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2013/1 (Vol. 16)

  • Pages : 118
  • DOI : 10.3917/mana.161.0001
  • Éditeur : AIMS


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The literature has long acknowledged the importance of customer participation (i.e., the fact that customers take part in service operations), as customers have been recognized as factors that may greatly influence the structure and functioning of formal organizations (Barnard, 1948; Chase, 1978, 1981; Lefton & Rosengren, 1966; Lovelock & Young, 1979; Parsons, 1956). In particular, customers can participate in and influence service delivery methods (Bolton & Saxena-Iyer, 2009; Shahin & Nikneshan, 2008), new product development (Fang, 2008; Fang, Palmatier, & Evans, 2008), value creation processes (Payne, Storbacka, & Frow, 2008), service employees’ performance (Chan, Yim, & Lam, 2010)… However, even though practitioners and researchers alike recognize the increasing role of customers (Chesbrough, 2006; Plé, Lecocq, & Angot, 2010), questions about the place and role of customers in service firms remain unresolved (Bowen & Hallowell, 2002; Danet, 1981; Spohrer & Maglio, 2010). In particular, their influence on firms’ intra-organizational coordination has received little attention and remains unclear (Okhuysen & Bechky, 2009). Schematically, two main streams of research study coordination (Gittell, 2002a; Shah, Goldstein, Unger, & Henry, 2008). The first focuses on the firm’s organizational design and views coordination as the manner to integrate or link an organization’s interdependent parts or activities (Lawrence & Lorsch, 1967; Nadler & Tushman, 1997; Van de Ven, Delbecq, & Koenig, 1976). Some scholars thus consider the impact of customers on coordination, through the uncertainty they introduce into the firm’s organizational design. For example, Argote (1982) shows that the greater the uncertainty resulting from the customer’s presence in the service process, the more organizational performance relies on the use of non-programmed coordination mechanisms (i.e., employees rely on what Mintzberg (1979) calls “mutual adjustment”). Rathnam, Mahajan & Whinston (1995) show that coordination gaps (breakdowns in work and information flows) stem partly from customer-generated uncertainty. Bowen & Jones (1986) suggest that customer-induced uncertainty can trigger the use of specific governance mechanisms, chosen according to their transaction cost efficiency. Larsson & Bowen (1989) also develop a conceptual framework with four uncertainty scenarios, each corresponding to a particular type of interdependency within the organization.


The second stream of research is more recent (Okhuysen & Bechky, 2009). It regards coordination as a social process that takes place between participants who interact within a mutually reinforcing web of communication and relationships to integrate interdependent tasks; scholars call this interaction “relational coordination” (Gittell, 2000, 2001, 2002a, 2002b). Although the literature on relational coordination focuses on employees who work together to deliver a service, research to date has not considered customers as participants in this social process, despite their great potential influence on service employees’ daily jobs and relationships with their colleagues (Rafaeli, 1989). This paper aims to contribute to this second stream of research. I posit that customers should be integrated as participants in relational coordination, such that they are likely to influence the relational coordination among frontline service employees through the way that these frontline employees perceive the participation of customers with whom they interact.


By combining intra-organizational coordination and services marketing literature, this article seeks to address this gap, recently noted by Gittell (2011), and explore the way customers may influence relational coordination among service employees. Relying on the concepts of relational coordination on the one hand and customer participation (CP) in service processes on the other, I first suggest how CP might theoretically influence relational coordination among frontline employees. I then present two case studies, carried out in the context of multichannel retail banking. The data analysis results in two key contributions. First, customers appear to influence relational coordination among service employees through the employees’ perception of CP. That is, the perception that frontline service employees develop of (1) the antecedents of customer participation (i.e., why customers participate or not) and (2) customers’ inputs in a service process (i.e., what customers bring to this process) that may influence relational coordination among these employees. Second, the nature and history of the customer–employee interaction have unexpectedly emerged as potential moderators of the scope of this influence. In addition, this study suggests the possibility of a new dimension of relational coordination, namely, “mutual leniency.” It offers a potentially useful complement to other dimensions of relational coordination. These results are presented as five propositions. Finally, limitations and directions for further research are discussed.



The concept of relational coordination can be defined as “a mutually reinforcing process of interaction between communication and relationships carried out for the purpose of task integration” (Gittell, 2002b, p. 301). Relational coordination is necessary to achieve high levels of performance under conditions of reciprocal task interdependence, high levels of uncertainty, or rigorous time constraints (Anderson, 2006; Gittell, 2001; Gittell, Weinberg, Pfefferle, & Bishop, 2008; Gittell & Weiss, 2004). It focuses on the interactions between the roles endorsed by participants, carried out through communication and a “web of relationships” (Gittell, 2002a, 2002b) among these participants. Thus, relational coordination focuses on the relationships between roles, rather than on personal ties between and among the participants (Carmeli & Gittell, 2009; Gittell, 2008; Gittell, Seidner, & Wimbush, 2010).


The two dimensions of relational coordination (communication and relationships) reciprocally influence each other, and can be broken down further into different sub-dimensions (Table 1). The quality of relational coordination then depends on frequent, timely (that is, information must arrive on time for those who need it), accurate, and problem-solving communication among participants. It also depends on the strength of the relationships, which is assessed by the existence and intensity of shared knowledge, shared goals, and mutual respect among the participants involved in the relational coordination process. Such a web of relationships reciprocally influences the quality of communication, enabling improved and effective coordination among the participants involved. Relational coordination thus indicates that high-quality communication both supports and is supported by the relationships, to enhance coordination of highly interdependent work between participants. Therefore, a high level of shared knowledge with regard to each other’s tasks reinforces the bonds between participants and enhances communication, which arguably increases accuracy and/or timeliness. Shared goals, as well as mutual respect for each other’s competence, also reinforce these bonds.

Table 1 - Dimensions of relational coordination (based on Gittell, 2001, 2002a, 2002b)

Other than defining the concept of relational coordination and determining its dimensions and sub-dimensions, prior research has mainly investigated two topics: the antecedents of relational coordination, and its impact on the performance outcomes of service processes. Both of these types of research, however, share the same primary focus on the inside of the organization, as they do not include customers as active participants in relational coordination. This gap is surprising, considering the crucial role that customers play in service processes (Bitner, Faranda, Hubbert, & Zeithaml, 1997; Dong, Evans, & Zou, 2008; Lovelock & Young, 1979)—a role that both scholars and practitioners have acknowledged, and that has become increasingly important in recent years (Chesbrough, 2006; Plé et al., 2010).


The antecedents that enable service employees to reach (or prevent them from reaching) high levels of relational coordination include coordination mechanisms, control mechanisms, human resource practices, formal work practices, and environmental pressures and practices (Ahuja, 2003; Gittell, 2000, 2008). All of them except environmental pressures and practices relate to internal practices and mechanisms, and thus exclude customers. The latter indirectly appear as one of the sources of environmental pressures, but only through the uncertainty that results from the level or nature of their demand (Gittell, 2002a, 2008). In sum, customers’ actions in service processes are not viewed as potential influences on relational coordination between service employees.


A second type of studies examines how relational coordination affects the performance of service processes, by measuring this performance against either internally or externally focused indicators. The internally focused perspective investigates the influence of relational coordination on (1) service processes’ efficiency (Gittell et al., 2010; Gittell & Weiss, 2004), (2) how the performance of supply chains can be improved (Shah et al., 2008), (3) service employees’ job satisfaction (Gittell et al., 2008), or (4) the quality of manager—employees relationships (Anderson, 2006). Some organizations also rely on relational coordination to help their employees learn from failed processes and thus improve their performance (Carmeli & Gittell, 2009). Other scholars describe how relational coordination can help encourage a strong organizational identity (Prati, McMillan-Capehart, & Karriker, 2009) to enhance the employees’ work behaviors. The externally focused perspective examines the consequences of relational coordination on customers’ performance outcomes. Anderson (2006) suggests that the quality of relational coordination between service employees and their managers affects the nature of customers’ outcomes. Empirical research in healthcare settings demonstrates that a higher level of relational coordination among employees results in a better quality of life for residents (Gittell et al., 2008) and improved patient-perceived quality of care (Gittell et al., 2010). In addition, customers are more satisfied, and recommend a service provider more readily, when relational coordination between employees is high (Gittell, 2002b).


In short, no literature on either topic features customers as actual active participants in the coordination process. When considered, customers have been viewed either as a source of uncertainty to be dealt with, or as beneficiaries of services that rely on high-level relational coordination. Yet Gittell (2011, p. 406) herself points to the need for research on the extension of relational coordination theory, “to include a broader network of participants,” including the customers who interact with frontline service employees. Relying on services marketing literature, this article seeks to address this gap by arguing that customers should be taken into consideration as participants in the coordination process who, accordingly, are likely to influence relational coordination among frontline service employees.



In order to investigate the effects of the customers’ actions in a service process on relational coordination among service employees, I propose to draw on the concept of customer participation (CP), which is widely discussed in the services marketing literature (for a summary, see Plé et al., 2010). CP can be defined as “a behavioral concept that refers to the actions and resources supplied by customers for service production and/or delivery. CP includes customers’ mental, physical and emotional inputs” (Rodie & Kleine, 2000, p. 111). This definition highlights that customers bring varied inputs to participate in different contexts and processes (service production and delivery). However, the mere fact that customers participate in a service process may seem counterintuitive, because they are supposed to be the beneficiaries of this process, since they pay to receive its outcome. Therefore, to understand CP, it is first necessary to investigate why customers participate, that is, the antecedents or “facilitating factors” of CP (Auh, Bell, McLeod, & Shih, 2007, p. 360). The remainder of this section presents the antecedents of CP, and then its inputs, as listed and defined in Table 2.

Antecedents of CP


The antecedents of CP consist of two categories: customer and firm. Five customer antecedents seem key to understanding why customers participate or not: role size, role awareness, role clarity, customer ability, and customer willingness. In addition, firms can trigger or hinder participative behavior by organizationally socializing customers. Studies that consider the nature of these antecedents focus on the manner in which they can increase the levels of CP, but they do not take into account the potential impact of these antecedents on the service process itself or on the coordination that supports the process In addition, as Wu (2011) notes, most of this research is theoretical (Bowen, 1986; Etgar, 2008; Kelley, Donnelly, & Skinner, 1990; Rodie & Kleine, 2000); empirical studies are scarce.


Among the latter, a majority focuses on customers’ ability, willingness (i.e., customer antecedents), and on the customers’ organizational socialization (i.e., firm antecedent), as it shows that customers’ actual and perceived ability to participate (Auh et al., 2007; McKee, Simmers, & Licata, 2006) and willingness (Meuter, Bitner, Ostrom, & Brown, 2005) favor participative behaviors. Furthermore, customer organizational socialization significantly and positively influences the level of CP in two ways: by increasing customers’ understanding of the firm’s norms, values, and expectations, and by providing customers with the knowledge and skills they need to participate (Claycomb, Lengnick Hall, & Inks, 2001; Wu, 2011). Such objectives can be achieved through organizational communication literature (e.g., brochures, websites, Frequently Asked Questions) or by emphasizing the advantages of CP through discounts, faster delivery, etc. (Bateson, 1985). Employees can also encourage customers to participate, or help them understand the content of the participation and how to participate (Bove, Pervan, Beatty, & Shiu, 2009). Eventually, firms can also implement customers’ online communities that provide information on how to participate , as this increases the level and efficiency of customers’ participative behaviors (Algesheimer, Borle, Dholakia, & Singh, 2010).

Inputs CP


Because CP antecedents trigger customers’ participative behaviors in service processes, they influence both the nature and quantity of inputs that customers bring to these processes. Researchers identify seven kinds of inputs: informational [1][1] These informational inputs are also called “mental inputs”... , emotional, physical, financial, temporal, behavioral, and relational. These inputs are nonexclusive, and not all of them are necessarily mobilized by a single participating customer. They have been identified in prior literature, yet research on these inputs suffers from three gaps: (1) it is mainly conceptual, (2) it essentially focuses on CP at large, regardless of the nature of the inputs, and (3) it often puts the emphasis on some specific inputs and gives little attention to others.


First, similar to a majority of research on CP (Bendapudi & Leone, 2003), studies on CP inputs are primarily conceptual. Inputs have been theoretically defined and discussed (e.g. Bitner et al., 1997; Grönroos, 2001; Kelley et al., 1990; Song & Adams, 1993) but they have rarely been studied empirically. Second gap in research listed above, conceptual and empirical studies are largely restricted in that they examine the extent of customer participation, rather than the inputs of CP themselves. Research shows that the intensity of CP can influence the overall performance of service processes, whether through service delivery methods (Bolton & Saxena-Iyer, 2009; Shahin & Nikneshan, 2008), new product development (Füller & Matzler, 2007), value creation processes (Chan et al., 2010), or service employees’ performance (Chan et al., 2010; Yi, Nataraajan, & Gong, 2011). In other words, these studies examine the influence of the level of CP rather than the influence of the nature of CP, and they aggregate the inputs to measure their combined influence, without distinguishing different inputs. Moreover, some inputs have been better investigated than others. In particular, the influence of the information that customers provide to firms (i.e., informational inputs) has been studied extensively, empirically and theoretically, both in business-to-business and business-to-consumer contexts (Fang, 2008; Fang et al., 2008; Siehl, Bowen, & Pearson, 1992).


Thus, the antecedents and inputs of CP alike relate to what customers do in service processes. For that reason, it is suggested here that antecedents and inputs likely play important roles for understanding how customers might influence relational coordination between service employees involved in this process. The next section presents the conceptual framework I propose to link both literature streams and thereby suggest the manner in which this influence may occur.

Table 2 - Antecedents and inputs of customer participation



The presence and participation of customers make them part of the set of participants involved in a service process, along with frontline employees (Bolton & Saxena-Iyer, 2009; Mills & Morris, 1986). Thus, it seems theoretically sound to integrate them as participants in relational coordination. In so doing, there are at least two possible ways to study the consequences of this integration. The first would delve into the coordination process between customers and service employees who need to coordinate to co-create the service (Bettencourt, 1997; Fang, 2008). The second would inquire how customers, through CP, can influence relational coordination among the different frontline service employees with whom they interact—an avenue Gittell (2011) recently mentioned as a potential new direction for relational coordination theory. Such research seems necessary, given the increasing importance that customers play in firms’ processes (Auh et al., 2007; Bettencourt, 1997; Bitner et al., 1997; Wu, 2011). This article adopts the second perspective and builds on previous works that provide insights into customers’ influence on the level of communication and the nature of the relationships among service employees. First, Rafaeli (1989) shows that five factors are likely to modify the way frontline employees coordinate with other employees of the company: customer– employee physical proximity, the amount of time customers and employees spend together, the amount of feedback customers give, the amount of information they provide, and the crucial role cashiers attribute to customers. Expressed in terms of CP, three of these factors refer to specific customer inputs: the amount of time spent together (temporal inputs), the amount of feedback and the amount of information (both informational inputs). In addition, Rafaeli notes customers’ attempts to discourage frontline employees from interacting with colleagues or managers. Even though service delivery supposedly necessitates this interaction, it may be perceived as a waste of time by the customer, who will therefore want to prevent it. In other words, the level of interaction employees have with one another and their manager depends on the willingness of customers to participate in the way the company expects of them. Customers also indirectly influence employees through other employees and managers, such as when they report an incident to a manager, (whether positive or negative) or when they comment on an employee’s behavior to colleagues or the manager, etc. Put another way, the ability and willingness of a customer (CP antecedents) to provide information (CP input) potentially affects both the level and quality of communication, and the relationships among the firm’s employees (relational coordination).


In an empirical study of service quality in a multichannel service system, Wiertz, de Ruyter, Keen & Struikens (2004) unexpectedly discover a positive relationship between customers’ quality image of a channel and the level of cooperation between channels – with “image quality” defined as a group of variables “that reflects the opinion of the customer” (p. 433), whereas “cooperation” refers to channel partners who voluntarily undertake “similar or complementary actions to achieve mutual or singular outcomes with expected reciprocation over time” (p. 428). In terms of relational coordination and CP, identifying a positive relationship between image quality and the level of cooperation means that when customers share their positive image of channel A with an employee who works in channel B (informational inputs), the latter values the work and competence of the channel A employee, which should increase and reinforce the mutual respect between these channels. Plé (2006) further demonstrates that customer feedback (informational inputs) either positively or negatively modifies the perceptions that some employees have of colleagues who work in other channels. Using relational coordination terminology, these results implicitly reveal an alteration of employees’ shared knowledge (i.e., knowledge that the employees of one channel have with regard to the nature and content of their colleagues’ work), a decrease in the channel employees’ mutual respect, and a decrease in shared goals perceptions.


Implicit in these developments is the assumption that the factor most likely to influence relational coordination is the way frontline employees perceive the antecedents and inputs of CP, in line with the crucial role of perceptions in service encounters (Cook et al., 2002; Czepiel, 1990). In addition, these studies do not explicitly focus on the customer’s influence on relational coordination, but merely suggest it. This article attempts to overcome this gap by providing an empirical study that explicitly explores the influence of CP on relational coordination, through the manner in which frontline service employees who must coordinate with one another perceive CP.


Research field


This research was performed in two French retail banks (names changed for confidentiality): FB1, a mutual regional bank, and FB2, a national bank. Like other banks in their sector, both were developing multiple channels, including diverse new channels such as sophisticated call centers (CCs), mobile services, Internet services, etc. However, because my purpose was to explore customers’ influence on relational coordination between frontline employees, I focused exclusively on branches and CCs in FB1 and FB2, which communicate and work with one another. I also excluded self-service channels that involve no customer–employee interaction.


Both banks had set up these channels for similar reasons. Their main objective was to outsource many operations that did not generate any direct profit for the bank (e.g., transfers or accounts consultation) to customers. In so doing, they hoped to increase the productivity and efficiency of branch advisors, who would no longer be disturbed by phone calls during their selling time. FB1 and FB2 also wanted to improve service quality; for example, 25 % of the phone calls to FB1 branches were unanswered, compared with only 5 % of phone calls to the CC. To ensure that customers would use the inbound CC, both banks listed the CC phone number, and changed the phone number of the branch. Moreover, both banks communicated these shifts heavily to customers and employees, to help them understand the channels’ complementarity. This move was indispensable, because many customers were unhappy about the change and feared that their branch would be closed and replaced by the CC. However, FB1 and FB2 insisted that its sole purpose was to help the branches and provide customers with better service in the course of their global, multichannel relationship with the bank. Branch advisors and CC advisors also used different means to share information (internal e-mail and telephone at FB1; fax and very occasional phone calls at FB2). Finally, both banks implemented outbound CCs, employing outsiders with no previous banking experience. They had to canvass customers to get appointments with branch advisors.

Data collection

Data triangulation


The relative dearth of academic literature on the research topic and the “how” nature of my research question together raise the need for qualitative data that can provide rich descriptions and explanations of processes grounded in the reality of a local context (Miles & Huberman, 1994). It is interesting to note here that most of earlier research on relational coordination has traditionally relied on quantitative methods. This study thus provides qualitative input into a quantitative stream of research. The collection of qualitative data relied on two case studies that lasted approximately 11 months. With a view to triangulation (Miles & Huberman, 1994; Yin, 2003), I used a variety of collection methods, sources of information, and types of data to enhance the reliability of the data (Table 3).

Table 3 - Data collection methods and sources

The interviews were the main source of data. Extant literature suggests that customers may influence relational coordination through service employees’ perception of CP, so I decided not to interview customers. Instead, I met branch advisors, CC advisors, and site managers (i.e., managers of a branch or CC), who interact daily with customers and whose jobs – which involve delivering banking services to customers – are interdependent. The sites were chosen in such a way that, in each of these, I could meet diversified respondents who shared many characteristics with respondents from other sites (e.g., age, seniority, structure of the clientele). In addition, I ensured that branches sampled from FB1 and FB2 were from the same cities or geographically very close. Thus, I favored heterogeneity among the respondents within each site and homogeneity in the respondents’ characteristics from one site to another. All the recorded interviews were structured in four parts: daily work, introduction of new channels, channel coordination, and consequences of customers’ perceptions of the development and use of new channels. A short synthesis (no longer than two pages) was then sent to each interviewee for validation. On-site interviewing had three important implications for the data collection. First, the interviews were time-constrained; I was granted approximately the same amount of time as a customer (1–1.5 hours). Usually, the interview ended when a customer arrived for an appointment, though many interviewees granted me additional time (even sometimes to the detriment of their customers) to ensure we had covered all the topics. Second, most of the interviewees either relied on events that occurred during the interview (e.g., they received an e-mail from the CC, or a customer call) or gave me some documents to illustrate their statements. Third, I was able to observe the interviewees and their colleagues in their working environment, which provided worthwhile additional information and gave me a better understanding of their perspective.

Reading over the interviewees’ shoulders


I chose data collection, and accordingly data analysis, methods that would establish precise local meanings of the observed phenomena. I adopted an interpretive approach to the field and data, and persistently tried to consider these phenomena from the perspective of the actors I met (i.e., branch advisors and CC advisors who are in direct contact with customers and perceive their participation) with their own subjective frame of reference in mind (Buchanan & Bryman, 2007; Wiliams, 2000). In other words, I consistently tried to “read over the shoulders” (Geertz, 1973) of these actors to gain a deep understanding of their work and their relationships with their colleagues and customers. To that end, data triangulation was useful, as was the interview report that I sent to each interviewee, to ensure that I adhered as closely as possible to their perspectives. However, I faced three major difficulties in the process, which I had to overcome to remain coherent with my interpretive approach. These difficulties arose because, as Fontana & Frey (2005) note, an interviewer may become “an advocate and partner in the study,” (p. 697) because interviews are “not neutral tools of data gathering, but rather active interactions between two or more people leading to negotiated, contextually-based results” (p. 698). To start with, it became evident after the first two interviews that I needed to make sure that my respondents and I shared the same meanings. For example, many did not understand what I meant by the word “channel.” Having worked as a seasonal branch advisor a few years before, I relied on this field knowledge during the interviews to attempt to “speak the same language” as my interviewees (a strategy also described by Clegg, 2009) and thus I could provide examples from their daily work to develop a shared meaning. Taking their role from time to time developed mutual empathy and improved our mutual understanding (Fontana and Frey 2005). Another difficulty I faced was that many interviewees wanted me to endorse two different roles during the interviews. They asked for my opinion as either an expert, or a customer, of the banking industry. To deal with this, I progressively adopted two strategies. When asked as an expert, I suggested that I would give my opinion at the end of the interview, and made a note of this to show that the question was important to me. I broached the subject at the end of the interview, after the recorder was turned off, which enabled me to obtain additional information that had not been provided during the interview—possibly because of the recorder. When appealed to as a customer, I explained that my own experience was certainly biased due to my knowledge of the industry, and that it was difficult for me to answer—a response the interviewees generally accepted. A third challenge was that most of the interviewees expected me to relay some messages to a third party. Because the research echoed with their daily issues, they hoped that I could deliver information that would help lead to the changes they wanted to see. For example, I often heard such phrases as, “you should tell them” [ “them” being the bank’s directors], or “you should write that in your report,” as leveraging attempts.


I progressively became aware of these issues after five or six on-site interviews, as I realized when I wrote up the interview synthesis that I was progressively falling into a three-sided role trap. The “customer in me” wanted to provide an opinion and use the study to make changes in the banking relationship. Because I was trying to develop empathy with the interviewees to obtain as much quality data as possible, I noticed that the “former branch advisor in me” became involved in the interviewees’ issues to such an extent that I risked turning my study into a political tool to make changes in their favor. Finally, the “banking sector expert in me” tempered these other roles to provide a balance that, I believe, enabled me to remain as close as possible to the interviewees’ frame of reference while making sense of the phenomena I was studying, though “complete asepsis is impossible” (Fontana & Frey, 2009, p. 720). I was very careful to bear this in mind during the rest of the data collection process and analysis.

Data analysis


As soon as I became aware of this role stress, I began my analysis of the first set of data. Following prior literature, I developed a thematic coding list that included both dimensions of relational coordination (communication and relationships) and their sub-dimensions, as well as the inputs and antecedents of CP. I progressively refined and enriched this list in the coding process to reflect the subjective perspective of the actors, in accordance with my interpretive approach of the field and the data.


First, a new sub-dimension of relational coordination related to relationships rapidly emerged. I called it “mutual leniency,” because it describes the capacity of frontline service employees to forgive their colleagues who work in other channels for making mistakes in their work. Mutual leniency differs from mutual respect, insofar as the latter indicates situations in which service employees value the work contribution of their colleagues from other channels. In contrast, mutual leniency refers explicitly to the tolerance and understanding that some employees show towards colleagues who work in other channels and make mistakes because of the customer’s participation in the process. For instance, some branch advisors noted that the lack of accurate information from CC advisors could be due to the fact this information was not provided by the customer. Because they encountered this kind of situation with their own customers, some branch advisors were more tolerant and ready to forgive their CC colleagues.


A second complementary important theme that emerged from the data is “customer–employee interaction.” This encompasses all the information related to the nature of the interactions between the customer and frontline employees, as well as how those interactions occurred over time. This theme progressively appeared to play a role both in the way that customers influenced relational coordination, and the scope of this influence.


The data were coded with Nvivo 7 software; all the documents, observational notes, and interviews had been transcribed. This was helpful to create relationships among the codes and exemplify them with quotes. Thus, I gradually built a helpful corpus of relationships that gave a better insight into the data and provided more complex, detailed explanations of the links between CP and relational coordination. I also relied heavily on three other Nvivo tools to analyze each case and compare them. In particular, I created many matrices using the “Queries” tool. For example, I determined the number of interviews in which a code appeared, or the occurrences of this code in all the interviews. I occasionally used the matrices for quantitative analysis of the data, for two reasons: firstly, to ensure the process was not distorted by my own perception, due to the aforementioned role stress, and secondly, to remain as close as possible to the interviewees’ perspective and subjective frame of reference. For example, the content analysis of one FB1 branch led me to believe that customers’ influence was higher (in terms of frequency) than in the other FB1 branches. However, a cross-branch comparison of the number of words that illustrated this phenomenon resulted in low differences. The “Sets” tool also enabled me to regroup the information according to the functions of the site, case, relationship, and other factors. For instance, a set called “FB1–CP influences RC” regrouped relationships to illustrate the influence of CP inputs and antecedents on relational coordination (RC) sub-dimensions in FB1. I was thus able to quickly access the data grouped by sites, cases, and relationships. Finally, I used the “Models” tool to illustrate schemes of the relationships among many different codes. These schemes were useful as they provided more detailed explanations of the links between CP and relational coordination.



The results are presented in function of the influence of CP on the dimensions and sub-dimensions of relational coordination. Given that they present many similarities, I present both cases simultaneously, mentioning and explaining differences where they exist.

The influence of CP on the communication dimension of relational coordination


The influence of CP on the communication dimension is identical for FB1 and FB2 (see Table 4).

Communication frequency


My results indicate that the branch and CC advisors’ perception of CP influences the frequency of communication between actors. First, customers do not always accept the new rules that result from the existence of the CC (willingness). Many of them refuse to give CC advisors the information needed to handle their demand, thus forcing CC advisors to send a message to the branch: “When the customer definitely refuses us to take care of her demand, then we send the

Table 4 - The influence of CP on the communication dimension at FB1 and FB2

message” (CC, FB2). Second, specific customer behaviors such as impatience (behavioral inputs) may demand more frequent communications: “Sometimes, even though we are meeting a customer, we receive a second message from the call-center. The customer has phoned again, and told them, ‘She did not call me back, is it normal? Can you please give her the message again?” (Branch, FB1). Third, the nature or quality of information the customer provides (informational inputs) may oblige CC advisors to contact the branch: “According to the nature of the information the customer gives us, we may need to contact the branch” (CC, FB2). Lastly, the emotional state of the customer (emotional inputs) greatly influences the intensity of communication between the channels: “Sometimes, if they have someone who gets worked up, they call and ask us to deal with the customer, because the situation is tense” (Branch, FB1).

Communication accuracy


Unsurprisingly, my analysis reveals that the communication accuracy dimension is the most affected by CP, because direct communication between channels is mainly rooted in the information the customer provides. Therefore, it seems logical that both informational inputs and willingness should influence communication accuracy. For example, the information the customer provides may be too limited: “Sometimes, the information we have is too short compared to what we would need to answer the demand of the customer. But, well, the question is, did the customer really explain everything clearly enough?” (Branch, FB2). Other customers do not want their demands to be handled by the CC and refuse to provide any information: “It varies a lot, because it depends on what the customer accepted to say. Sometimes, we just have the message ‘please call your customer back, ’ and most often, in this case, it is because the customer did not want to give the grounds for calling” (Branch, FB1). However, not all the branch advisors are aware of this potential impact of the customer on the communication accuracy level: “Above all, what they should be told, at the call center, [is] how important it is for us that they give us correct and appropriate information. (…) When a customer calls because she’s in debt, but (…) intends to make a deposit [and] the call center sends the message, and well, we do not need to call the customer back to hear ‘well, money is about to arrive’” (Branch, FB1).

Communication timeliness and problem-solving communication


Neither communication timeliness nor problem-solving communication was influenced by CP at FB1 or FB2. This finding is not unexpected, since customers generally contact their bank only when they need its services. Another explanation is specific to FB2, where the nature of the main communication means between the channels (fax) is not appropriate, and results in complaints from many branch advisors as they do not receive information on time. Finally, problem-solving communication is difficult to operationalize or measure, and is also close to communication accuracy, which may also explain the absence of a relationship either between communication timeliness and CP, or between problem-solving communication and CP.

Influence of CP on the relationship dimension of relational coordination


In contrast to the communication dimension, the manner in which CP influences the relationship dimension of relational coordination greatly differs between FB1 to FB2 (see Table 5).

Table 5 - The influence of CP on the relationships dimension at FB1 and FB2

Shared knowledge


Shared knowledge relates to the level of knowledge that branch advisors have about what CC advisors do in the CC, and vice versa. During the interviews, it appeared that this level of shared knowledge was significantly higher at FB1 than at FB2. Most CC advisors came from the branches at FB1, but not in FB2. In addition, there was a lack of communication about the nature of CC advisors’ work at FB2, which resulted in a deficit in shared knowledge and a lack of information about each other’s jobs. FB2 interviewees strongly regretted this: “When they [branch advisors] visit the call-center, some wonder if they work in the same company. It is a total discovery” (CC, FB2) and “We do not have that much information about what they can or can’t do. We do not get any of it” (Branch, FB2). This difference may explain why CP influences shared knowledge at FB2 but not at FB1. The absence of shared knowledge likely empowers the customer to “generate” this knowledge, or distort the perception that employees have of their colleagues’ jobs. Uncontrolled by the bank, this influence could have negative consequences. Many FB2 branch advisors indicated that they had accidentally discovered that CC advisors could sell consumer loans: “I was very surprised, because I even received customers who had asked for a loan at the call-center. They had received all the documents, and only came here because they were not sure how to fill them in” (Branch, FB2). This illustrates that the customers did not really understand the process of which they were a part (ability), refused the nature of the participation that the bank required from them (e.g., some customers acted this way out of fear that the mail might be lost), and thus did not do what the bank expected them to do (behavioral inputs).

Shared goals


I identified three goals shared by both banks: (1) economic (superior productivity in each channel and lower global distribution costs), (2) commercial (Branch advisors should sell more because they do not field customer service calls anymore. Plus, at FB2, CC advisors must sell too, just like branch advisors), and (3) customer satisfaction (which all channels aim to increase). It appears that CP does not change the perception of any of these shared goals among branch advisors and CC advisors at FB1. However, it influences the perception of shared commercial goals at FB2. Branch advisors consider that the CC competes with them, insofar as CC advisors must sell products and services only sold by branches in the past. Hence, branch advisors receive less commissions, which generates conflicts: “For some branches, it is not a problem, but it is for many others, who do not regard us as colleagues, but as, well, as thieves, in a way, as though we are stealing their sales from them [embarrassed laugh]” (CC, FB2). Some branch advisors confirmed this perception: “With the call-center selling products, it annoys some advisors. Because they feel like ‘the bread’s taken out of their mouth’; you see what I mean?” (Branch, FB2). These conflicts occur because customers accept to participate the way that the firm expects them to (willingness), by providing enough information to CC advisors (informational inputs) and buying products and services from the CC. In other words, CP alters the perception of shared goals through informational inputs and willingness to participate.

Mutual respect


Mutual respect refers to situations in which service employees deem that their colleagues have performed particularly well in their job and value this contribution. My results enable to refine this construct by identifying four dimensions. The first one, entitled “comfort at work”, involves branch advisors, whose working conditions have improved significantly because they no longer field customer service calls: “Today, even though we all complain at least once a week about one of their mistakes, no one would accept to go back to what it was” (Branch, FB1). The second dimension refers to the respect that the employees show for one another’s technical competencies, assessed through the quality of the answers they gave to customers. Third, they respect one another’s relational competences (i.e., the way their colleagues deal with customer demands). Finally, branch and CC advisors mutually respect commercial results. At both FB1 and FB2, CP influences these four dimensions of mutual respect, relying on the same antecedent (willingness) and inputs (behavioral, emotional, informational, and relational). This influence also seems dynamic, in that mutual respect appears to evolve over time.


To begin with, customers’ willingness and behavioral inputs can influence what I call “comfort at work”: “You know, the customer is in the middle. And they are not necessarily disciplined. They can say ‘the call-center told me to come here’, while it is completely false. And CC advisors can take appointments for us because the customer put a lot of pressure on them… The customer is here, the customer can try and bypass the call-center” (Branch, FB2). In such a situation, if branch advisors were not aware of the customer’s behavior (i.e., unwilling to participate as expected or behaving badly, cheating), they could shift the work discomfort that results from having the customer on the phone onto CC advisors. The nature or quality of the information the customer provides (informational inputs) also affects mutual respect for technical and functional competences: “What’s really great, at the call-center, is that… Well, even we have customers we cannot easily understand, not all of them can make themselves clear, so it is hard to understand them, and that is not always easy, but they [CC advisors] do” (Branch, FB2). Mutual respect is also influenced by the emotional state of customers who interact with service employees: “Well, sometimes, we are angry about what they [CC advisors] did, but when you think about it, you tell yourself ‘well, the customer, she must have moaned, or got mad at this poor colleague’, so it is not easy for them to manage these situations… I think they understand we have a lot of work, and we understand that they, they have unhappy customers to deal with, and they do this well” (Branch, FB1).


Finally, CP influence seems dynamic, in that mutual respect evolves over time. By progressively accepting this new participation, customers have positively influenced mutual respect: “Today, we can answer the customer, but she had [a] hard time herself to become accustomed to this system. Because in a way we cut her from her branch advisor, with whom she had a direct relationship. So at the beginning, she was rather reluctant, but now, it is OK for a majority” (CC, FB1). Moreover, many branch advisors explained that their initial perception of the CC improved when they received positive feedback from their customers about the level of overall perceived service quality, which reflected and demonstrated the technical and relational competences of CC advisors. Stated otherwise, relational inputs dynamically influenced mutual respect, as well as informational inputs (it is necessary that the customer provides this information): “They [customers] say ‘I had one of your colleagues on the phone, very nice, he told me you were busy and that you would call me back, and in the end, he could give me the information I was looking for’. So yes, I think that, overall, it is positive” (Branch, FB1).

Mutual leniency as a potential new dimension of relational coordination


The data analysis indicates a potentially new dimension of relational coordination, namely “mutual leniency.” I define it as the capacity of service employees to forgive their colleagues for making mistakes in their work. It thus differs from mutual respect, in that it reflects the consideration that employees of one channel grant to the quality of the work achieved by their colleagues in another channel. Whereas CP seemed to influence mutual leniency at FB1, this was not the case at FB2.


Whereas FB1 branch and CC advisors shared the same customer contact experience (many of the latter used to work in branches, and branch advisors used to field customer service calls), they usually showed understanding for colleagues who worked in another channel and made mistakes. They knew how difficult and tense their daily job was. For example, branch advisors did not blame their CC colleagues when they did not receive all the information they needed to deal with customers’ demands. They were conscious that it may be because customers were unwilling to provide CC advisors with the necessary information: “Most of the messages I receive include most of the information I need to answer my customer. So when they send me an incomplete one, I suspect that the customer refused to explain the reason of his call” (Branch, FB1). Knowing how customers can behave (behavioral inputs) also increased branch advisors’ leniency toward their colleagues: “They [CC advisors] certainly get inundated with everyday calls. Customers are far from easy to deal with.… They are far from always being pleasant or polite!” (Branch, FB1).


Conversely, CP did not influence mutual leniency at FB2; the lack of information from the bank did. The branch and CC advisors did not blame each other for making mistakes but rather asserted that the bank could have avoided (and could still avoid) many of them: “Well, the way it [the CC and its missions] was presented to them, maybe it was not always appropriate” (CC, FB2).

Emergining result: differential influence of CP on relational coordination


An unexpected result emerged from this analysis: customers’ influence over relational coordination seemed different when they interacted with branch advisors, versus CC advisors. The influence of CP on branch advisors seems to depend on the level of shared knowledge between the channels: “Some customers often told me they had to wait at least ten minutes before they could talk to somebody, and I thought, well, it is not good, not good at all… But now, I know how it works, I went there and I saw them, so I tell the customers, ‘Listen, it is impossible, they have these technical things that prevent customers from waiting more than, say, 3 minutes. Before that, we did not really know, and we had to be the devil’s advocate, in a way, between the call-center and the customers” (Branch, FB2). Because customers and branch advisors gained repeated contacts over time and knew each other personally, the latter were prone to give more credit to their customers when the level of shared knowledge between the channels was low, but they tended to take some distance from what the customers told them when they learned how their colleagues worked in other channels. This finding suggests that the influence of CP on relational coordination could evolve over time in function of the firm’s decisions and actions (e.g. by increasing the level of shared knowledge). Long-term relationships between customers and branch advisors also enabled the latter to recognize the usual behavior of many of their customers, which made them more or less inclined to believe what they were told: “You know, we have customers who are very rude. So from time to time, when one we know not to be very polite tells us ‘they answered me like that, it’s not normal’… We think, well, he must behave the same on the phone, so even though it is the customer’s [word] versus the call-center advisor’s word, it is not necessarily the call-center that did something wrong” (Branch, FB1).


In contrast, the absence of long-term relationships between customers and CC advisors had two consequences. Firstly, “because they do not know [CC advisors]” personally (CC, FB1) and do not have repeated contacts with them, customers are reluctant to give information to CC advisors. This diminishes the communication accuracy between the channels. Secondly, customers have far less power over CC advisors, whose job is highly constrained by the organizational procedures that they must follow, and who are less impressionable because of the absence of direct and repeated contacts with the same customer: “I often have customers who insist [on getting] in touch with their branch. The pressure is very high, sometimes, but the rule is the rule. I only have to take them through if they have a particular need” (CC, FB1).



This article has attempted to investigate how customers can influence relational coordination between frontline service employees. Drawing on the concept of customer participation (CP), I suggest that the perception of CP by frontline employees who need to coordinate with each other could influence the communication and relationship dimensions of relational coordination between those employees. The results of this empirical study provide several theoretical and managerial implications.

Theoritical implications


This research offers two key contributions to the study of relational coordination. First, it illustrates how customers can influence relational coordination between frontline service employees. Second, it suggests that the nature and history of the interactions between customers and service employees are potential moderators of this influence. In addition, this study suggests a new sub-dimension of relational coordination: mutual leniency. Figure 1 builds on these two contributions to propose a conceptual framework of how the customer may influence relational coordination between service employees.

Figure 1 - A framework proposal of how the customer may influence relational coordination

The main contribution of this study pertains to the influence of CP on relational coordination. Previous research on relational coordination has been limited, thus far, to the study of interactions among employees. Customers were either not taken into account (Ahuja, 2003; Anderson, 2006; Gittell, 2002b) or taken into account only indirectly, through the uncertainty that they brought to the process (Gittell, 2002a, 2008). Yet, scholars in services marketing have shown the importance and influence of the customer on service processes (Bendapudi & Leone, 2003; Bolton & Saxena-Iyer, 2009; Wiertz et al., 2004). By combining these two literature streams, my research views the customer as a participant in relational coordination and reveals that the interactions between a customer and frontline service employees may influence relational coordination among the latter. This influence occurs through the service employees’ perceptions of the inputs and antecedents of CP and applies to the two dimensions of relational coordination: communication and relationships. It seems to occur in three different, complementary ways.


First, customers can filter the information between employees, whether consciously, by providing no information at all or different information to interacting employees, or unconsciously, by explaining their issues in a different manner or forgetting to provide the same information. At this level, customers influence the accuracy of the communication between service employees, insofar as they do not deliver the same quality or quantity of information to the diverse employees with whom they interact. Given that relational coordination is coordination between roles, this means that customers endorse a role of “informational filter” between the employees with whom they interact.


Second, customers also influence the frequency of communication between employees. For instance, they may refuse the conditions of their participation with some employees or behave in a manner that initiates or discourages communication and interaction among employees. In line with the role-based nature of relational coordination, the customer could thus be called an “interactional catalyst,” insofar as the way in which some of the inputs and antecedents of CP are perceived by service employees provokes the presence, or absence, of interactions between employees.


Third, customers may influence relational coordination when they positively or negatively modify the perception that employees have of one another. This aspect involves the relationship dimension of relational coordination. The inputs and antecedents of CP seem to influence how service employees regard one another’s work and capacity to fulfill their function, manifested in the level of mutual respect or mutual leniency. In addition, this perception might be altered by the influence of the customer on the level of shared knowledge and shared goals between employees. Thus, customers can trigger either the improvement or weakening of the opinion that service employees have of one another. Accordingly, I suggest that customers may act as perceptual catalysts in their relational coordination between service employees.


To reflect the insights that come with this first contribution, I offer the following propositions:


Proposition 1: Customer participation influences relational coordination among frontline employees through the perception that these employees have of (a) the inputs and (b) the antecedents of customer participation.


Proposition 2: The customer may play the role (s) of (a) “informational filter,” (b) “interactional catalyst,” and/or (c) “perceptual catalyst” in relational coordination among frontline employees who must coordinate with one another.


Next, the unexpected result that emerged from the analysis may lead to a second theoretical contribution: the scope of the customer’s influence may depend on the nature of the interactions between the customer and frontline employees. These interactions can be of two kinds: a service relationship or a service encounter (Gutek, 2000). In the former, the customer and service employee expect to have repeated contacts in the future and to get to know each other on a personal level, such that “they develop a history of shared interaction that they can draw on whenever they interact to complete some transaction” (Gutek, 2000, p. 371). In this paper’s empirical study, this refers to the nature of the interactions between customers and branch advisors. Conversely, in service encounters, the customer interacts with different service employees over time – such as when contacting a CC – and these are expected to be functionally equivalent. Hence, the customer does not expect to be served by the same employee again, and no history of interactions develops (Gutek, Groth, & Cherry, 2002). My results illustrate that customers may have a stronger influence on relational coordination when they have relationships, rather than encounters, with service employees. Indeed, in the case of service encounters, employees seemed less influenced by customers, and instead seemed to favor procedures over customers. This tendency also arose with customers’ decreased willingness to provide information to employees whom they did not know or necessarily trust, which in turn lowered communication accuracy among employees. Contrariwise, the influence of customers seems greater in service relationships, because employees are more likely to favor their demands. However, a high level of shared knowledge between employees appears to counterbalance the influence of CP. A potential explanation for this is that prior interactions with service employees influence subsequent customer reactions (Bolton, Smith, & Wagner, 2003). Thus, I offer the following additional propositions:


Proposition 3: The scope of the customer’s influence on relational coordination among frontline employees depends on (a) the nature and (b) the history of interactions between the customer and frontline employees.


Proposition 4: The customer has a stronger influence on relational coordination among frontline employees when having a relationship, rather than an encounter, with these employees.


Finally, an emerging result also suggests a new sub-dimension of relational coordination, which I call mutual leniency. Although not directly related to the main aim of the study (i.e., to explore the influence of the customer on relational coordination), it may enrich the concept of relational coordination. Previous research has identified shared knowledge, shared goals, and mutual respect as the three sub-dimensions of the relationship dimension of relational coordination (Gittell, 2002b; Gittell et al., 2010). This study suggests that mutual leniency could be a fourth. Mutual leniency refers to the capacity of service employees to forgive colleagues with whom they coordinate for making mistakes in their work. It differs from mutual respect, which pertains to situations where service employees value the contributions of their colleagues. Because relational coordination focuses on relationships between roles (Carmeli & Gittell, 2009; Okhuysen & Bechky, 2009), mutual leniency means forgiving colleagues who did not fulfill their role, whereas mutual respect means acknowledging colleagues’ high-level role fulfillment. Mutual leniency thus requires a certain amount of role empathy among employees, since they must show tolerance and understanding with regard to the mistakes made by their frontline colleagues. In this study, I focused on such mistakes as results of customer participation, but other contexts could be considered. Mutual leniency could occur even in the absence of a customer, depending on work circumstances (e.g., when an employee makes a mistake because they are new to the job or when an employee makes an uncharacteristic mistake) or individual characteristics (e.g., a personal tendency to be tolerant or show understanding). Thus:


Proposition 5: Mutual leniency, defined as the capacity of service employees to forgive colleagues with whom they coordinate for making mistakes in their work, is a fourth complementary sub-dimension – in addition to shared knowledge, shared goals, and mutual respect – of the relationship dimension of relational coordination.

Managerial implications


At a managerial level, this research underscores the potential importance of managing CP to improve coordination processes in service firms. The inappropriate management of CP is likely to lead to at least three detrimental consequences. First, relational coordination is supposed to increase organizational efficiency (Gittell, 2002a; Shah et al., 2008), but the influence of CP on relational coordination may make this relationship more complex to determine. Second, relational coordination plays a significant role in the level of service processes’ quality outcomes (Gittell, 2002b; Gittell et al., 2010) and, accordingly, in the level of customer satisfaction (Anderson, 2006), such that customer satisfaction may be negatively altered by the influence of CP on relational coordination. Third, firms may overestimate their customers’ profitability, because the coordination costs that may result from the detrimental influence of CP on relational coordination are usually either unforeseen or hidden.


Firms could avoid or limit these possible damages by enhancing customer socialization, increasing shared knowledge among employees, and developing new indicators of customer profitability. First, firms should rely on socialization techniques in a traditional manner (that is, integrate customers in their processes more efficiently) and in a way that can transform CP into a positive organizational leverage. This could improve many dimensions of relational coordination, and would prevent customers from raising relational barriers between employees. Put another way, firms should learn how to use their customers, because their positive feedback may facilitate organizational change (here, the introduction and development of a new channel).


Second, firms should encourage good communication and high levels of shared knowledge between service employees, especially if these employees are geographically distant. In order to limit the detrimental consequences of negative customer feedback on relational coordination, companies need to ensure that customers’ positive feedback is diffused and known throughout the organization. This is likely to reinforce mutual respect and increase perceptions of shared goals.


Third, because of the potential coordination costs resulting from the detrimental influence of CP, classic indicators of customers’ profitability could become less reliable—a risk that has most certainly increased in a world where both firms and customers use multiple channels (Kumar, Lemon, & Parasuraman, 2006), and therefore where customers are likely to interact with many different frontline employees. It thus seems necessary to forecast the potential negative impact of customers on service organizations. Firms should draw detailed blueprints that would allow them to identify the manner and moment in which a customer may hinder relational coordination between employees. They can also rely on their own customer relationship management data to statistically evaluate the manner and the moment in which customers are most likely to negatively influence such coordination.




Although the current study provides an important step in clarifying how customers fit into, and affect, relational coordination, it has a number of limitations. The first is that although relying on qualitative data led to noteworthy results, it also rendered these results less amenable to replication. This study setting is restricted to a specific sector of activity that does not support generalization of the results. Second, I was not able to evaluate the impact of each bank’s different status or structures on the influence of the customer. As a mutual bank, FB1 aims to be closer to its customers and to implicate them more than FB2, but my data did not enable me to determine whether these statutory differences altered the influence of CP. Organizational differences between the two banks occasionally seemed to explain some variation in the scope of customer influence on relational coordination, yet these differences were not studied in depth here.


Third, I collected the data only from employees, which was appropriate considering the overall research aim, namely, to investigate how service employees’ perception of CP could influence relational coordination between these employees. However, collecting additional data directly from customers or, even better, from customer–employees dyads would likely have provided complementary insights. For example, I could have put into perspective the actual desire of customers to influence employees and relational coordination, as well as the importance that employees grant such participation. This would have enabled me to determine whether the customer’s influence was voluntary or involuntary. Finally, customers’ individual characteristics (e.g., age, income, educational level, lifestyle) were not taken into account, and might help to explain some of the customers’ relative influence on relational coordination.

Further research avenues


These limitations can be seen as fruitful avenues for further research. In addition, scholars may wish to pursue at least three other directions. First, the use of quantitative studies to test the propositions and the conceptual framework formulated in this article would provide worthwhile, generalizable insights for the study of relational coordination. Additional qualitative research about relational coordination and the potential influence of the customer might enable a deeper exploration of the phenomena pointed to in this study. Although research on relational coordination has partially involved some qualitative research methods (Gittell, 2001, 2002a), most work relies essentially on quantitative approaches (Carmeli & Gittell, 2009; Gittell, 2000, 2008; Gittell et al., 2010). A qualitative stance, now that the concept is theoretically well-established, could result in a more profound understanding of its underlying processes and enable “analytic (link to theory) and naturalistic (link to experience) generalization” (Buchanan & Bryman, 2007, p. 494). This would usefully complement, and be complemented by, statistical generalizations from quantitative research.


Second, this study has underscored how customers can harm coordination among employees. High levels of relational coordination improve employees’ performance and customers’ outcomes (Gittell et al., 2008; Shah et al., 2008), yet this study shows that these can potentially be limited by CP. In other words, CP generates coordination costs, as manifested in losses to the firm’s efficiency or customer satisfaction. However, researchers and practitioners alike rarely take these costs into account (Chan et al., 2010), or else do not recognize the potential positive influence of customers. In a world where co-creating with customers is becoming the norm (Cova & Salle, 2008; Dong et al., 2008; Vargo & Lusch, 2004), the potential harm CP can bring to coordination raises the following worthwhile research avenues: To what extent can customers generate coordination costs? To what extent do these costs limit the firm’s efficiency? How can a firm combat hidden coordination costs?


Third, it would be of interest to explore the influence of CP on coordination mechanisms. The study of coordination comprises research on the organizational design and coordination mechanisms on the one hand, and on the coordination process (relational coordination) on the other (Okhuysen & Bechky, 2009). Some scholars suggest that customers influence coordination mechanisms through the uncertainty they introduce in service processes (Argote, 1982; Bowen & Jones, 1986; Larsson & Bowen, 1989; Rathnam et al., 1995). Yet, the exploration of whether the antecedents and inputs of CP actually influence these coordination mechanisms might lead to a more fine-grained management of CP and the firm’s coordination mechanisms, which could help limit the detrimental effects or increase the positive consequences of CP.



By providing qualitative inputs that complement previous quantitative studies on relational coordination, this article adopts a transdisciplinary perspective, and suggests that including customers in the set of participants in relational coordination offers a better understanding of the coordination process between service employees. This process may be enriched or disrupted by the participation of customers, due to the way that frontline employees who coordinate with one another perceive the inputs and antecedents of customer participation. Furthermore, the customer’s influence appears to depend on the nature and history of the interactions between the customer and frontline employees. This finding may pave the way for further research that could provide a clearer view, and a more precise understanding, of the role that customers can play in a firm’s organizational dynamics. The increasingly important role of the customer in firm processes (Chesbrough, 2006; Payne et al., 2008; Plé et al., 2010) clearly raises the need for such studies.


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These informational inputs are also called “mental inputs” (Rodie & Kleine, 2000), but this term might be misconstrued, since it can also refer to the mental state of customers. Thus, in line with other research (Fang, 2008; Fang et al., 2008; Mills & Turk, 1986), I have used the term “informational inputs.” I thank a reviewer for this insight.



Although their importance in service operations is widely acknowledged in services marketing literature, the place and role of customers in organizational theories remain unclear. In particular, the way customers may influence the firm’s intra-organizational coordination has received little attention. By combining services marketing and intra-organizational coordination theories, this paper contends that customers may influence the coordination process among service employees, also called relational coordination. Relational coordination is a process that focuses on the interactions among the roles endorsed by employees who participate in this process, carried out through communication and a web of relationships among these participants. It is argued here that customers should be included among the set of participants in relational coordination; they might influence relational coordination among service employees through the way service employees perceive customer participation (CP) in service processes.
This article proposes a conceptual framework of the potential influence of CP on relational coordination among frontline service employees, by reporting the findings of case studies carried out in two multichannel retail banks. The data analysis offers two main results. First, the way in which frontline employees perceive inputs (i.e., what customers bring to service processes) and the antecedents of CP (i.e., reasons customers participate in service processes) appears to influence relational coordination among employees. Second, this influence seems to be moderated by the nature and history of the customer–employee interaction. The data analysis also suggests mutual leniency as a potential new sub-dimension of the relationship dimension of relational coordination. Presented as five propositions, these results offer some limitations and further research directions discussed at the end of the paper.


  • Relational coordination
  • customer participation
  • retail banking
  • multichannel

Plan de l'article

    1. Antecedents of CP
    2. Inputs CP
    1. Research field
    2. Data collection
      1. Data triangulation
      2. Reading over the interviewees’ shoulders
    3. Data analysis
    1. The influence of CP on the communication dimension of relational coordination
      1. Communication frequency
      2. Communication accuracy
      3. Communication timeliness and problem-solving communication
    2. Influence of CP on the relationship dimension of relational coordination
      1. Shared knowledge
      2. Shared goals
      3. Mutual respect
      4. Mutual leniency as a potential new dimension of relational coordination
    3. Emergining result: differential influence of CP on relational coordination
    1. Theoritical implications
    2. Managerial implications
    1. Limitations
    2. Further research avenues

Pour citer cet article

Plé Loïc,  Clegg Stewart R., « How does the customer fit in relational coordination? An empirical study in multichannel retail banking », M@n@gement, 1/2013 (Vol. 16), p. 1-30.

DOI : 10.3917/mana.161.0001

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