How service customers tidy their service quality perceptions

1 Introduction

Their importance is rising globally as services already account for eighty percent of the US economy. Yet academic research might not have fully recognized how crucial service innovation and management are [1, 18]. For example, the operations management (OM) field remains mostly focused on manufacturing, instead of predominately looking at services [14]. Some researchers do call, however, for the development of a transdisciplinary services science [3], defined as “a general theory of service with well-defined questions, tools, methods and practical implications for society” [18, p. 71].

The success of individual firms and entire economies heavily depends on service innovation [10]. With commoditization becoming common in many industries, goods-dominant firms are also vying to build services [1]. So they are learning to handle close supplier and customer interaction, simultaneous production and consumption, and utilization and co-creation of tacit knowledge, i.e., those wonderful service characteristics [3]. IBM, an information-technology (IT) manufacturing leader, found that services constitute its fastest growing business, yet it had not allocated enough of its R&D budget to services [16].

With IT becoming a critical enabler of service firms [2, 3], services science has also been described as “a melding of technology with an understanding of business processes and organization” [16, p. 18]. Emerging business trends, such as Software-as-a-Service (SaaS) and, more broadly, cloud computing, signify the increased importance of digital and Internet services. IT service management has become crucial to the IT function [13, 20]. Plus, a failure to appreciate the growing significance of knowledge networks and our ecosystem’s network structures [11, 12] can lead to poor service quality performance worldwide.

A typical service design matrix incorporates both policy analysis and design variables, such as innovation, operational focus and worker requirements, allowing grouping services into ‘pure’, ‘mixed’ and ‘quasi-manufacturing’, along the most crucial, service customer contact dimension [4]. Pure services, such as legal and medical services, require direct, face-to-face contact, with loose specifications between each customer and service provider. Quasi-manufacturing services entail little or no contact among service customers and workers, while mixed services have two components: the first requires little labor, but the second is very labor intensive.

Quasi-manufacturing service examples are television broadcasting, with its transmission and production components, and research and development (R&D), both requiring both expensive equipment and human thought. Like pure services, mixed services require intense customer contact, but within tight specifications. Customer contact can be active or passive, with its intensity varying across industries. Generally, producers of tangible goods, such as soap and beer, limit customer contact to the retail end.

Customer preferences are crucial to manufacturing production, but the actual customer presence is not. Conversely, customer contact intensity is high both in pure and in mixed services, while quasi-manufacturing services offer a high innovation potential. The explosive growth of the latter matches that of associated technical progress, so that their service customers can potentially reap the benefits of great service variety at a declining cost [5].

Intense customer contact blurs functional boundaries and makes measuring service quality and productivity difficult. Service employees who interact with customers end up performing service production and delivery as well as many marketing tasks. Customer perceptions of service quality depend on interpersonal skills, such as courtesy, friendliness, pleasantness and tolerance. Service quality involves both the processes and the outcomes of service production, delivery and consumption [15].

Besides, service quality’s impact on service firm performance depends on the difference between customer expectations and quality perceptions. Service promotions raise customers’ quality expectations; if not met, then a gap forms between customer expectations and quality perceptions. Cognitive science and psychology eagerly explain how service customers form such gaps in their minds.

Yet the ultimate service quality design and management challenge is to anticipate these gaps, not just in the minds of past and present service customers, but also in the minds of future and potential service users. And, the more distant the future is, the more challenging a customer-centric service quality design is, particularly for future, potential service customers and users.

In the context of service quality design and management, Parasuraman et al. focus on accessibility, communication, competence, comprehending and knowing the customer, courtesy, credibility, reliability, responsiveness and security [15]. They also stress tangibles, such as the appearance of personnel, physical facilities, and the sophistication of the tools used to provide a service. Knowledge of the service product, attention to customers and courtesy rank as the top causes of good service quality. Rudeness, indifference or the “I don’t care” attitude and a reluctance to correct errors rank as the top causes of poor service quality.

Price correlates negatively with perceived service quality. If customers feel that the price of a service is unfairly high, then their service quality perceptions will be low. Both case and empirical studies converge on the most salient causes of service quality. The empirical studies contribute to theory building through hypothesis testing, but the cases provide an equally important, indeed necessarycontribution.

Some cases suggest linking service front-line variables, such as customer expectations and service quality perceptions, with promises that firms make to customers. Others call for integrated models that help service managers and researchers consider quality at the service design phase.

Motivated by the need to build more insight into the dynamic complexity of service quality and performance, this study’s research results inform managers of service firms who seek to design effective and productive service quality strategies. It contributes to the emerging dynamic-complexity research, information systems and digital services [6–9]. The unceasingly counterbalancing counterweight being each and every failure to comprehend the dynamic complexity of service quality, which usually is detrimental to a service firm’s performance.

The system dynamics (SD) modeling method provides a very useful approach to assessing service quality’s dynamic complexity in service organizations. The method’s internally coherent structure offers the integrated perspective required for comprehending how service quality works.

Grounded on the contributions of earlier research, this article’s SD simulation model shows the dynamic interdependence of policy and service quality performance variables in a typical service firm. Besides its other, direct implications for service quality research and practice, the simulation output reveals the morphology of a plausible topology, showing how service customers might neatly tidy their perceptions of good and poor service quality.

2 Model structure

While seeing defecting customers as a measurable metric, the top management of this service firm is poised to soliciting feedback from its defecting customers. Aimed at averting potential losses, a company-wide, continual quality improvement effort guides the service firm’s defecting customers analysis. The pertinent level and rate variables, and the auxiliary constant parameters of the stock and flow diagram on Fig. 1, both propel and curb the buying decisions of the service company’s present customers. Together, these pertinent variables and parameters depict its present customers’ expectations of attention, courtesy and knowledge, i.e., their service quality perceptions.

On Fig. 1a, the model’s service customer sector, potential service users consider using the service at the prevailing industry rate. Even though some respectfully decline to join the firm’s current customer base, if potential service users do use the service, according to the industry’s use norm, then they become the service firm’s customers. Customers stay with the firm until either the industry’s defect norm or a gap between their perceived over expected service quality (Fig. 1b) turns them into Defectors (Fig. 1a). Worth noting is that their defecting is not necessarily permanent, but is in a state of flux.

A few Defectors stop using the service completely yet, depending on the industry’s use norm, most of them reuse the service unless, of course, a gap between their perceived over expected service quality emerges. This gap determines sales opportunity through the prevailing use, reuse and defect rates, so it moves Defectors permanently into a competitor’s service customer base. As long as service Customers stay with the firm, they generate sales and profit; hence the longer it keeps its service customers, the more profit the service firm makes.

On Fig. 2b, the model’s customer expectations and perceptions sector, together, the calls soliciting feedback from defecting customers and service quality, collegially co-determine the firm’s sampling opportunity. Yet service quality is enabling with diminishing returns.

The higher service quality is, the more permeable each call contact is, the higher each sampling opportunity is for defecting customers’ feedback, which the firm effective turns into service quality improvements. Conversely, the lower service quality is, the less transparent the contact is, the less effective the firm’s sample of Defectors, the less effective the feedback the service firm receives from its defecting service customers.

Unlike manufacturing, services do not always allow inspecting quality before delivery and consumption. During each call the firm makes, each defector shares the cost of appraisal, while verifying and inspecting service quality, as an ad hoc client-worker team emerges.

Errors need not ‘get out’ to affect perceived service quality. During these calls, Defectors witness costly investigations and adjustments, penalties and lost service accounts, right there on the ‘shop floor’ of a phone call.

Many Defectors are disappointed customers. Even if they defect temporarily, assuredly they spread negative (–) ‘word of mouth’, with a time delay or lag.

Modified through the service firm’s Calls, the defector gap directly affects reuse, but the use and defect rates also embody word-of-mouth weights. Although reciprocal, the defect and reuse rates carry weights of equal magnitude because retained customers— who have already experienced the firm’s defector call treatment— have already been sensitized and thereby are more perceptive and receptive to word of mouth than potential service users are.

Many of the parameters affecting these rates depend on the three-sector model’s nine auxiliary graphical-table functions. The left and low side panels on Fig. 1 show these functions, which mostly stem from the firm’s service manages and workers’ judgements, manifested in multiple in-depth interviews and interactive dialogues with them.

The service firm monitors and calls defecting customers from its fondly called ‘hut of quality’, hence the hut of quality in services sector on Fig. 1c. Along with the effective feedback, which the service firm needs in order to improve its service quality, the gap, ghosted here (Fig. 1c) from the model’s customer expectations and perceptions sector (Fig. 1b), often causes unsolicited complaints.

Call decisions depend on service quality’s enabling effect, i.e., both on feedback effectiveness and on the service firm’s call limit or limCall. The firm’s managers control this limit through their collegially and consensually decided internal policy lever of the average customer life target (Fig. 1c).

Yet, on Fig. 1c, the firm’s average customer life target is just one co-determinant of the firm’s call limit or limCall. The second co-determinant of this internal policy lever is the average customer life or avgLife of the firm’s Customers stock, with avgLife also ghosted here (Fig. 1c), now from the model’s service customer sector (Fig. 1a).

During our in-depth interviews and interactive dialogues with the service firm’s managers and workers, all our action- or praxis-research participants were sensitized to ‘Little’s Law’, named after John D.C. Little, the operations research (OR) professor at MIT, who proved it first [19, p. 423]. This is a rather famous and most useful queueing law, which equates the average number of customers in a service firm’s queue or waiting line, with the rate at which customers arrive and enter the queue, multiplied by the average customer sojourn time, regardless of the probability distribution of the line’s outflowrate.

In the simulation model’s initial, dynamic equilibrium phase, on Fig. 1a, the use inflow, which feeds the service firm’s Customers stock, and the defect outflow, which depletes the firm’s Customers stock, are equal. Hence, according to Little’s Law, the ratio of the Customers stock to its defect outflow determines the average residence time or avgLife of the service firm’s Customers.

3 Model behavior

Initializing the model in equilibrium prevents latent artifacts of naturally deterministic relations, operating within each individual model sector, from contaminating the computed behavior patterns through time or the dynamics of the firm’s Customers, Defectors and profit per customer metric. To conduct the required model tests, the SD simulation model was disturbed from equilibrium through step increments of a 1 (one) percent change in the firm’s average customer life target, 30 weeks from the initial time.

This rather simple procedure allowed assessing how the firm’s customer base, defectors and profit per customer metric respond to incremental changes in the firm’s average customer life target. Moreover, the same procedure made the SD simulation model’s dynamic behavior patterns through time fairly easy for the service firm’s managers and workers tointerpret.

The first two, time-series graphs on the top panel of Fig. 2 show how the Customers level rises and the Defectors level drops by 0.5 percent, respectively, in response to a 20 percent step increase in the firm’s average customer life target. These changes show up 21 weeks after the initial step increase in the internal policy lever target, at t = 30 weeks.

On the third time-series graph of Fig. 2, the resulting discrepancy between avgLife and its target causes Calls to increase. As a result, Defectors’ expectations now begin to exceed their service quality perceptions.

The rising Calls convey an increased attention to defecting customers, so the sampled Defectors’ service quality perceptions rise, only to further raise the defecting customers’ service quality expectations. With the gap dropping quickly after its initial increase, customer complaints rise too, pushing service quality back down.

This happens independently of the firm’s effort to improve its service quality through Calls, which solicit feedback from its defecting service customers. In turn, service quality’s low enabling effect reduces Calls, along with the associated complaints received about these service calls, so service quality starts rising again, increasing the defecting customers’ perceived service quality.

This cycle repeats itself through time, while service quality and the gap reach higher levels each time they peak. Once service quality exceeds its equilibrium level, the reversed gap between perceived and expected service quality causes a drop in the firm’s Defectors.

Defectors reuse the service, thereby again increasing the service firm’s customer base, so the Customers’ avgLife goes up, again reducing the firm’s number of Calls to its defecting customers. The phase plots at the low panel of Fig. 2 both clarify and confirm the characteristic features of this service quality cycle.

The Call fluctuations affect each sample that the firm takes from its Defectors, which in turn affects these defecting customers’ perceptions and expectations of service quality. It is only at the peak of the gap between perceived and expected service quality that Defectors respond to, setting the simultaneously plotted values on the phase plot of Customers vs. Defectors of Fig. 2 into a small cycle, in this firm’s quasi-manufacturing service.

Yet under pressure from an unyielding average customer life target, the service firm’s Calls enter into a cycle-doubling pattern, unable to return to their initial, dynamic equilibrium level. This pattern becomes even more apparent on the through-time development plots, on the top panel of Fig. 3.

Now, the firm’s 10-year average customer life target increases the amplitude of the call limit cycle as well as the amplitude and frequency of the Customers and Defectors cycles. The phase plots on the low panel of Fig. 3 verify this dramatic increase in amplitude, particularly in the Customers and Defectors cycles. By itself, the service firm’s policy of ‘zero defections’, i.e., keeping every single customer the firm can serve profitably, might not always produce the desired service quality performance its service managers anticipate.

On the top panel of Fig. 4, the time-series graph of the firm’s profit per customer metric shows how futile the adoption of such a drastic policy change can be, if not combined with much more substantive, service process redesign or reengineering efforts. Such efforts always entail changing either a service firm’s entire organizational structure or at least its service production, delivery and consumption process or processes.

Service process redesign or reengineering efforts, must always aim at collegially creating an organic, truly systemic structure. Only such a structure enables service customers’, employees’ and suppliers’ autonomy, mastery and purposefulness, toward the emergence and sustainability of high-quality service production, delivery and consumption processes.

Pulling too hard on a conventional service firm’s internal policy levers, i.e., on its average customer life target, can jolt or shock its defecting customers’ attention from good to poor service quality perceptions. Figure 4 shows the associated morphology of a likely topology that emerges, which might be possibly underlying the defecting service customers’ gap between expected and perceived servicequality.

Much like the ‘Hugoniot loop’, which mathematical physicists use in assessing the jolts that emerge near the sonic lines of shock waves, the three-dimensional middle panel on Fig. 4 shows such a Hugoniot loop-shaped crossover of defecting customers’ perceived service quality. In two dimensions, this to and fro crossover between good and poor service quality perceptions, evidently shows up on the phase plot of the service firm’s Defectors sample vs. gap values, on the low right panel of Fig. 4.

Looking at how a typical service firm’s customers neatly tidy their good and poor service quality perceptions might unveil non-homotopic paths on tori surfaces (middle panel, Fig. 4). Each such torus bears an infinite number of closed, non-homotopic paths, which do not touch each other at any point and, normally, do not deform one into another, either continually or continuously.

Tentatively, in their minds, a typical firm’s service customers might continually or continuously group together their perceptions of good service quality attributes, characteristics and variables, on the good quality torus of Fig. 4. Congruently, they might also continually or continuously group together their perceptions of poor service quality attributes, characteristics and variables, now on the poor quality torus of Fig. 4.

4 Conclusion

Motivated by the need for insight into the dynamic complexity of service quality design, management and performance, on the one hand, this study’s findings inform managers of service firms, who seek to design superior service quality strategies. On the other hand, however, not comprehending the dynamic complexity that service quality design, management and performance entail always is detrimental to a service firm’s performance.

The system dynamics modeling method allows studying exactly how its relational structure determines the dynamic behavior of a true system through time. Action or praxis research via SD modeling and simulation can help approach, formulate and even dissolve the highly problematic decision situations, which service firms often encounter, in their corporate-, business- and functional-level strategy making, implementation and control processes.

If nothing else, SD praxis research can at least prevent the managers of some service firms from adopting even more of those, worldwide famous, company-wide ‘zero solutions’ initiatives. Typically, such initiatives result in self-inflicted problems in service organizations. An a result, some of them end up treating their customers, whether defecting or not, not as human beings in and about a true service system, but like widgets to churn out of a fully-automated assembly line.

This case study shows how the decision scenarios computed with a SD simulation model can trace the sinister behavior patterns experienced with service quality performance to the inauspicious effects of service managers pulling on internal policy levers too hard. A radical change in the firm’s average customer life target triggers a cycle-doubling pattern, forcing the entire service organization along with its customers to respond accordingly.

SD praxis research can easily provide the integrated-process view required for comprehending self-inflicted problems in services. Along with its policy analysis and service quality design and management implications, the SD simulation output also suggest the morphology of a certain topology, according to which a service firm’s customers might sprucely declutter their good and poor service quality perceptions.

These research results’ immediate, inductive implication being that, in their minds, service customers might indeed be continually or continuously separating good service quality attributes, characteristics and variables from poor service quality attributes, characteristics and variables. Tentatively or scientifically put, the intermediate or medium-term implication of the research results here might be opening up new vistas for fruitful service quality research.

Indeed, the need is urgent for service quality research, aimed at more effective and, at once, more productive, customer-centric service quality design and management, than the ones humankind has been so far experiencing. The collegial purposefulness of future service quality research must be, we move, high-quality service production, delivery and consumption processes, to the benefit of all concerned.