context. Thus, failures driven by low-involvement features demand affective and non-pecuniary recoveries, as well as immediate and cumulative satisfaction; whereas those driven by high-involvement go for hybrid instruments. Studies relied on the principles of resource exchange and found that satisfaction judgments differ by failure type and magnitude (Cantor & Li, 2019; Smith et al., 2012); as losses increase with failures, the customer continually perceives unjust exchanges. Similarly, higher magnitude or severe failure attracts lower satisfaction (Cantor & Li, 2019; Koc et al., 2017). A16, A22, and A16 supported by all other cases, say that regardless of type and magnitude, their disgusts are minimized when they are interfaced in devising ways of reducing the effects and the chances of re-occurrence, as well as in choosing the right recoveries. Scholars (Istanbulluoglu et al., 2017; McQuilken et al., 2017; Vargo & Lusch, 2004) emphasize trapping disgusted customers’ ingenuity to minimize failure occurrences/reoccurrences, and to surge customer satisfaction.

7. Conclusion and implications

The accord between recovery, user-involvement and customer satisfaction offers through-put into the nature of buying behaviour. Consumers weigh input/output contributions and compare them to those of referent others in similar situations to ensure equity. When outcomes fall-short of ideals, inequity and perhaps complaint result. The customer is the reason for firms’ existence; hence, the need for effective and tangible recoveries to re-generate or re-engineer satisfaction amidst disgust. Efficient management and alignment of complaints and recovery generate higher satisfaction, protect the providers against disgust behaviour, and build behavioural and attitudinal loyalty. Most empirical inquiries that connect recovery and satisfaction obtained diverse results that are context-specific and survey-based. In mobile-telephone context; this paper pursues greater understanding of how dissatisfied customers assess recoveries in relations to post-complaint satisfaction by proposing an integrated theoretical framework that captures failure-context (types and magnitudes) and the moderation of user-involvement in the choice of recoveries. In-depth interviews were conducted to provide valuable insights into active solution-seeking process (personal-efforts) and providers’ instituted passive solution-seeking process. The findings led to the general support for the proposed framework, conform to the expectations of socio-emotional selectivity theory, and show that the significance of the felt ordeals in purchase situations and service-officers’ willingness are antecedents of post-complaint satisfaction. Thus, customer characteristics, user-involvement, failure-contexts, and providers’ behaviour interface to influence satisfaction with failure/recovery experiences.

Based on social exchange, our proposed theory-driven and proactive framework contributes to the theoretical and methodological discourse within services marketing and consumer behaviour, and shows how involvement of disgusted customer in a rescue exercises updates customer database with the dynamic effects of recoveries and thus, moderates failure-context and instruments and ultimately satisfaction. The framework provides antecedent factors that underlie post-complaint satisfaction; and substantially expands relationships to, and affirms, socio-emotional selectivity theory, and the theoretical principles that explain customer responses to failure/recovery experiences. Further, the salience and recency principles differentiate satisfaction with transactional recovery and overall-firm satisfaction—long-term satisfactory relationship hinders recalls of recent experiences. Immediate and cumulative post-complaint satisfaction psychologically and systematically grows when there exists equity and hybrid of utilitarian and symbolic recoveries. Social interactions are often narrowed to maintain emotionally gratifying activities in the light of regular failures. Of the instruments studied, the decay-time of the effects of compensation and explanation on post-complaint satisfaction is quite longer than that of timeliness; whereas, personalized care and atonement have the shortest decay-time and the most effective pedigree. Attribution and failures beyond the service-officers’ control is possible and make for divided loyalty.

Subject to failure-type and its magnitude, user-involvement creates a scenario where the effects of satisfaction significantly spill-over in immediate and long-run terms; thus, the effect of higher previous post-recovery satisfaction makes it easy to regain immediate lost grounds and this is guaranteed by user-interface. The essence of time depends on the ordeals’ magnitude and complexity, as well as the established relationship; for minor issues and where transactional paradigm exists, satisfaction decays in a short-run because of its immediacy though it lives a bit longer and in cumulative form when hybrid recovery is timely administered. However, the build-up effects of satisfaction are hard to measure when some avoid complaining, especially on minor and trivial issues. For complex ordeals and when relational paradigm suffices, disgusted customers show willingness to wait to resolve the issue once and for-all and so, build-up effects of satisfaction grow even when recovery is delayed. These imply:

• less emphases on affective instruments since their build-up effects decay in the shortest time.

• minimize protocols and tactically apply a hybrid of utilitarian and symbolic package

• de-emphasize attribution; rather explain to the complainants what to urgently do to critically allay their fears.

• emphasize user-interface to improve customer satisfaction.

8. Limitations and further studies

This study reports how user-involvement moderates complaints and post-complaint satisfaction. However, it is limited by relatively small sample size and data from single-service context; thus, multiple samples from different populations and/or replication across customer-groups and industry settings will increase generalizability of the identified causal relationships. The measures of codes are subjective and prone to biases; therefore, extended and cross-validation may serve as opportunities for further studies. Further, the study did not unfold the extent and magnitude of utilitarian and symbolic instruments that could calm-down the disgusted customer and get him reinstated. It also did not inform distinctively on waiting time and personal time (and effort) that customers need to attempt a resolution. Future research should validate our findings and emphasize on bridging the gaps, so that the conclusion drawn may be properly contextualized and compared with the findings of other investigations.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Hart O. Awa

Hart O. Awa has L-8298—2019 as a Web of Science (WOS) ResearcherID, powered by Publons of Wiley. He is a professor of Marketing, and a research point-man of the Department of Marketing and Faculty of Management Sciences, University of Port Harcourt, Nigeria. Hart writes and reviews for top-indexed and WOS journals, many of which are housed in Emerald, Taylor & Francis, Elsevier, Wiley, and Sage. He is a member of Emerald Literati Network, UK and Informing Science, US, and a Fellow of Word Business Institute (WBI), Australia.

Nnachi K. Ikwor

Nnachi K. Ikwor is a doctoral research student of the Department of Marketing, University of Port Harcourt, Nigeria. He is a seasoned writer, a petroleum and community relations expert, a technical advisor, and a management consultant in the oil and gas sector.

Doris G. Ademe

Doris G. Ademe is a senior academic in the Department of Marketing, University of Port Harcourt, Nigeria. She has published extensively in learned journals, and participated in colloquia.