IV: Service design, delivery, and customer engagement

Service digitization and the provider-to-customer handoff

Eileen Bridges, Charles F. Hofacker, and Chi Kin (Bennett) Yim


It may be argued that, in an active and increasingly specialized economy, busy consumers increasingly engage service workers to complete tasks for which they do not have time; these tasks typically require physical labor (e.g., yard work or meal preparation). On the other hand, digital technology allows many information-based (i.e., intangible) sendee tasks to be completed more quickly and easily, whether by service employees or by the customer directly. Therefore, completion of the latter type of task tends to migrate in the opposite direction, from service employees to consumers. For example, in the past, medical doctor schedules were kept in physical form, such that patients who wanted to make an appointment would have to either call the office or visit in person. This function is now often handled using a self-service technology (SST). However, other aspects of a medical visit may not yet be available in a virtual fonn. For instance, medical doctors typically require patients to visit the office in person in order to receive a diagnosis and/or prescription. Therefore, it is possible that additional tasks involved in a medical visit could be virtualized in the future; in fact, some physicians are already offering online visits for more common and benign ailments, and prescriptions are being sent electronically for fulfillment.

Other information-based service processes are already largely digitized. For instance, tasks such as investigating travel options, making reservations, handling purchase of tickets, checking in, and obtaining virtual boarding passes or room keys may be completed entirely by a customer using SSTs. Thus, these customers engage in technology-based self-service (TBSS), according to Dab- holkar (1996). Some service processes have partially migrated to consumers, with tasks such as placing an order for groceries or clothing utilizing SSTs; however, the remainder of the process (which requires physical labor) continues to be completed by service providers, including packaging and delivery of items. The present chapter considers the set of all service processes executed in the economy that do not require physical labor. Some subset of the tasks involved in these processes is perfonned by service employees and another subset of these tasks, fonnerly executed by employees, is perfonned by customers using SSTs. During a particular period of rime, some elements of the former subset (i.e., tasks perfonned by employees) migrate into the latter subset (i.e., perfonned by customers). The purpose of this chapter is to better understand which service processes tend to migrate from sendee providers to customers, when, and why.

To organize thought regarding which current employee processes are already or will be digitized and then operated directly by customers, service processes may be described as either physical (e.g., baggage handling) or informational (e.g., providing flight infonnation) categories. These two categories reflect “tangible” and “intangible” core service processes, as defined by Lovelock (1983). Among informational processes, Lovelock (1983) identified “mental stimulus processing” as acting upon a person, and “information processing” as acting upon a possession. Another way of viewing informational processes is to identify them as either: (1) those enabled by digital storage and communication (e.g., providing account balances) or (2) those enabled by digital computation and processing (e.g., investment advice). The latter elements may be sub-divided into sensing/signal processing (e.g., voice recognition) and higher-order cognition/symbolic processing (e.g., artificial intelligence (AI)). This chapter takes as its domain the full range of informa- tional service processes, and describes the process by which they are moved into SSTs.

A particular service encounter may require many tasks that include both tangible and intangible elements, and operate on either people or their possessions. This chapter proposes that a firm’s decision to digitize a particular informational task in a service process depends, in addition to social and cultural considerations, on technical aspects such as the labor costs involved in carrying out the task manually, the number of states required to digitally model the task, and the number of times the task must be repeated (Hilbert 2014a, b). Perhaps more importantly, if the newly digitized task is to be carried out by consumers, issues such as sensor)' needs, relationship preferences, synchronization requirements, and needs for identification and control must also be considered in making the decision to virtualize for customer use (Overby 2008).

In summary, the focus of this chapter is on informational service processes and tasks which can be digitized; the goal is to examine which individual tasks and complete processes are likely to migrate to SST form, and how customer and firm adoption considerations affect this migration. To narrow the focus of the chapter, the assumption is made that processes and/or tasks which are handed off to customers have been virtualized prior to making the handoff. Therefore, there are two situations to consider: (1) the process is currently handled by employees in person, but is digitized to hand off to customers, and (2) the process has already been digitized for employees and is handed off to customers using similar technology. From the firm’s point of view, situation (1) is more complex, because the service process must first be digitized; situation (2) might be viewed as simpler because the technology is already available and may require only minor modification for customer use. From the customer point of view, it does not matter whether the firm faces situation (1) or (2); customers are presumed to adopt a digitized version of the service task or process and to begin using TBSS at the time of the handoff. Therefore, it is equally important in both situations for the firm to understand and consider potential customer reactions before deciding to hand off the service process to customers in the form of an SST.

The conceptual development section begins by providing background on adoption of SSTs to position this chapter within the relevant research streams. Next, it looks at the customer point of view, including elements that may encourage or discourage use of an SST. It continues by considering this topic from the firm’s point of view, examining literature that relates to development of new SSTs as well as trial and continuing use of existing SSTs. Next, it goes into firm considerations when making a decision to invest in an SST, as well as customer decisions regarding use of SSTs. Background regarding how a firm might decide when to invest in digitizing a service process, and when to hand it off to customers for their direct use is also described. The chapter continues with some qualitative predictions regarding service processes that will be handled by customers using digital technology, based on the conceptual framework that is developed.

Conceptual development

The SST lifecycle

For the first half-century of their existence, telephone calls required operator assistance. Beginning in 1951, long distance calls could be self-dialed, and it would be humorous to refer to placing a call as an “SST.” (Even use of the word ‘dialed’ is long out of date!) This observation illustrates how successful SSTs follow an evolutionary process that begins with a digitizable innovation and finishes with ubiquity (Brodie, Winklhofer, Coviello, & Johnston 2007; Hennig-Thurau et al. 2010). Thus, the spread of a successful digital innovation follows a sort of diffusion process, penetrating the population at an increasing rate (Parasuraman 2000) until virtually all potential customers have adopted (i.e., the innovation reaches ubiquity, as illustrated in Figure 15.1). At this point, the service process is no longer considered an SST, because it is perceived as always perfonned by customers, and is no longer compared to a provider-perfonned service.

Of course, in some cases an innovation fails and the related SST does not reach ubiquity; this possibility is also shown in Figure 15.1. Another possibility' is that a particular innovation is replaced by some superior SST that comes along later. For instance, it is not yet known whether a sufficient number of consumers will adopt device-driven home security/operation systems to pennit these products to continue in their present forms. If an insufficient number of consumers adopt, it would become too expensive to continue updating software and delivering these digital products; consequently, those brands already on the market might fade away'. Alternatively', the present device- driven technology might be replaced by some more advanced technological breakthrough.

Dynamic process of adoption of SST

Figure 15.1 Dynamic process of adoption of SST.

Although possibly transparent to the user, a new digital technology could potentially accomplish the same purpose more conveniently and/or at lower cost. In this example, the current digitized service process for home security/operation would not reach ubiquity.

As mentioned above, the goal of this chapter is to identify the set of service tasks now performed by employees (either in person or using a virtual interface) that will migrate to SSTs in the near future, and to better understand the reasons behind firm decisions to migrate. Although the approach taken in this chapter is more descriptive than prescriptive, it is clear that understanding the decision to introduce new SSTs is quite important. Ceteris paribus, as the number of service employees declines, productivity increases. On the other hand, as SSTs emerge and productivity increases, corresponding sectors of the economy will suffer downturns in employment. (Aside from these societal level phenomena, predicting future adoption patterns for SSTs would be useful in identifying service investment opportunities.) More germane to the present purpose is the fact that within larger firms, the marketing and IT functions are most involved in developing and managing SSTs and have an inherent interest in understanding their evolution (Bridges & Hofacker 2016). Both disciplines would benefit from a positive theory that anticipates the SST cycle illustrated in Figure 15.1 and facilitates strategic thinking. For academics in business disciplines, this positive theory suggests important future research topics, and perhaps even cycles of research categories.

To review, the goal for this chapter is to address the following question: of all the informational service tasks and processes that are currently performed by employees, which will soon be performed by customers with the help of information technology? The literature review continues below with a more detailed look at the customer and provider points of view on service process digitization.

Service innovation and the customer point of view

There are substantial streams of research focused on sendee innovation leading to new SSTs as well as on factors that enhance customer adoption of these SSTs. For instance, Meuter, Bitner, Ostrorn, and Brown (2005) distinguish between characteristics of the innovation and characteristics of the consumer, and use both to understand and predict consumer trial of an SST. There is also some extant literature on ubiquitous SSTs, describing the state that follows in the wake of near-universal customer adoption, as pictured in Figure 15.1 (see e.g., Hennig-Thurau et al. 2010). However, there is very little in the literature on continuing use of SSTs; for a notable exception, see the work of Prius, Verhoef, and Franses (2009). Of course, continuing use is typically related to customer satisfaction, and there is a growing literature on customer satisfaction with SSTs.

From the literature on service innovation, Brohman et al. (2009) make use of service dominant logic to better understand the evolution of SSTs, and go on to identify and test several principles that assist finns in improving the design of SSTs. Later work by Ordanini, Parasuraman, and Rubera (2014) takes a closer look at the specific needs of customers who wish to deal with service providers in an online environment. Such digitized sendee processes invite the customer to participate in value co-creation experiences (Prahalad & Ramaswamy 2004). An early means of assessing a customer’s willingness to do so was provided by the “Technology Readiness Index” (TRI) proposed by Parasuraman (2000). This instrument allows measurement of the challenges and frustrations faced by customers who move from receiving personal service to using SSTs. Matthing, Kristensson, Gustafsson, and Parasuraman (2006) tested the instrument among a sample of consumers, finding that it successfully identifies innovative customers who are highly creative and willing to adopt new technology. The effects of each of the dimensions of the TRI instrument (innovativeness, optimism, discomfort, and insecurity) were continued as predictors of acceptance, adoption, and usage of internet-related technologies by Lam, Chiang, and Parasuraman (2008). Parasuraman and Colby (2015) improved and updated the TRI scale by reducing the total number of items required, revising retained items for relevance (i.e., appropriateness for current technologies), and including new items to enhance the scale’s usefulness.

Some of the issues that affect customer adoption of TBSS relate to convenience (Berry, Sei- ders, & Grewal 2002) and other improvements in the service encounter experience, often based on use of SSTs (Bitner, Brown, & Meuter 2000). To assess this, Dabholkar (1996) conducted a qualitative, interview-based study of key characteristics of TBSS options, finding that required time, effort, and complexity of a digitized service process, reliability and accuracy of the outcome, and overall enjoyment influence customer perceptions of sendee quality. In addition, Dabholkar (1996, p. 35) mentions that control is “quite relevant for evaluating technology-based self-service options,” even though its importance is not clearly supported in the study.

Several authors observe that the general attitude of customers toward technology as well as situational influences can impact their likelihood of adopting TBSS (Bobbitt & Dabholkar 2001; Dabholkar 1996; Dabholkar & Bagozzi 2002; Parasuraman 2000). Furthermore, customers who desire human interaction tend to have lower likelihood than others of selecting TBSS (Bobbitt & Dabholkar 2001; Dabholkar & Bagozzi 2002; Forman & Sriram 1991); these customers are more likely to select an option that offers in-person service. Bobbitt and Dabholkar (2001) argue that a unifying theory is needed to describe how customer attitudes can be used to predict adoption of TBSS. In particular (p.424), they specify that “consumers are more likely to use technology- based self-service if it offers them a sense of control, and if they do not have to wait to use it.”

Specific influences on adoption of a TBSS are offered by Dabholkar and Bagozzi (2002), who observe that not only do individual characteristics such as novelty seeking, technolog)' self-efficacy, and low need for personal interaction enhance adoption of technology, but situational factors such as waiting time and anxiety may also. Dabholkar, Bobbitt, and Lee (2003) find that consumer differences, situational influences, and attributes of the technolog)' are all reasons that consumers do or do not adopt use of a TBSS. Other researchers suggest that customer adoption of e-services depends on such drivers as organizational reputation, relative advantage, and perceived risk (DeRuyter, Wetzels, & Kleijnen 2001).

In regard to customer satisfaction, which relates directly to continuing use of SSTs, Meuter, Ostrom, Roundtree, and Bitner (2000) examine categories of critical incidents and customer satisfaction with SSTs. Their results indicate customers will choose TBSS if it offers benefits over personal service and helps in difficult situations. Pires, Stanton, and Rita (2006) find that customers are empowered and receive greater control when using TBSS; because they have the power to choose among more available options, loyal customers are increasingly valuable to firms. Similarly, Prahalad and Ramaswamy (2004) observe that customer value is created by a personalized customer experience; thus, the interaction between firm and customer both creates and extracts value.

To ascertain customer levels of satisfaction with TBSS (specifically websites), Parasuraman, Zeithaml, and Malhotra (2005) developed the “E-S-Qual” scale, which measures customer perceptions of efficiency, fulfillment, system availability, and privacy. These authors also briefly considered service recover)' in an online environment, but very small incidences of failure led to insufficient numbers of respondents to complete the analysis. Customer satisfaction with use of SST has been investigated in specific categories of online services, including retailing (Wolfinbarger & Gilly 2003) and business-to-business services (Pujari 2004). Efficiency, reliability, and security are found to be very important to customer satisfaction, and service recovery is also a crucial issue. Other authors that looked at service recovery in TBSS situations include Dabholkar and Spaid (2012), who found that dissatisfaction owing to technology failure can be mitigated by interpersonal interaction with an employee, but that this in itself increases negative attributions to the technolog)'. In a related study, Rein- ders, Dabholkar, and Frambach (2008) observed that involuntarily moving customers from a full service situation into one where the sendee personnel are replaced by TBSS can result in negative customer attitudes.

In summary, customers are more likely to welcome an SST when it offers improved convenience, perceptions of control, and an overall better user experience. In addition, consumers who have low need for personal interaction and are comfortable with technology are more likely to adopt TBSS. They prefer a process that reduces time and effort requirements and is less complex; the service should also be enjoyable to use (possibly offering novelty) and provide a reliable and accurate outcome.

Service innovation and the firm point of view

From the sendee firm’s point of view, SST introductions are not always rosy (Bendapudi & Leone 2003; Brady, Saren, & Tzokas 2002). However, implementation of SSTs can be strategically transforming and should be carefully considered (Brodie et al. 2007). In fact, Brodie et al. (2007, p. 3) obsen'ed “a positive relationship between the intensity of e-Business adoption and firm performance.” This section of the chapter follows up by describing literature on topics that relate to pros and cons of transferring a service process to the customer in the form of an SST, from the firm point of view.

Some of the issues that must be considered in replacing customer—provider interactions with technology-based interfaces are addressed by Overby (2008). According to Overby (2008), process virtualization theory (PVT) identifies four inhibitors that negatively impact the virtualizability of service processes from the firm’s point of view: sensory requirements, relationship requirements, synchronism (i.e., immediate results) requirements, and identification and control (i.e., process involvement) requirements. Given these inhibitors that can reduce the appropriateness of SSTs for particular service tasks or processes, firms may need to provide more personal interfaces; these would allow customers to use their five senses to evaluate products and/or would facilitate interpersonal relationships for customers that prefer in-person service to technology-based interaction. Furthermore, specific timing requirements and/or the need for identification or other process inputs may affect the choice of interface.

As part of transforming service processes from provider-operated to customer-operated SSTs, it is important to identify design and functionality to maximize the increase in usage with respect to potential usage (Piccoli, Brahman, Watson, & Parasuraman 2004). Piccoli et al. (2004) developed a taxonomy that describes customer needs amenable to online fulfillment and studied the evolution of website design accordingly, noting areas for potential improvement. These authors observed that change occurs quickly, soon after a firm adopts TBSS, and slows as the technology matures, consistent with the shape of a diffusion curve approaching ubiquity. Boyer, Hallowell, and Roth (2002) provided a detailed case study illustrating this type of successful introduction of TBSS, its adoption by customers, and subsequent retention. Another article that studies managing and improving e-service quality from the firm point of view is that by Field, Heim, and Sinha (2004). These authors developed a process model for an e-service system, and examined it at the component level to identify areas where quality improvements were needed. Finally, Bauer, Falk, and Hammerschmidt (2006) examined a specific example of such an e-service system, identifying five quality dimensions critical to online shopping, including functionality/ design, enjoyment, process, reliability, and responsiveness.

Returning to the idea that it is not always appropriate for a firm to adopt SSTs or encourage their customers to engage in TBSS, Akesson and Edvardsson (2008) identified five categories of change required when certain government services move online, including the service encounter/process, customers as co-creators/sole producers of services, efficiency, integration, and complexity'. (The latter issue is discussed further in a later section of this chapter.) Barnes, Hinton, and Mieczkowska (2004) also urge caution, following their observation that a move to e-commerce can result in conflicts between the technology adopted and the management of operations. In other words, the technology may be inconsistent with the overall business strategy, which may require changes to the technology and/or redesign of operations.

Combining firm and customer points of view: co-production and the service blueprint

When considering whether to move a service process from the firm to customer use of TBSS, Chan, Yim, and Lam (2010) observed that customer participation can help and/or harm co-creation of value. This occurs due to the combination of improvements in customization and control for customers as well as additional effort required to obtain it. Building on this background, Yim, Chan, and Lam (2012) examined how customer participation in co-creation of value can be enjoyable, resulting in increased satisfaction. Mende, Scott, Bitner, and Ostrom (2017) looked at the opposite extreme: they' considered what happens when co-production is allocated to customers, in situations where they may not wish to assume the burden, and/or are ill-equipped to do so. Subsequently, they' evaluated customer response to the allocated workload, finding that it may require additional organizational support, and may' or may' not improve customer satisfaction, depending on individual differences. Finally, Rust and Huang (2014) examined how firms respond to nearly ubiquitous customer use of communication technology', finding that service becomes more personalized through use of increasingly' sophisticated analytics. Huang and Rust (2018) built on this background to enhance understanding of how this technological change leads to new definitions of work in services. Specifically, AI first replaces more analytical service provider tasks; as it improves, it is expected to begin to replace more intuitive and empathetic skills as well. Thus, as AI improves more sophisticated service tasks increasingly become candidates for migration.

Another way of looking at what occurs as technology permits customers to perfonn more service processes for themselves is to represent the changes in a stylized service blueprint, shown in Figure 15.2. In this example, tasks that move into the customer domain are relocated to positions above the line of interaction, showing that customers assume tasks previously' performed by service providers. This idea was pioneered by Letheren, Russell- Bennett, and Mulcahy' (2018). It is also of interest where the tasks are taken from when this occurs. Some of the tasks taken on by' customers might be those previously' completed directly' by' front line employees: such tasks, which were previously just below the line of interaction (and typically' visible to customers), can be more physical tasks involving tangible service processes, which are modified as such for compatibility with self-service. However, despite the presence of counterexamples, it is more common for informational tasks to be taken on by' customers following a migration to TBSS. Such tasks, typically' those which were previously below the line of internal interaction (i.e., embedded well within the firm and hidden from the viewpoint of the customer), often include those that were already digitized for employee use, which tend to be easier to migrate to TBSS than those which have not yet been virtualized.

Changes in a service blueprint as customers take over tasks using SSTs

Figure 15.2 Changes in a service blueprint as customers take over tasks using SSTs.

Figure 15.2 shows lines of interaction and internal interaction that separate tasks assigned to customers, employees, and technolog)'. Between panels (a) and (b) in the figure, these lines move such that more tasks are performed digitally by customers, whereas fewer tasks are performed by employees. In panel (a) of Figure 15.2, Time Period 1 is illustrated with a specific mixture of tasks or steps in the service process performed by employees, by customers, and by technology. In panel (b), the mixture has shifted, because in Time Period 2 some tasks previously executed by employees are executed by customers using TBSS, which may be based on a new SST or perhaps a more powerful version of a previous SST.

Campbell, Maglio, and Davis (2011, p. 184) suggest five steps involved in shifting the service boundary between provider and customer:

(1) defining value from the customer’s perspective; (2) mapping the current service boundary between provider and customer so that their respective activities are clearly laid out; (3) mapping the proposed boundary shift in which opportunities for shifting the boundary are identified and analyzed; (4) identifying the resources that are required by both the service provider and its customers to co-create value at the new boundary position; and (5) executing the service boundary shift, putting the proposed shifts into practice.

The service digitization decision

This section develops an organizational scheme linking the application of digital technology to development of SSTs. Also included is a description of how complexity underlies decisions regarding SST development.

Service process evolution

Evolution of digitized SSTs represents a subcategory of service process evolution; the more general case is illustrated in Figure 15.3. New SSTs emerge in the upper right-hand corner; this is followed by service employee processes being digitized or replaced by technology, and customers beginning to take over tasks that are digitized through TBSS. In some luxury examples, employees take over a task previously performed by digital technology; for example, a high-end hotel might have a personal assistant help a high value guest, allowing them to skip the kiosk and receive personal, human attention.

The allocation problem

One way for a firm to consider the issues involved in making the decision to digitize a particular service process and hand it off to customers for direct use is called the ‘allocation problem,’ because it is concerned with allocating tasks between machines and humans. The idea in the allocation literature is to assign tasks to humans and machines based on what ‘Men Are Best At’ and what ‘Machines Are Best At,’ otherwise known as the ‘MABA-MABA’ approach. Much of this literature is rooted in ‘Fitts’ list’; among other tasks, Fitts (1951, p. 10) listed “reasoning inductively,” “improvising,” and “using flexible procedures” as things that humans do relatively better, whereas “responding quickly to control signals” and “applying great force smoothly and precisely” were tasks listed as better suited to machines. A more recent analysis was done for the service sector by Fluang and Rust (2018), who focused on four categories of abilities (mechanical, analytical, intuitive, and empathetic) that are required to perfonn tasks. AI is viewed as taking

Categorization of service evolution paths

Figure 15.3 Categorization of service evolution paths.

over these types of tasks sequentially, beginning with those requiring mechanical abilities. Thus, these findings are consistent with those of Fitts, in that both view the decision (whether to assign a task to humans or technology) from the perspective of the abilities required to perform the task.

An organizational scheme for making SST development decisions

For the present purposes, it is useful to categorize service tasks and processes based on some information-theoretic notions. This section recommends an organizational scheme for SST design based on three basic types of information system capabilities, and on subcategories of those three capabilities. The three fundamental informational capabilities include data storage, communication, and computation/analysis, as described by Hilbert and Lopez (2011). Changes to digital SST capabilities can be classified into these three groups, providing a clear picture of how SSTs evolve over time. In the following discussion, storage and communication are combined for simplicity.

Storage and communication

To date, most SSTs have been enabled by improvements in firm abilities to store or communicate digital information. The long distance dialing SST mentioned at the beginning of the conceptual development emerged when firms created a way for customers to communicate the desired number directly to the switchboard. SSTs based on storage and communication capabilities can be thought of as ‘pass-through’ SSTs, because the firm passes access or information through to the customer and thereby substitutes customer cognitive and physical processes for (more costly) employee processes. For example, online banking became possible when financial institutions developed ways to provide stored customer financial data over the internet. Online travel sites and airline self-booking became possible when firms learned to communicate stored flight data across the internet and present it appropriately to the customer. In offline retailing, some stores use digital technolog)' to offer self-service checkout, providing customers with direct access to the information systems that underlie point-of- purchase technology such as cash registers, scales, and so forth. Online retailing goes a step further, offering customers pass-through access to see and command firm inventory and fulfillment systems. In this and similar cases, the customer acquires ‘read’ access, and even ‘write’ capability, to stored information that was previously accessible only to employees.

Another example of SSTs is based on changes in the retailing of information products. For instance, iTunes is implemented as an SST whereby music is sold directly to the customer, who then gets access to the raw data file. This abrupt change in the supply chain for music provides an example of the disruption that can be created when electronic channels are run directly from corporate storage to the consumer, allowing transfer of music, video, or books. To anticipate where new SSTs in this category might be expected, it is reasonable to look for economic sectors that still require a firm employee to access information or information systems. For instance, an airline or the post office might require employee intervention for access to lost baggage or lost package information, respectively. Other examples might include providing private, identity-related access to healthcare data or educational materials.


Compared to storage and communication, there are fewer examples of computation or processing extant in the current economy; however, improvements in computational speed, developments in AI, and the recent appearance of‘born digital’ firms imply that new developments may accelerate the pending disruption of service workplaces and markets. For the purpose of this chapter, computation is further split into two categories: signal processing and symbolic processing, the latter of which tends to emulate higher-order cognitive processes.

Signal processing

This section discusses human sensory processes and how they are incorporated into technology- based sendee provision. One type of sensory process encompasses balance, equilibrioception, and the vestibular, which are senses capable of receiving gravity, body movement, angular momentum, or acceleration information. Similar sensors are now routinely built into handheld devices that can modify the orientation of a screen depending on the angle at which it is held. Developments in this area are driving improved robotic walking gaits through outside or inside spaces. Newer industrial robots have integrated vision systems based on detection of electromagnetic radiation, allowing them to recognize objects in different orientations. Visual signal processing Is at the heart of automated systems’ abilities to read analog documents and convert them to text. Developments in automated driving, such as those being made by Google, foretell important changes to the transportation sector. Improvements in machine hearing have been rapid in recent years, leading to new fbnns and increased use of telephonic SSTs. These systems are becoming more capable and developments are changing the nature of the input to other SST systems, such as Apple’s Siri interface. Although these developments are automated, as opposed to being used by customers in the form of TBSS, the technologies involved may eventually address some of the concerns expressed by Overby (2008), in that they allow service processes having more sensory requirements to potentially be virtualized.

Although not as far along at present, new developments might be anticipated in the senses of touch, pain perception, temperature, taste, smell, echolocation, detection of polarized light, electrical current, and pressure (Clark 2013). In these examples, capabilities are apparent that are not always analogous to human capabilities. For example, long wavelength infrared detectors might notice customers in a store before a clerk does.

Symbolic processing and the meaning of complexity

As mentioned above, the second type of computation is symbolic processing, which takes place at a higher level of abstraction than does signal processing, because it uses symbols to represent information. Here, it is necessary to broach the management of SST complexity, the resulting programming cost, and the representation of such complex information in the context of the firm’s decision-making. Because of the difficulties involved in symbolic processing, it is important to comprehend the meaning of complexity.

The present usage of the term ‘complexity’ is consistent with Shostack’s notion, which is “the number and intricacy of the steps required to perform” a service process (Shostack 1987, p. 35). Shostack (1987, p. 36) also refers to “divergence,” or the “executional latitude stemming from the judgment and decisions of the individual performing the service.” Thus, divergence represents the amounts of flexibility and customization potential that are present. It is clear that divergence produces additional (hidden or stored) states in the blueprint, such as table lookup processes whereby the provider finds a good match between a customer’s taste and product feature combinations, which lead to greater complexity. (Thus, divergence is a service blueprint term used to indicate the presence of multiple possible states, which are not explicitly drawn.) However, note that complexity may occur without divergence, whereas increased divergence leads to increased complexity.

Another reason for considering the complexity involved in digitizing service processes and offering additional SSTs is proposed by Benedettini, Swank, and Neely (2017, p. 114), who argue that supporting more complex services can result in “loss of focus, complexity of coordination and potential increase in ... uncertainty.” This can impact customers and, consequently, it is important to verify trade-offs between the value to customers of additional services (such as SSTs) and their costs in terms of both investment and risk in making the decision to digitize. Issues to consider include resource consistency and cash flow' synergy over time, alongside any improvements that can be obtained in product positioning and customer satisfaction.

Complexity may also be considered from the customer point of view'. Customers prefer not to deal with increases in complexity (Rogers 1983), so if changes to a service (such as digitizing it and offering SSTs) result in greater complexity for the customer, this could make the service unattractive and actually reduce adoption. Therefore, in making the decision to digitize a service process, firms must consider any additional costs to the customer as well as their own costs. Considering the service blueprint in Figure 15.2, the discussion about complexity must evaluate the whole service, not just the part that is on the customer side of the line of interaction. Depending on how the digitization is done, it is possible that complexity for the customer may decrease even though there is more complexity overall (which must be dealt with by the firm). For instance, if switching several tasks from personal handling to use of digital technology by the employee does not involve the customer (i.e., these tasks are invisible from the customer point of view), this may increase complexity for the employee while complexity for the customer might stay the same or even decline. However, it is important to note that the complexity of digitizing those steps can influence the cost of virtualizing the service process, because the firm must then figure out how to turn the steps into an algorithm.

Finns may wish to consider which type(s) of programming complexity will be involved in digitizing a particular service process; these may be due to execution time, execution (memory) space, and/or program size required. Program size complexity may be most important to making the decision regarding shifting from personal service to an SST, because this investment must be borne by the firm up front. Because time and memory are less expensive in digital fonn than in human form, it is anticipated that, after the programming work is done, the investment can begin to pay for itself.

Digitizing a service process and the SST development decision

Clearly it is more difficult to create SSTs by digitizing complex service processes as compared to simple sendee processes. As mentioned earlier, the number of states required by a service blueprint can be used as a rough measure of the complexity of that service. One advantage of this measure is that the number of states is closely tied to the cost of programming required to digitize the service process (Binmore & Samuelson 1992). Thus, a given sendee process is more amenable to digitization when it will be repeated a large number of times as compared to a onetime event: it is not worth investing in the technology if it won’t be used repeatedly. In effect, there is a trade-off benveen costly lack of elegance or parsimony of the algorithm, and savings based on the number of times the algorithm will be repeated on behalf of the firm. However, the cost of producing elegant software can be reduced through design techniques such as recursion, modularity, and reusability. If a finn has accumulated software objects that employ such techniques and the know-how to utilize them, it may be easier to justify a more elegant digital approach. The presence of open-source software may also appear to offer opportunities to reduce cost; however, this also incurs added risk, owing to the fact that it is not adequately tested for firms that wish to utilize it as part of a product. Typically, such firms will need to invest in both customizing and testing the software; this is particularly true for unique and/or divergent service processes. One additional concern with regard to this approach is that the number of program steps required to implement a sendee process cannot be known in advance (Chaitin 1975; Sipser2006).

Anticipating applications of technology

This section uses the framework described qualitatively in the previous sections to link infonna- tion and choices regarding application of technology to development of SSTs. In particular, the chapter focuses on SSTs that are digitized sendee tasks or processes performed by customers; these tasks and processes were previously performed by employees, in either in-person or digitized forms. Thus, as new SSTs emerge, customers take over technology-based perfonnance of informational tasks and processes previously carried out by service employees. Viewed as such, this evolution to SSTs represents a subcategory of service process evolution. This is true for two reasons. First, instead of evolving to SSTs, service processes may instead evolve into being ‘automated,’ which implies having no active human intervention; therefore, automated tasks are different from SSTs, which inherently require active customer participation. Second, in yet another type of evolution, service processes may move from being completed by employees to being completed by customers in person, particularly if they require physical tasks rather than infonna- tional tasks. This chapter considers only those informational service tasks and processes performed by employees that transition to being performed by customers using SSTs. Furthermore, the scope of the chapter is limited to SSTs for informational processes that are implemented using digital technolog)'. In this situation, the term ‘TBSS’ is used to clarify that customers serve themselves using digitized or technology-based service processes.

Technology usage by the customer

Before considering a firm’s decision regarding movement of service tasks or processes to SSTs, this section provides additional detail on consumer acceptance and value placed on availability of digital self-service. Specifically, the chapter focuses on informational service processes carried out by consumers in virtual form, regardless of whether they were previously handled by service employees digitally or in person. Therefore, an evaluation of a particular task for its SST potential can be facilitated by better understanding customer needs and desires within this scope.

Success of online services, such as retailing, indicates that consumers would like to be able to obtain information, evaluate products, and place orders online. However, they may not believe that they will be able to accomplish the full range of tasks in a service process virtually. For instance, Overby (2008) indicates that consumers may require a human interface when they need to use their five senses to evaluate products and/or when they wish to interact in person with a front line service provider. In addition, specific timing requirements and/or the need for identification or other process inputs may affect consumer preference for a human or virtual interface. Bobbitt and Dabholkar (2001) provide consistent results, observing that online services may be selected over in-person services due to perceptions of waiting time (i.e., synchronism), fun, and ease of use (i.e., process involvement). In addition, Dabholkar (1996) found that consumers prefer an interface offering greater ease of use, enjoyment, and control, all of which are increasingly available virtually. Consistent with this, Moon, Kim, Choi, and Sung (2013) observed that online shoppers can meet needs for personal interaction by engaging socially with an avatar. This is particularly true as a virtual interface becomes more anthropomorphic (i.e., human-like), according to Moon (2000).

In a different type of study focusing on consumer preference for SSTs in evaluating and controlling electricity usage, Russell-Bennett et al. (2017) found that customers prefer to use an interface that offers greater functional value, ease of use, convenience of on-demand availability, fun, and reward programs. In summary, consumers wish to obtain value, but they do not want to deal with added complexity to obtain it; they also want ease of use and convenience. Thus, across a range of industries, consumers have been found to prefer an SST when it offers functional value (i.e., ability to accomplish tasks, convenience, and ease of use), hedonic value (i.e., fun and enjoyment), and control, without increasing the difficulty of use.

In summary, from the consumer point of view, a virtual interface or SST is preferred when it is not necessary to use five senses to evaluate products, when a personal relationship is not needed, if specific timing requirements are important, and when greater control is perceived over the process and/or outcome. Furthermore, not only do consumers want functionality, convenience, and control, they want these advantages without incurring costs (such as increased complexity or difficulty of use), and they want additional fun and enjoyment. Thus, there is a high bar to overcome when converting infonuational tasks previously performed by service employees into SSTs.

Technology development by the firm

Predictions regarding future task and process migration

Which service processes and tasks currently performed by employees are firms more likely to migrate to SSTs? The characteristics and variables that drive these decisions may be small and seemingly minor, such as issues related to specific difficulties of creating the software for an SST, or as large as overarching concerns of senior executives, possibly related to external issues, or to the size and scope of the conversion project. It is important to work through the anticipated computational (and especially symbolic) processing aspects of the service process that will need to be implemented in digital form to create the SST. Perhaps, even more importantly, qualitative comments should be obtained regarding how to assess influences on migration of informational sendee processes from employees to customer-interfacing SSTs. Current innovations in Al focus on perception, reasoning, and inference. Therefore, a new wave of digitized processes is anticipated that will disrupt the service sector, as many informational tasks (e.g., computationally difficult verbal tasks) are moved to SSTs. This section contains thoughts related to these topics.

Identifying tasks to digitize

The allocation literature reflects on whether tasks should be automated or handled in person; one potentially useful contribution in this literature is Fitts’ (1951) list. Specifically, this list suggests that tasks requiring inductive reasoning, flexible procedures, and use of improvisation and judgment are better allocated to humans, whereas tasks requiring response to control signals, performance of repetitive tasks, and use of deductive reasoning are best handled by machines. Therefore, because the focus of this chapter is on which tasks can be handed off to customers in a digitized form for use in TBSS, this literature is helpful. It can assist in advising firms as to which service tasks (and entire processes) previously performed by employees will add the most value and successfully migrate to SSTs. Those tasks made available through TBSS will include non-repetitive tasks requiring the ability to be flexible and think quickly, using inductive reasoning and judgment, because they will be operated by customers. On the other hand, repetitive tasks that require response to control signals and deductive reasoning may be digitized in order to be automated.

As mentioned earlier, PVT (Overby 200B) recommends that finns considering virtualizing service tasks or processes identify the potential impact of sensor)', relationship, synchronism, and iden- tification/control requirements. If customers will be able to use their five senses to evaluate products, form interpersonal relationships, meet specific timing requirements, and provide identity and/or other process inputs as needed, then the task(s) may be candidates for digitizing and providing in SST fbnn. Because of innovations in AI, it is becoming possible to address an increasing array of sensor)' requirements, so it is anticipated that more sendee tasks and processes should be candidates for virtualization. The changing trade-off will increasingly rely on having good potential for signal processing capacity improvements, which will make them attractive to migrate to SSTs. AI is also expected to facilitate an increased ability to meet requirements related to relationships, timing, and identification/control, which will allow additional tasks to migrate to SSTs.

Considerations related to creating software for task digitization

The decision to virtualize an informational service process to obtain an SST for use by customers is not a simple decision. After considering the needs of the customers and the costs and benefits to the firm, it is also important to evaluate its potential for representation (i.e., the ability to present relevant information in a virtual form), reach (i.e., the ability to serve customers across time and space), and monitoring capability (i.e., the ability to authenticate participants and track activity), according to Overby (2008).

Because of the need to develop software representing service tasks and processes, the firm must commit to and then manage these projects, including such mundane aspects as tracking progress, collating modules for repeated use, and observing change over time in the tasks taken on by outsourced software companies (or in-house programming departments). Because of modularity, software has become an asset that can be deployed in the front and/ or back offices. Thus, there is a mix of administrative and production costs involved in developing and using software, which change as task complexity increases. Firms must assess the costs of complexity that accrue in production of software, and use this information in making subsequent digitization decisions. For this reason, Pekkarinen and Ulkuniemi (2008) observe that some organizational aspects of services themselves are increasingly similar to organizing software projects (even to the terminology such as ‘modularity'’).

Specific issues meriting consideration in future research that investigates firm choice (as to when to introduce an SST to replace an employee—customer interaction) include the following.

  • 1 The more times the same employee action will be repeated, the better the prospects for conversion to an SST. The ‘same action’ refers to actions that share identical service blueprint tasks or processes. An action might be repeated within-customer, as in the case that the customer’s preferences might lead to a similar action, or between-customer, as in the case that there is modest or no heterogeneity of customer preferences.
  • 2 The higher the labor cost per service process, the better the prospects for digitizing and converting to TBSS. Specifically, assuming that labor markets are reasonably efficient, firms pay employees when they cannot practically replace them with digitized customer interfaces. The higher the unit labor cost, the more motivated the firm is to try to replace each employee with less expensive software.
  • 3 Because programming costs are typically related to the complexity of the service task or process, the simpler that is, the better the opportunity' for digitizing. The number of service blueprint activities that describe the task or process can be used as a rough guideline forjudging the complexity of the interaction.

In summary, issues related to a firm’s decision to build the software for migrating informational tasks front being carried out by employees to being offered in digitized SST form fall into three categories. First, the task type must be evaluated, because more routine tasks need not be handled by either employees or customers and may be automated. If a particular task is not folly automated (i.e., it requires human input), then it may be a candidate to move from (in-person or digital) employee effort to SST fonn, to be handled virtually by customers using TBSS. Second, the task should be evaluated to detennine whether or not it will be possible to meet sensor)', relationship, synchronism, and identification/control requirements if it is digitized. Finally, it will be important to assess whether the task can be digitized cost-effectively. This means evaluating how frequently the task is repeated, the cost of using employees to handle the task, and programming costs, which are related to task complexity.


This chapter, infonned by literature and offering a qualitative framework for fmn decisionmaking, develops a basis for thought regarding which infonnational service tasks and processes will migrate from employee handling to customer handling. Two situations are within the scope of the chapter. The first is a task or process that is performed by employees in person, and is digitized to hand off to customers in a virtual fonnat. The second type of task or process has already been digitized for employee use; therefore, fewer changes are required to hand off to customers in the fonn of SSTs. By offering the service tasks to customers to carry out using TBSS, it is important to note that the tasks are not being automated: automation refers to tasks that are perfonned without human intervention. Thus, the chapter offers some thoughts on future investments in SSTs and conversion decisions that allow TBSS.

Consumers are increasingly willing to use SSTs to carry out their own informational service processes. For instance, airport kiosks for self-check-in, ATMs for self-service financial transactions, and online restaurant reservations all require that the consumer adds (primarily) digital input to a specific virtual service system. The general category of e-tail self-service technolog)' substitutes consumer labor for front line service provider labor. Thinking about websites like WebMD and TurboTax makes it clear that technology can already, at least to some degree, substitute for professional services as well. Online financial calculators, calendars that remind the user of important meetings, and book recommendation systems are other examples of digitized services. Mobile devices offer electronic payment (making the bank teller redundant) and travel mapping (making a guide redundant). In fact, a mobile mapping service, as used with a GPS device or smartphone, is technically a new e-service, because the role of guide is particular to the tourism context, and travel routing is useful in a variety' of contexts.

The focus in this chapter is on information-based services; the primary goal is to assess which types of these services are most likely to move from provider deliver)' to consumer effort, implemented via SSTs. Several characteristics have been identified that appear to increase the desirability' of a particular service to move to TBSS in the near future. The chapter also details issues from both the customer and firm points of view to assess the decision from both sides. From the consumer point of view, use of TBSS is more attractive if it is not necessary to use the five senses to evaluate products, when a personal relationship is not needed, if specific timing requirements are important, and when greater personal control is perceived. Thus, consumers want greater functionality and convenience, without having to deal with increased complexity or difficulty' of use.

From the firm point of view, some of the concerns are similar. For instance, for an infonnational service process to be made available to customers using an SST, it must be evaluated to ensure that it will be possible to meet sensory, relationship, synchronism, and identification/control requirements. In addition, fimis must determine that the task can be digitized cost-effectively, which involves assessing how frequently the task is repeated, the cost of using employees rather than customers to handle the task, and programming costs (which are related to task complexity).

Predicting future adoption potential of SSTs would be useful in identifying investment opportunities for service businesses. However, more germane to the present purpose is the fact that within firms the marketing and IT functions are most involved in developing and managing SSTs and have an inherent interest in understanding their evolution (Bridges & Hofacker 2016). Both disciplines can benefit from a positive theory that anticipates the SST cycle as illustrated in Figure 15.3 and which therefore facilitates strategic thinking. For academics in business disciplines, the positive theory suggested in this chapter provides important future research topics, and perhaps even cycles of research categories.

This chapter has described a qualitative framework to evaluate which infonnational service tasks and processes that are currently perfbnned by service employees (either in person or using a virtual interface) will soon be performed by customers with the help of infonnation technology. A number of issues have been identified that are pertinent to firm decisions to move these tasks into TBSS contexts. Future research should address creation of metrics to assess the qualitative issues that are identified here. In addition, it would be helpful to better understand consumer preferences regarding features as well as situational usage of virtual and in-person interfaces.


Akesson, M. & Edvardsson, B. (2008). “Effects of e-Government on Service Design as Perceived by Employees,” Managing Service Quality, 18(5), 457-478.

Barnes, D., Hinton, M. & Mieczkowska, S. (2004). “The Strategic Management of Operations in E-Business,” Production Planning & Control, 15(5), 484—494.

Bauer, H. H., Falk, T. & Hammerschmidt, M. (2006). “EeeTransQual: A Transaction Process-Based Approach for Capturing Service Quality in Online Shopping”, Journal of Business Research, 59(7), 866—875.

Bendapudi, N. & Leone, R. P. (2003). “Psychological Implications of Customer Participation in Co-Production,” Journal of Marketing, 67(1), 14—28.

Benedettini, O., Swink, M. & Neely, A. (2017). “Examining the Influence of Service Additions on Manufacturing Firms' Bankruptcy Likelihood,” Industrial Marketing Management, 60, 112—125.

Berry, L. L., Seiders, K. & Grewal, D. (2002). “Understanding Service Convenience,” Journal of Marketing, 66(3), 1—17.

Binmore, Kenneth G. & Samuelson, L. (1992). “Evolutionary Stability in Repeated Games Played by Finite Automata,” Journal of Economic Theory, 57(2), 278—305.

Bitner, M. J., Brown, S. W. & Meuter, M. L. (2000). “Technology Infusion in Service Encounters,” Journal of the Academy of Marketing Science, 28(1), 138—149.

Bobbitt, M. L., & Dabholkar, P. A. (2001). “Integrating Attitudinal Theories to Understand and Predict Use of Technology-Based Self-Service: The Internet as an Illustration,” International Journal of Service Industry Management, 12(5), 423—450.

Boyer, К. K., Hallowell, R. & Roth, A. V. (2002). “E-services: Operating Strategy: A Case Study and a Method for Analyzing Operational Benefits,” Journal of Operations Management, 20(2), 175—188.

Brady, M., Saren, M. & Tzokas, N. (2002). “Integrating Infonnation Technology into Marketing Practice: The IT Reality of Contemporary Marketing Practice,"Journal of Marketing Management, 18(5-6), 555—577.

Bridges, E., & Hofacker, C. F. (2016). “Service Marketing and Adoption of Promotional Technology: A Qualitative Study,” Service Science, 8(4), 368—385.

Brodie, R. J., Winklhofer, H. M., Coviello, N. E. & Johnston, W. J. (2007). “Is E-Marketing Coming of Age? An Examination of the Penetration of E-Marketing and Finn Performance,” Journal of Interactive Marketing, 21(1), 3—31.

Brohman, К. M., Piccoli, K., Martin, P., Zulkemine, F., Parasuraman, A., & Watson, R. T. (2009). “A Design Theory Approach to Building Strategic Network-Based Customer Service Systems,” Decision Sciences, 40(3), 403-430.

Campbell, C. S, Maglio, P. P., & Davis, M. M. (2011). “From Self-Service to Super-Service: A Resource Mapping Framework for Co-Creating Value by Shifting the Boundary between Provider and Customer,” Information Systems and E-Business Management, 9(2), 173—191.

Chaitin, G. J. (1975). “A Theory of Program Size Formally Identical to Information Theory,” Journal of the Association for Computing Machinery, 22(3), 329—340.

Chan, K. W., Yim, С. K., & Lam, S. S. K. (2010). “Is Customer Participation in Value Creation a Double-Edged Sword? Evidence from Professional Financial Services across Cultures,’’Journal of Marketing, 74(3), 48-64.

Clark, L. (2013). Study: Echolocation algorithm maps cathedral in 3D to the millimetre,” Wired Magazine, visited at www.wired.co.uk/article/echolocation-app, last visited October 30, 2018.

Dabholkar, P. A. (1996). “Consumer Evaluations of New Technology-Based Self-Service Options: An Investigation ot Alternative Models of Service Quality,” International Journal of Research in Marketing, 13(1), 29-51.

Dabholkar, P. A. & Spaid, В. 1. (2012). “Service Failure and Recovery in Using Technology-Based Self-Service: Effects on User Attributions and Satisfaction,” Sendee Industries Journal, 32(9), 1415—1432.

Dabholkar, P. A., Bobbitt, L. M., & Lee, E-J. (2003). “Understanding Consumer Motivation and Behavior Related to Self-scanning in Retailing: Implications for Strategy and Research on Technology-Based Self-Service,” International Journal of Service Industry Management, 14(1), 59—95.

Dabholkar, P. A., & Bagozzi, R. P. (2002). “An Attitudinal Model of Technology-Based Self-Service: Moderating Effects of Consumer Traits and Situational Factors,” Journal of the Academy of Marketing Science, 30(3), 184-201.

DeRuyter, K., Wetzels, M., & Kleijnen, M. (2001). “Customer Adoption of E-Service: An Experimental Study,” International Journal of Service Industry Management, 12(2), 184—207.

Field, J. M., Heim, G. R., & Sinha, К. K. (2004). “Managing Quality in the e-Service System: Development and Application of a Process Model,” Production and Operations Management, 13(4), 291—306.

Fitts, P. M. (1951). Human engineering for an effective air-navigation and traffic-control system. Washington, DC: National Research Council.

Forman, A. M., & Sriram, V. (1991). “The De-Personalization of Retailing: Its Impact on the ‘Lonely’ Consumer, "Journal of Retailing, 67(2), 226—243.

Hennig-Thurau, T., Malthouse, E. C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., & Skiera, B. (2010). “The Impact of New Media on Customer Relationships,” Journal of Service Research, 13(3), 311-330.

Hilbert, M. (2014a). “Technological Information Inequality as an Incessantly Moving Target: The Redistribution ot Information and Communication Capacities Between 1986 and 2010,” Journal of the Association for Information Science & Technology, 65(4), 821—835.

Hilbert, M. (2014b). “How Much of the Global Information and Communication Explosion Is Driven by More, and How Much by Better Technology?” Journal of the Association for Information Science & Technology, 65(4), 856—861.

Hilbert, M. & Lopez, P. (2011). “The World’s Technological Capacity to Store, Communicate, and Compute Information,” Science, 6018, 692—693.

Huang, M-H., & Rust, R. T. (2018). “Artificial Intelligence in Service,” Journal of Service Research, 21(2), 155-172.

Lam, S. Y., Chiang, J., & Parasuraman, A. (2008). “The Effects of the Dimensions ofTechnology Readiness on Technology Acceptance: An Empirical Analysis, "Journal of Interactive Marketing, 22(4), 19-39.

Letheren, K., Russell-Bennett, R. & Mulcahy, R. (2018). Making the invisible, visible: Humanised technology for low-contact services, Paper presented at Frontiers in Service, Austin, TX, September 6-9, 2018.

Lovelock, С. H. (1983). “Classifying Services to Gain Strategic Marketing Insights,” Journal of Marketing, 47(3), 9-20.

Matthing, J., Kristensson, P. Gustafsson, A. & Parasuraman, A. (2006). “Developing Successful Technology-Based Services: The Issue of Identifying and Involving Innovative Users,” Journal of Services Marketing, 20(5), 288-297.

Mende, M., Scott, M. L. Bitner, M. J„ & Ostrom, A. L. (2017). “Activating Consumers for Better Service Coproduction Outcomes Through Eustress: The Interplay of Finn-Assigned Workload, Service Literacy, and Organizational Support,” Journal oj Public Poliq’ & Marketing, 36(1), 137—155.

Meuter, M. L., Bitner, M. J. Ostrom, A. L., & Brown, S. W. (2005). “Choosing among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies,” Journal of Marketing, 69(2), 61—83.

Meuter, M. L., Ostrom, A. L. Roundtree, R. I., & Bitner, M. J. (2000). “Self-Service Technologies: Understanding Customer Satisfaction with Technology-Based Service Encounters,” Journal of Marketing, 64(3), 50-64.

Moon.J. H., Kim, E., Choi, S. M., & Sung, Y. (201.3). “Keep the Social in Social Media: The Role ofSocial Interaction in Avatar-Based Virtual Shopping,"Journal of Interactive Advertising, 13(1), 14—26.

Moon, Y. (2000). “Intimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers,” Journal of Consumer Research, 26(4), 323—339.

Ordanini, A., Parasuraman, A., & Rubera, R. (2014). “When the Recipe Is More Important Than the Ingredients: A Qualitative Comparative Analysis (QCA) of Service Innovation Configurations,” Journal of Service Research, 17(2), 1.34—149.

Overby, E. (2008). “Process Virtualization Theory and the Impact of Information Technology,” Organization Science, 19(2), 277—291.

Parasuraman, A. (2000). “Technology Readiness Index (TRI): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies,"Journal of Service Research, 2(4), 307—320.

Parasuraman, A. & Colby, C. L. (2015). “An Updated and Streamlined Technology Readiness Index: TRI 2.0,” Journal of Service Research, 18(1), 59-74.

Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). “A Multiple-Item Scale for Assessing Electronic Service Quality, ” Journal of Service Research, 7(3), 213—233.

Pekkarinen, S. & Ulkuniemi, P. (2008). “Modularity in Developing Business Services by Platform Approach,” International Journal of Logistics Management, 19(1), 84-103.

Piccoli, G., Brohman, M. K., Watson, R. T. & Parasuraman, A. (2004). “Net-Based Customer Service Systems: Evolution and Revolution in Web Site Functionalities,” Decision Sciences, 35(3), 423—455.

Pires, G. D., Stanton, J. & Rita, P. (2006). “Commentary: The Internet, Consumer Empowerment, and Marketing Strategies,” European Journal of Marketing, 40(9/10), 936—949.

Prahalad, С. K. & Ramaswamy, V. (2004). “Co-Creation Experiences: The Next Practice in Value Creation,” Journal of Interactive Marketing, 18(3), 6—14.

Prins, R., Verhoef, P. C. & Franses, P. H. (2009). “The Impact of Adoption Timing on New Service Usage and Early Disadoption,” International Journal of Research in Marketing, 26(4), 304—313.

Pujari, D. (2004). “Self-Service with a Smile? Self-Service Technology (SST) Encounters among Canadian Business-to-Business,” International Journal of Service Industry Management, 15(2), 200—219.

Reinders, M. J., Dabholkar, P. A., & Frambach, R. T. (2008). “Consequences of Forcing Consumers to Use Technology-Based Self-Service,” Journal of Service Research, 11(2), 107-123.

Rogers, E. M. (198.3). Diffusion of innovations. New York: Free Press.

Russell-Bennett, R., Mulcahy, R., McAndrew, R., Letheren, K., Swinton, T., Ossington, R., & Horrocks, N. (2017). Taking advantage of electricity pricing signals in the digital age: Householders have their say. A summary report, Brisbane: Queensland University of Technology, 1—98.

Rust, R. T. & Huang, M-H. (2014). “The Service Revolution and the Transformation of Marketing Science,” Marketing Science, 33(2), 206—221.

Shostack, L. G. (1987). “Service Positioning through Structural Change,"Journal of Marketing, 51(1), 34—43.

Sipser, M. (2006). Introduction to the theory of computation (Second ed.). Boston, MA: Thomson Course T echnology.

Wolfmbarger, M. & Gilly, M. C. (2003). “EEeTailQ: Dimensionalizing, Measuring and Predicting Etail Quality,"Journal of Retailing, 79(3), 183-198.

Yim, С. K., Chan, K. W„ & Lam, S. S. K. (2012). “Do Customers and Employees Enjoy Service Participation? Synergistic Effects ofSelf- and Other-Efficacy,"Journal of Marketing, 76(6), 121-140.

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