How the response to service incidents change customer-firm relationships

Coelho, Pedro Simões; Rita, Paulo; Ramos, Ricardo F. Article How the response to service incidents change customerfirm relationships European Journal of Management and Business Economics (EJM&BE) Provided in Cooperation with: European Academy of Management and Business Economics (AEDEM), Vigo (Pontevedra) Suggested Citation: Coelho, Pedro Simões; Rita, Paulo; Ramos, Ricardo F. (2023) : How the response to service incidents change customer-firm relationships, European Journal of Management and Business Economics (EJM&BE), ISSN 2444-8451, Emerald, Leeds, Vol. 32, Iss. 2, pp. 168-184, https://doi.org/10.1108/EJMBE-05-2021-0157 This Version is available at: https://hdl.handle.net/10419/325531 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ How the response to service incidents change customer– firm relationships Pedro Sim~ oes Coelho and Paulo Rita NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Lisboa, Portugal, and Ricardo F. Ramos Instituto Polit ecnico de Coimbra, ESTGOH, Oliveira do Hospital, Portugal; Instituto Universit ario de Lisboa (ISCTE-IUL), ISTAR-IUL, Lisboa, Portugal and CICEE –Centro de Investigaç~ ao em Ci^ encias Econ omicas e Empresariais, Universidade Aut onoma de Lisboa, Lisboa, Portugal Abstract Purpose –This paper analyzes previously unmeasured effects of a response to a service incident called “benevolent”within the customer –firm relationship. Design/methodology/approach –A questionnaire was administered to telecommunication customers in a Western European country, and the model was estimated using partial least squares (PLS). Findings –This study shows that the customer–firm relationship is surprisingly affected by the response to expected incidents that the customer interprets as acts of benevolence or opportunism. This research also shows that the firm’s incident response interpreted as benevolence or opportunism has an effect that merely positive or negative events do not. Acts of benevolence response towards an incident positively affect customer–firm relationship quality, and expectations of such acts may lead to an upward spiral in customer commitment. Originality/value –While benevolence trust has been proposed and studied before, the response to incidents interpreted as benevolent or opportunistic and their consequences have been under-studied, hence exhibiting a research gap. Keywords Customer relationships, Service incidents, Expectancy and disconfirmation, Benevolence, Opportunism Paper type Research paper Introduction In product exchange, the stakeholders start to interact in relationships. The energy and closeness of a relationship between two parts are referred to as relationship quality (Tajvidi et al., 2021).In a business context, relationship quality is related to the level of trust, satisfaction and commitment between a firm and a customer (Xie et al., 2017), leading to successful relational exchanges. Relationship marketing research has found that a positive relationship between the firm and a customer will reduce uncertainty, increasing exchanges between parts guided by relational norms (Gummesson, 2017;Steinhoff et al.,2019). High-quality relationships between parts have become deeply relevant for firms in achieving success. Recently, relationship quality has been under the interest of researchers. For instance, Tajvidi et al. (2021) aimed to understand the factors that affect consumers’intention to engage EJMBE 32,2 168 © Pedro Sim~ oes Coelho, Paulo Rita and Ricardo F. Ramos. Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2444-8494.htm Received 28 May 2021 Revised 18 November 2021 3 February 2022 Accepted 13 March 2022 European Journal of Management and Business Economics Vol. 32 No. 2, 2023 pp. 168-184 Emerald Publishing Limited e-ISSN: 2444-8494 p-ISSN: 2444-8451 DOI 10.1108/EJMBE-05-2021-0157 in co-creation activities, with results revealing that interactivity between stakeholders positively affects social support. Chi et al. (2020) investigated the relationship between customers and the firm from a social perspective, concluding that customer perceptions contribute to evaluating service quality. Boonlertvanich (2019) studied the causal relationships between service quality, customer satisfaction, trust and loyalty, finding that service quality affects attitudinal behavioral loyalty. Although there is a growing body of literature exploring the relationship quality between firms and customers, no study aimed to understand the impact of the customer perception of an incident and the firm’s response towards such incident being interpreted as benevolent or opportunistic on the consequences to the relationship quality between these parts. Given the recent importance given to academia to this subject and the importance of understanding how an incident impacts the customer–firm relationship, this study aimed to uncover the impact of a service incident and the firm’s benevolence response and surprise on the relationship quality between the parties. An incident can be defined as an unexpected event that provokes the degradation of the quality of service (Ho et al., 2020). The firm’s act of benevolence facing an incident is interpreted by a customer as an indication of caring and willingness to sacrifice its outcomes (Fazal-e-Hasan et al., 2020). The obverse of benevolence is opportunism, which is also addressed in this study. Data were collected from 224 individuals who lived at least a service incident during the previous year to achieve this purpose. Using partial least squares (PLS) for data analysis, we enriched the relationship quality between firms and customers. Theoretical background Relationship quality theory Relationship quality aims to capture and keep customers through the close connection between service relationships to organizational outcomes (Macintosh, 2007), based on the premise that the customers’evaluation of the service performance has a significant impact on the customers’satisfaction and retention. The relationship quality theory consists of three constructs (Tajvidi et al., 2021): trust, satisfaction and commitment. These are the attributions made by customers regarding events that occur in a firm–customer relationship. An extensive literature has been attempting to explain customer satisfaction, trust and commitment. Customer satisfaction has been studied as a central variable in marketing outcomes (Makanyeza et al., 2016;Xu and Li, 2016) in recent years (El-Adly, 2019;Lucini et al., 2020;Rita et al., 2019) and is defined as the emotional evaluation of the firms’performance (El-Adly, 2019). Trust and commitment have been two central constructs of interest since marketing began to consider relationships central to profitability (Dawson et al., 2017; Goutam and Gopalakrishna, 2018). Trust refers to the compliance to rely on a business partner in a relationship based on confidence (Iglesias et al., 2020), while commitment is the wish to maintain a relationship (Keiningham et al., 2017). One can say that supplier performance (S aenz et al.,2018), mutual dependence, attributions, expectancies, disconfirmations, perceptions of equity (Oliver and DeSarbo, 1988), perceived value (Agustin and Singh, 2005), and other constructs affect satisfaction, trust and commitment. Expectation and disconfirmation Superior performance can lead to high levels of customer satisfaction, influencing the relationship between the customer and the firm (Martinaityte et al., 2019). Performance is constituted of positive, negative and neutral events. Positive events contribute to incremental relationship development, whereas adverse events can dramatically impact a relationship. Moreover, how the firm deals with the client regarding those events will also determine the Response to service incidents 169 relationship quality. Such relationship events disconfirm relational expectations (positive or negative) (Harmeling et al., 2015). Over time, a series of merely satisfactory transactions with a product can increase satisfaction and commitment (Harmeling et al., 2015). However, expectations and their disconfirmations play a prominent role in satisfaction judgments (Bravo et al., 2017). Expectations are positively related to satisfaction. Positive disconfirmations (pleasant surprises) raise satisfaction, whereas negative disconfirmations lower it. Unexpected incidents cause an “updating”in satisfaction, while incidents within expectations cause little change (Mattila, 2003). The key to satisfaction change is the “disconfirmation of expectations”experience. However, expectations are in constant flux, adjusted by events like experiences with the product or supplier, marketing communications and changing awareness of alternatives (Payne et al., 2017). Positive and negative disconfirmations affect satisfaction and make adjustments in expectations, which in turn affect repurchase intentions or commitment in a service context (Yi and La, 2004). A pleasant surprise makes a customer more satisfied and raises expectations about the supplier, leading to repurchase intentions. An unpleasant surprise has the opposite effect. Cai and Chi (2021) discussed the implications of these effects, which may be that raising expectations only sets up the firm for later negative disconfirmation experiences by its customer base. Finally, as Howard and Barry (1990) evidenced, an unexpected and favorable event (winning a prize, in their experiment) tends to shift evaluations of a product from being based on the attributes of that product to the effect generated as a result of the pleasant surprise. This suggests that a firm might positively evaluate consumers with astutely chosen positive surprises. Managing customers’expectations of future benefits is the basis of positive customer emotion and is crucial to their satisfaction and commitment (Hsieh and Yuan, 2021). Benevolence and opportunism Benevolence in response to an incident is an action that firms can use to help strengthen customer relationship quality. The act of benevolence implies genuine caring and kindness towards a customer, resulting in feelings of respect, indebtedness, respect and liking (Hiller et al., 2019). Ganesan and Hess (1997) distinguished credibility trust and benevolence trust, where the former is an assessment of how a firm will deliver on its promises in the future, and the latter is an assessment of the degree it cares about the customer and is willing to make sacrifices for the customer. Opportunism is the obverse of benevolence and is the assessment by the customer that the firm does not care about the customer and will take advantage of the relationship (thus making the customer sacrifice for the benefit of the firm) if the opportunity presents itself. Opportunism is defined as “self-interest seeking with guile” (Paswan et al., 2017). The customer observes the act of a firms’opportunism as deceitful behavior. Generally, benevolence is the firms’initiative to advocate for customers’well-being to avoid disadvantageous circumstances (Nguyen, 2016). It informs customers that the firm has pro-customer actions facing an incident. Although benevolence towards an incident may lead to a financial loss, it leads to customer trust and increases relationship quality (Aljarah, 2020). Moreover, benevolence has an impact on increasing customer trust and commitment. Trust is considered crucial for decision-making in risky situations since customers’reluctance is determined by a lack of trust in the firm (Chaouali et al., 2016;Svare et al., 2020). Customer commitment generates satisfaction, increases relationship quality and results in the customers’preference for the brand, vetoing competitors (B eal and Sabadie, 2018). Customer commitment can be most effectively built through acts that the customer perceives as coming from a benevolent firm. EJMBE 32,2 170 Positive, negative, benevolent and opportunistic response to an incident Positive and negative incidents have been studied in the context of service failures; the most significant effect of adverse incidents over positive ones is well-known (e.g. Allen et al., 2019; Tontini et al., 2019). Incidents have an irregular impact on the relationship strength, whether positive or negative (Allen et al., 2020). From the customers’point of view, a positive incident benefits the customer. However, a negative incident reveals as a cost to the customer. Although it would be expected that a negative incident would lower the relationship strength, literature has speculated that customers understand and forgive when a first negative experience occurs (Christodoulides et al., 2021;Tsarenko and Rooslani Tojib, 2011). In addition, very little has been done to understand the impact of a firms’response considered benevolent or opportunistic to an incident caused to the relationship quality between the parties. Nevertheless, the act of benevolence usually results in forgiveness (Sajtos and Chong, 2018) and an opportunistic act turns into revenge and avoidance (Gr egoire et al., 2009). Conceptual model and hypotheses Our model is based on specific service incidents. We consider the effect of an incident on the change in the relationship quality (trust, satisfaction and commitment) between the customer and the firm (Tajvidi et al., 2021). We hypothesize that the positivity or negativity of an incident and the benevolence or opportunism of the firm’s response to that incident affect relationship quality. However, the extent of the surprise, or expectancy-disconfirmation of the incident, may produce a curious effect (Harmeling et al., 2015). In other words, under conditions of great surprise, the positivity/negativity of the incident may have an enhanced effect on relationship quality. However, the effect on relationship quality of the benevolence/ opportunism attributed to the firm’s response to the incident may not be significantly enhanced. The situation is exactly reversed when the surprise is low: the positivity/negativity of the incident has a more negligible effect on relationship quality, but benevolence/ opportunism response might have a more enhanced effect on relationship quality. Constructs and definitions Relationship quality. Grounded on relationship quality theory, we refer to customer-perceived changes in the three central relationship quality variables (satisfaction, trust and commitment) as a second-order construct called “relationship quality.”While these constructs have different roles in understanding a relationship (El-Adly, 2019;Iglesias et al., 2020;Keiningham et al., 2017), we consider them as common indicators of a higher-level construct that indicates the relationship quality between the customer and the firm. This is a broad-brush approach to relationship quality that later research may articulate into different effects. Positivity. This is defined as the customer’s perception of the extent of the positivity or negativity of an incident. Benevolence. Based on the incident, this is the extent to which the customer attributes the firm’s response as benevolent or opportunistic. Surprise. This is the extent to which the customer’s expectations were confirmed or disconfirmed. Research hypotheses From the customers’point of view, the quality of the relationship between customer and firm is very much a forward-looking construct mainly based on the past behavior of the firm and the attributions made to it. When consumers consider whether they will continue to do business with the firm, they project its behavior into the future. Has the firm behaved Response to service incidents 171 satisfactorily in the past? Can the customer infer that it is trustworthy? Would it take advantage of the customer? Shall the customer continue to do business with it or survey the market for another supplier? Furthermore, answers to these questions are dynamic, changing over time as incidents accumulate. Positive and negative incidents have an irregular impact on relationship quality. Positive incidents reveal a benefit for the customer, leading to a positive affective reaction (RamseookMunhurrun, 2016;Zhu et al., 2019). In turn, a firms’benevolence response towards an incident indicates that the firm is concerned with the well-being of its customers (Nguyen, 2016). This approach also leads to a positive affective reaction from the customer. Therefore, we hypothesize that: H1. The positivity of an incident positively influences the relationship quality. H2. The benevolence response towards an incident influences the relationship quality. Nonetheless, there is a moderating effect of surprise or expectancy-disconfirmation of the incident. Usually, a positive incident is good for the relationship, while a negative incident is not. However, if it does not surprise the customer, it is just standard good or bad service. A positive incident that surprises the customer indicates that more excellent performance may be expected in the future (Lenz et al., 2017), thus enhancing the relationship quality. A surprising failure is an indication that poorer service may be expected (Endrikat, 2016), thus lowering the relationship quality. Notwithstanding, when the customer faces a surprising positive incident, a twofold enhancement of the relationship quality occurs (Sajtos and Chong, 2018) since the positive incident alone would have an enhanced effect on the relationship quality, increased by the surprising effect. In turn, facing a surprising negative incident, the customer would feel doubly frustrated, negatively influencing the relationship quality. Thus, we hypothesize: H3a. Higher levels of surprise facing an incident will positively influence the relationship quality. The act of benevolence regarding a firms’incident will increase the relationship quality (Nguyen, 2016). However, when the firm promotes the element of surprise, it would be expected that the relationship quality would have a double effect, increasing the relationship quality. In turn, if the firms’act facing the incident is viewed as opportunistic and surprising, the opposite double effect will occur (Paswan et al., 2017). However, if the opportunistic act is not a surprise, we can expect resignation toward the relationship and hatred toward the firm. In addition, one can expect the consumer to minimize contact with the firm, but each additional incident will confirm the customer’s powerlessness afresh (Bunker and Ball, 2009), leading to further declines in relationship quality. So, we hypothesize: H3b. Higher levels of surprise facing a benevolent response towards an incident will influence the relationship quality. Figure 1 presents the conceptual model and the associated research hypotheses that will be tested. Research methodology Data collection The quantitative research phase of this study was conducted by administering a questionnaire, through computer-assisted telephone interviewing, to telecommunication customers (mobile phone and cable TV customers) in a Western European country. EJMBE 32,2 172 Respondents were randomly selected through random digit dialing using the ranges of all available mobile and landline telephone numbers. After being identified as a customer of one of the telecommunication companies, the interviewees were asked about at least one service incident during the previous year. Only customers with specific and identifiable service incidents were selected to be interviewed. After identifying service incidents, the questionnaire queried the respondents’perception regarding the characteristics of the incident, including positivity, level of benevolenceopportunism in the response towards such incident, level of surprise (expect-non-expected) and attributions made by the respondent. The questionnaire also queried clients’perceptions of the incident’s relationship quality. The sample was stratified by industry (mobile and cable TV) and by type of service incident and response (positive, negative, benevolent and opportunistic) to guarantee an adequate sample size for each group. Along with the type of incident, a detailed description was collected. After data collection, the detailed description of all incidents was facially validated by independent marketing academics and telecom professionals. All records whose incidents were not found adequately described or did not correspond to the type of incident selected by the customer were discarded. This resulted in discarding 30 records. The sample size was 224 individuals (119 mobile telecommunication customers and 105 cable TV customers). All groups corresponding to the types of incidents and responses were also well represented (57 observations with positive non-benevolent incidents, 54 with negative nonopportunistic incidents, 70 with benevolent incidents and 43 with opportunistic incidents). The socio-demographic characteristics of the respondents are presented in Table 1. The socio-demographic profile is consistent with the known structure of the population and was validated by industry managers as consistent with industry data. Measures The questionnaire construction design followed the methodological approach of Malhotra (2019). First, exploratory research, through a focus group, was applied to university students who were customers of the selected industry and experienced at least one incident. Through a semi-structured approach, we aimed to understand how people experienced service incidents, how they distinguished them, the most relevant characteristics of each incident, their interpretation of the firm’s response towards the incident, and if such incidents and responses affected their relationship with the supplier. According to the results obtained in the first stage, the incidents were classified into four groups on a second stage. This stage led to a preliminary version of the scales. A qualitative pre-test was conducted through a pilot survey Trust Sasfacon Commitment Relationship quality change Positivity Benevolence Surprise H1 H2 H3a H3b Figure 1. Conceptual model Response to service incidents 173 directed to 28 customers of both industries on a third stage. Minor refinements were introduced in the phrasing and order of the questions and the filtering of the questionnaire. In the fourth stage, the final version of the questionnaire was presented to academia and industry independent experts (expert judgment), who validated it. Other forms of validity (convergent, discriminant and nomological) were also accessed. All constructs in the proposed model (positivity of the incident, level of benevolenceopportunism in the response, surprise and customer–firm relationship change) were based on reflective multi-item scales. All indicators were measured with a ten-point rating scale, with one representing the lowest and ten the highest. Table A1 of Appendix presents a detailed list of indicators used in the measurement model. Estimation The structural model consists of three latent variables (Figure 1). The model was estimated using the complete dataset (n5224) of telecommunication customers and four subgroups. The first two subgroups represented a split of the data set by industry: mobile telecommunications and cable TV. The other two subgroups were obtained by dividing the original data set into customers with a high (above average) level of surprise (unexpected incidents) and a low (below average) level of surprise (expected incidents). These two segmentations were used for subgroup analysis (Arnold, 1982;Kohli, 1989;Sharma et al., 1981) to assess the moderating effects of industry and level of surprise on the model structure. The contrast between high and low surprise groups is evident in Table 2, which shows each group’s mean and standard deviation of surprise indicators. (%) Gender Male 51.8 Female 48.2 Age of respondent <30 years 23.7 30–39 years 21.4 40–49 years 18.8 ≥50 years 34.4 Missing 1.8 100.0 Education level of respondent Basic education or less 13.4 High school education 46.4 University education 37.5 Missing 2.7 100.0 Occupation of respondent Employer 2.7 Self-employed 8.9 Worker on behalf of others 55.8 Unemployed 4.0 Housewife 3.6 Retired 10.3 Student 12.1 Missing 2.6 100.0 Table 1. Sample characteristics EJMBE 32,2 174 The model was estimated using PLS. This option is mainly motivated by the nature of the data (Hair et al., 2017). We measure categorical variables with an unknown non-normal frequency distribution, which is usually negatively skewed. In this context, PLS can be a preferable alternative to the use of maximum likelihood methods, comparisons between maximum likelihood methods, and PLS can be found in the studies by Fornell and Bookstein (1982),Dijkstra (1983),Chin (1998) and Vilares et al. (2010). All data analyses were done using SmartPLS and SAS system. Table 2 presents both means and standard deviations in high and low surprise groups. Analysis Descriptive analysis Means and standard deviations of original variables can be found in Table 3. The dataset means varied between 5.83 for x 11 (how positive–negative the customer considers the event) and 6.79 for x 31 (how much the event surprised the respondent). The highest means were found in surprise indicators and the lowest in positivity construct. Standard deviations varied between 2.47 for x 31 (how much the event surprised the respondent) and 3.24 for x 12 (how pleasant-unpleasant the customer considers the event). Surprise indicators were the ones that globally showed the lowest variability. Although the means for most of the measures were located just slightly to the right of the center of the scale, suggesting a slightly negatively skewed distribution, the standard deviations suggest a large variability in all indicators associated with a non-normal distribution. Reliability and validity We started by examining the model constructs’reliability and convergent validity measures (Table 4). All Cronbach’salphas(Cronbach, 1951) exceeded the 0.7 thresholds (Nunnally, 1978) and were consistently higher than 0.93. Without exception, latent variable composite reliabilities (Werts et al., 1974) were higher than 0.96, showing a high internal consistency of Construct Indicators High group Low group Mean Std. dev Mean Std. dev Surprise y 31 8.27 1.54 5.09 2.25 y 32 8.26 1.43 4.65 2.17 Construct Indicators Telecom Mean Std. deviation Loading Positivity x 11 5.83 2.81 0.970*** x 12 5.86 3.24 0.970*** Benevolence x 21 5.85 2.81 0.970*** x 22 6.21 2.72 0.972*** Surprise x 31 6.79 2.47 – x 32 6.58 2.56 – Change in relationship y 11 5.97 2.89 0.968*** y 12 6.00 2.96 0.979*** y 13 6.20 2.83 0.956*** Note(s): ***Significant at < 0.001 level Table 2. Means and standard deviations in high and low surprise groups Table 3. Means, standard deviations and standardized loadings of manifest variables Response to service incidents 175 Hiller, N.J., Sin, H.-P., Ponnapalli, A.R. and Ozgen, S. 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Appendix Measurement model Corresponding author Paulo Rita can be contacted at: [email protected]nl.pt For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Construct Measure Positivity x 11 Using a scale from 1 to 10, where 1 means “very negative”and 10 means “very positive”, how do you classify the event that you mentioned? x 12 Using a scale from 1 to 10, where 1 means “very unpleasant”and 10 means “very pleasant”, how do you evaluate the event that you mentioned? Benevolence x 21 In what way do you think that this event shows that the service provider cares or does not care about you as a customer? Using a scale from 1 to 10, where 1 means “service provider does not care about you as a customer”and 10 means “service provider has a genuine concern with you” x 22 In what way do you think that this event shows that the service provider cares or does not care about keeping you as a customer? Using a scale from 1 to 10, where 1 means that the “service provider just cares the profits and does not care about keeping me as a customer”and 10 means “service provider cares in keeping me as a customer and does not just care about the profits” Surprise x 31 Using a scale from 1 to 10, where 1 means “nothing surprised”and 10 means “very surprised”, how did this event surprise you? x 32 Using a scale from 1 to 10, where 1 means “very expected”and 10 means “very unexpected”, how expected (unexpected) do you consider the behavior that caused this event? Relationship quality change y 11 Using a scale from 1 to 10, where 1 means “a very negative way”and 10 means “a very positive way”, how do you classify the way this incident affected your trust in the service provider? y 12 Using a scale from 1 to 10, where 1 means “a very negative way”and 10 means “a very positive way”, how do you classify the way this incident affected your satisfaction with the service provider? y 13 Using a scale from 1 to 10, where 1 means “a very negative way”and 10 means “a very positive way”, how do you classify the way this incident affected your loyalty to service provider as a customer? Table A1. Indicators of the measurement model EJMBE 32,2 184