Taking a BPM Lifecycle View on Service Productivity - RESER

Taking a BPM Lifecycle View on Service Productivity: Results from a Literature Analysis Jörg Becker, Björn Niehaves, Andrea Malsbender, Kevin Ortbach, Ralf Plattfaut, Jens Pöppelbuß European Research Center for Information Systems, University of Münster, Leonardo-Campus 3, 48149 Münster, Germany, While the service sector contributes the major share to the gross domestic product (GDP) in most developed economies, it has a significantly lower productivity level than other sectors. We take a Business Process Management (BPM) perspective and conduct a literature review based on 31 articles from service and IS literature. Here, we conceptualize BPM as a dynamic capability. From the literature review, 10 recommendations for increasing service productivity are derived and assigned to the abilities of sensing, seizing and transformation as proposed by dynamic capability theory. The results of the analysis are then used to identify three particularly relevant areas of future research.

1.

Introduction

Over the past 50 years, the service sector has grown remarkably and now dominates economic activity in many parts of the world. Services are ubiquitous and represent the major share of the gross domestic product (GDP) and total employment in most advanced economies (Katzan 2008; Spohrer & Riecken 2006). The service sector includes a broad variety of different services like government, health care, finance, transportation, communications and many more (Chesbrough & Spohrer 2006). The increasing economic relevance of services feeds a growing academic interest in understanding how to achieve superior service performance (Spohrer et al. 2007). Following recent publications, a service can be understood as a process (Katzan 2008), since it transfers inputs into outputs. It is an interaction between customer and provider that creates and captures value. Thus, the notions of service and process cannot be separated. A process-oriented view on services thus seems a valuable approach for understanding and improving service performance (Armistead & Machin 1998; Grönroos & Ojasalo 2004; McLaughlin & Coffey 1990). Research generally distinguishes two ways of measuring the performance of service processes. Service performance can be addressed in terms of either service quality or service productivity. Service quality is a measure of value to the customer, related to the requirements and utility function of the customer (Hsu & Spohrer 2009). Service quality is highly subjective; it is typically defined as the difference between the expected and perceived outcome of service (Glushko & Tabas 2009). In contrast, service productivity is a measure of value to providers and relates to their profit (Hsu & Spohrer 2009). “Productivity measures express relationships between the outcomes or outputs of service processes and the resources or inputs required to operate them” (McLaughlin & Coffey 1990). The concept of productivity is deeply rooted and well understood in the context of manufacturing (Vuorinen et al. 1998). However, 1

with regard to services, research and practice still lack appropriate instruments to measure and manage productivity (Grönroos & Ojasalo 2004). To understand mechanisms to increase service productivity an interdisciplinary approach is needed. It is considered unlikely “that systematic approaches to service innovation can be achieved without an interdisciplinary effort that unites academic silos around a common set of problems” (Chesbrough & Spohrer 2006). With respect to this, we approach service management from a process-oriented perspective. Business processes are the interface between organization and technology (Van Der Aalst et al. 2003) and, thus, Business Process Management (BPM) is a central aspect to the Information Systems (IS) discipline. Furthermore, BPM has already been successfully applied to improve productivity of service processes (Armistead & Machin 1998; Küng & Hagen 2005). However, the general concept of BPM has not yet been put on a solid theoretical basis (Trkman 2010; Smart et al. 2009). To close this gap, we present our understanding of BPM based on dynamic capability theory. We then set out to identify recommended practices that can be used to improve the productivity of service processes. Therefore, our main research contributions within this paper are: 1) To conceptualize and discuss BPM as a dynamic capability and 2) To identify practices to increase service productivity. The second objective will be addressed by a literature review that is based on high quality publications gathered from both service and IS research. We look for recommendations and routines as well as tool support that can leverage improvements in service productivity. We also aim at the identification of unsolved practical problems that would benefit from thorough examination in BPM and IS research. These problems provide promising aspects of future research to be tackled within the interdisciplinary Service Science (Chesbrough & Spohrer 2006). The remainder of this article is structured as follows: In section 2, related work is presented to achieve a common understanding of the terms service, service productivity, BPM and dynamic capabilities. Furthermore, the conceptualization of BPM as dynamic capability is introduced and validated with help of BPM lifecycle research. The developed framework will then be used to structure the following literature analysis. The review methodology is subject to section 3. The main analysis is presented in section 4 where practices to improve service productivity are compiled from the analyzed literature. Section 5 discusses implications that result from our literature review. The article concludes with section 6, giving a brief summary and outlook.

2.

Theoretical Background

2.1.

Services and Service Productivity

A service is commonly defined as “a time-perishable, intangible experience performed for a client who is acting as a coproducer to transform a state of the client” (Spohrer & Maglio 2008, p. 240). It is the application of competences for the benefit of another (Vargo & Lusch 2004). The customer owns or controls inputs that the service provider is responsible for transforming according to mutual agreement (Spohrer 2

et al. 2007). Services are intangible, i.e., the results of service activity are intangible in contrast to manufacturing where tangible artifacts are produced (Katzan 2008). In addition, services are perishable as they cannot be inventoried like products and thus perish when remaining unused (Katzan 2008). Furthermore, services are produced and consumed simultaneously. Service delivery is thus inseparable from service consumption. Finally, services are heterogeneous, since they tend to differ in the nature of delivery from time to time. Service processes may vary significantly depending on customer needs and customer input (Katzan 2008; Klassen et al. 1998). Spohrer and his co-authors (Spohrer et al. 2007; Hsu & Spohrer 2009; Chesbrough & Spohrer 2006) define services to be the co-creation of value between service systems. Service systems are dynamic configurations of resources including people, technology, internal and external service systems and shared information (such as language, processes, metrics, prices, policies and laws) (Spohrer et al. 2007). The resources are the infrastructure and facilities necessary to support the service process (Katzan 2008). Service systems have an internal structure (intra-entity services) and external structure (inter-entity services) in which value is co-produced with other service systems (Spohrer et al. 2007). Examples for service systems are provider and customer, but also individuals, families, nations, and economies. According to Katzan (2008), service systems are open systems because they require the exchange with their environment to survive. In this context, improving service productivity has become a central theme among managers. However, there is no widely accepted definition of service productivity (Spohrer et al. 2007). Actually, the concept of productivity is rooted in the context of manufacturing. However, the relevance of managing productivity is also widely accepted in the service sector (Vuorinen et al. 1998). Traditionally, productivity is defined as the ratio of output value to its related input (Armistead & Machin 1998; Filiatrault et al. 1996; Vuorinen et al. 1998) with the additional condition of constant output quality (Grönroos & Ojasalo 2004). Due to the industrial origin of this ratio, the primary elements used as inputs are labor and capital – more precisely constituents of materials, plant, and resources. Measuring the output given a constant quality is possible by terms of volume, weight, or quantity (Armistead et al. 1993). The adoption of productivity to the service sector has been discussed for decades (McLaughlin & Coffey 1990; Buntz 1981) and there still seems to be the need to develop new instruments to measure service productivity (Grönroos & Ojasalo 2004). Illustrating the controversial views on this topic, C. R. Martin et al. (2001, p. 155) state that “proposing a single, quantitative technique to measure service productivity with any degree of objectivity, consistency, and usefulness will likely generate heated discussion among economists, marketers, policy makers, operation managers, and assorted academicians.” Grönroos and Ojasalo (2004) conceptualize service productivity as a function consisting of internal efficiency, external efficiency, and capacity efficiency. Other approaches for productivity measurement can also be found in the literature and will also be part of our review (McLaughlin & Coffey 1990; Vuorinen et al. 1998). All conceptualizations show that measuring service productivity is much more complicated than measuring manufacturing productivity. One major reason is that the assumption of constant quality does not apply to services (Grönroos & Ojasalo 2004).

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2.2.

Business Process Management

The notion of BPM has been primarily influenced by the concepts of Business Process Reengineering (BPR) and Total Quality Management (TQM). TQM can be considered an incremental and evolutionary approach focusing on continuous improvement. In its core, BPR incorporates a revolutionary approach to redesigning the organizational structure and processes of a company in a one-time undertaking (Davenport 1993; Davenport & Short 1990; Hammer & Champy 1993; Hammer 1990). While several studies have shown the significant impact of this approach on both theory and practice (Recker et al. 2010; Case 1999; Al-Mashari & Zairi 2000), especially its rather radical nature has been criticized by various empirical researchers. They reported that some companies had been more successful in taking an incremental approach to process improvement (Davenport & Stoddard 1994; de Cock & Hipkin 1997; Harkness et al. 1996; Stoddard & Jarvenpaa 1995). Hence, there is a broad agreement among various authors that neither BPR nor TQM are consummate concepts but rather have to be seen as complementary, constituting elements within the process-orientation of a company (Kettinger et al. 1997; H. Smith & Fingar 2006; Zairi 1997; Zairi & Sinclair 1995). Thus, we define BPM according to Ross and Perry (1999) as an integrated management philosophy and set of practices that includes both incremental and radical change in business processes. While the term BPM is omnipresent in literature, its constituting elements differ significantly among publications. Adopting Kettinger’s and Teng’s (1997) perspective on BPR, it can be recognized that BPM “[…] is not a monolithic concept but rather a continuum of approaches to process change”. However, there is broad consensus that BPM involves revolving activities aiming at the change of organizational procedures. This central idea of BPM as an ever-ongoing endeavor is at best reflected by the concept of lifecycle models. In this context, a multitude of business process lifecycle models have been proposed (Neumann et al. 2011; Scheer et al. 2005; Zur Muehlen 2004; Van Der Aalst et al. 2007; Van Der Aalst et al. 2003). In this article we follow the depiction of a coarse-grained lifecycle as presented by Van der Aalst et al. (2003) in order to structure our literature analysis. It encompasses four phases: design, configuration, enactment, and diagnosis. In the diagnosis phase, the operational processes are analyzed in order to identify problems and opportunities for improvement. The ideas generated here will then be used within the design phase where different process alternatives are derived and elaborated. Here, the designer aims at both the elimination of diagnosed and potential weaknesses and the fulfillment of identified improvement opportunities. Thus, the process alternatives have to be evaluated which may also include process simulation. In the configuration phase, the selected process alternative is configured for usage. This can involve the configuration of processes to be supported by workflow management systems, the configuration for organizational deployment, or the preparation for different specific instantiations of the process. Once the process is specified, it can be executed (enactment phase). Possible errors and misfits within the process execution will then be revealed by another ongoing diagnosis.

2.3.

Dynamic Capability View

According to the Resource-Based View (RBV) of the firm organizations can be seen as collections of distinct resources (Wernerfelt 1984; Wade & Hulland 2004; Penrose 4

1959). The term RBV was coined by Wernerfeldt (1984) and builds upon earlier work (Learned et al. 1969; Penrose, 1959). The RBV is widely and increasingly used in the IS domain to explain how information systems relate to the strategy and performance of an organization (Wade & Hulland 2004). In this context, resources are most commonly framed as “anything which could be thought of as a strength or weakness of a given firm” (Wernerfelt 1984, p. 172). Following Wade and Hulland, we understand resources as the more general term comprising both assets and capabilities. Assets are defined as anything tangible or intangible that can be used by an organization (Wade & Hulland 2004). In contrast, capabilities refer to the ability of an organization to perform a coordinated set of tasks for the purpose of achieving a particular end result which reflects the common definition of a process (Helfat & Peteraf 2003). Hence, we understand capabilities as repeatable patterns of action that utilize assets as input (Amit & Schoemaker 1993; Helfat & Peteraf 2003; Wade & Hulland 2004). Thus, both capabilities and assets are important determinants of a company’s competitive advantage. However, sustained competitive advantage cannot be achieved with a static resource configuration. Many authors stress the importance of fit between resource configuration and market dynamics. For example, Collis identifies several reasons why a competitive advantage generated by a particular resource may vanish over time: “erosion of the capability as the firm adapts to external or competitive changes; replacement by a different capability; and being surpassed by a better capability” (Collis 1994, p. 147). Eisenhardt & Martin (2000) argue that long-term competitive advantage does not lie in stable resource configurations, but in the ability of a firm to adapt these to the market environment. This is especially important in highly dynamic market environments (Teece et al. 1997). Hence, organizations need capabilities that enable them to adapt their resource configuration to changing settings. These capabilities are commonly referred to as dynamic capabilities (Zollo & Winter 2002; Koch 2010; Eisenhardt & J. A. Martin 2000; Teece et al. 1997). As such, scholars have differentiated two types of capabilities from one another: First, the basic functional activities of organizations are called operational capabilities. Such capabilities are, e.g., plant layout, distribution logistics, or marketing campaigns (Collis 1994). They are needed for the operational functioning of the organizations and relate closely to the original conceptualization of capabilities as given in the RBV literature (Zollo & Winter 2002). With relation to the understanding of operational capabilities as the ability to perform a coordinated set of tasks for the purpose of the operational functioning of the organization (Zollo & Winter 2002; Winter 2003; Helfat & Peteraf 2003) we understand the execution of business processes as an operational capability. Second, Teece et al. (Teece et al. 1997) introduced dynamic capabilities as the abilities of an organization to integrate, build, and reconfigure operational capabilities as well as external competences to address rapidly changing environments. Other scholars build on this conceptualization and argue that dynamic capabilities are “a learned and stable pattern of collective activity through which the organization systematically generates and modifies its operating routines in pursuit of improved effectiveness” (Zollo & Winter 2002, p. 340). Based on these arguments, in this paper we will understand Dynamic Capabilities as the firm’s ability to integrate, build, and reconfigure operational capabilities for the purpose achieving a fit with the market environment. Building upon the understanding of process execution as an operational capability we can thus understand BPM as a dynamic capability (Niehaves et al. 2010). 5

Scholars have argued that each dynamic capability consists of sensing, seizing, and transformation activities (Teece et al. 1997; Teece 2009). In our context of BPM, sensing refers to the identification of needs and opportunities to change an organization’s business processes. Seizing refers to the development of process alternatives that address the identified needs and opportunities as well as to the selection of one particular alternative. At last, transformation is concerned with the socio-technically implementation of changed business processes in the organization. In line with this argumentation, the dynamic capability perspective can be applied to BPM research in general and the previously outlined BPM lifecycle model in particular (Figure 1).

Figure 1 Dynamic Capability View on BPM Lifecycle

Here, a general differentiation can be made between the dynamic and the operational aspects of the lifecycle. While analysis, design and configuration abilities are deployed in order to change specific procedures within the company, enactment refers to the actual execution of these processes. In literature, especially this connectedness to change has been outlined as a major characteristic of dynamic capabilities (Eisenhardt & J. A. Martin 2000; Galunic & Eisenhardt 2001; Savory 2006; Teece et al. 1997). In addition, the activities of sensing, seizing and transformation as described within dynamic capability research can be matched to certain aspects of the BPM lifecycle. First, analysis – i.e. the discovery of inefficiencies and opportunities within the existing processes – can be understood as an activity within sensing. Second, the definition of the design phase as being concerned with the development of new process alternatives bears a strong resemblance with our notion of seizing. Third, within the configuration phase, “[…] designs are implemented by configuring a process aware information system” (Van Der Aalst et al. 2003), thus, revealing analogousness to the common understanding of transformation in dynamic capability theory. This matching shows that conceptualizing BPM as dynamic capability is not only possible on an abstract level, but is also a valid approach when considering more detailed sub-activities as developed by BPM researchers. Thus, the developed framework will be used as structuring device for the upcoming literature review. Here, we will focus on the ‘dynamic’ phases of the processes i.e. sensing, seizing, and transformation.

3.

Research Methodology

To answer our research question we reviewed relevant literature. Our review process was informed by commonly accepted guidelines for literature reviewing (Webster & 6

Watson 2002; Vom Brocke et al. 2009). We identified the literature through a journal and database search (no restriction to publication date). First, we concentrated on IS outlets including MIS Quarterly (MISQ), Information Systems Research (ISR), Journal of Management Information Systems (JMIS), Journal of Information Technology (JIT), Business & Information Systems Engineering (BISE), and Business Process Management Journal (BPMJ). Here we searched for the keywords service and productivity. Secondly, we scanned relevant service journals, namely International Journal of Services Technology and Management (IJSTM), Journal of Service Management (formerly published as International Journal of Service Industry Management; IJSIM), Journal of Services Research (JSR), and Manufacturing & Service Operations Management (M&SOM). We searched the service-oriented journals with the terms process and productivity. Second, to broaden our search, we employed similar searches to the databases Sciencedirect, EBSCOHost, and Google Scholar. The articles identified by keyword search were evaluated based on their titles and abstracts in order to assess their relevance for this study. The remaining articles became the basis for our review. Third, we selectively searched backwards, i.e., we reviewed papers referenced in the articles yielded from the keyword search. Keyword Journal IS Journals Service Journals Databases Backward Search

“service”

“productivity”

“process”

(Doherty & Perry 1999; Glushko & Tabas 2009; Ray et al. 2005) (Abdi et al. 2006; Armistead & Machin 1998; Blumberg 1994; Das & Canel 2006; Dausch & Hsu 2006; Dobni et al. 2000; Hsu & Spohrer 2009; C. R. Martin et al. 2001; McLaughlin & Coffey 1990; Vuorinen et al. 1998) (Armistead & Machin 1998; Armistead et al. 1993; Dobni et al. 2000; Fitzsimmons 1985; R. Green 2005; Grönroos & Ojasalo 2004; Grönroos 2007; Gummesson 1998; Harmon et al. 2006; Vuorinen et al. 1998) (Gummesson 1998; Bowen & Youngdahl 1998; Fitzsimmons 1985; Gadrey & Gallouj 2002; Goodwin 1988; Kimes 1989; Mauri 2007; Normann 1991; R. H. Smith 1985; Spohrer et al. 2007; Vargo & Lusch 2004; Womack & Jones 1996)

Table 1. Papers identified for the literature review.

In total, we analyzed 31 articles from the service and IS field (see Table 1). The following sections present the recommendations we derived from this analysis. They are assigned to the three phases sensing, seizing and transformation as proposed by dynamic capability theory. Some recommendations tend to address more than one phase. We tried to achieve the best possible fit of the purpose of a recommendation with its assignment to a particular phase. Due to the normative character of the identified recommendations, they are presented in an imperative wording.

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4.

Recommendations for Service Process Management

4.1.

Sensing

Monitor processes and use benchmarks In order to monitor the quality of processes, data can be obtained from the customer or by direct observation of the process or the results (McLaughlin & Coffey 1990; Vuorinen et al. 1998). Dobni et al. (2000) mention activity reporting systems as a monitoring approach that has been adapted in services from industrial engineering for enhancing service productivity. Activity reporting systems can help to determine the actual labor required for given activities and thus can be used for improvements to staff planning and budgeting (R. H. Smith 1985). Similarly, if there is a WFMS in place to steer the service process, monitoring can also be supported by the WMFS. Harmon et al. (2006) recommend implementing rigorous internal metrics to measure and improve service productivity. The advantage of internal benchmarks is that they deliver more detailed metrics and allow a company to find its own best practices. In external benchmarks, details like the exact definition of costs tend to get lost. Literature suggests that process benchmarks could be carried out against accepted quality awards. Quality awards have become increasingly popular for evaluating processes. Widely accepted are the Malcolm Baldrige National Quality Award and the European Quality Award based on the European Foundation for Quality Management (EFQM) model. Both explicitly consider processes and have their roots in TQM (Armistead & Machin 1998). Armistead et al. (1998) report on Royal Mail’s efforts where a selfassessment procedure based on the EFQM model was developed. The introduction of this EFQM-based assessment forced Royal Mail to identify all of its processes, and in doing so allowed sensible prioritization of major service productivity improvement efforts (Armistead & Machin 1998). Measure productivity Closely related to the monitoring of processes is the actual measurement of productivity. Although this measurement has been an issue in academia for decades, there still seems to be the need to develop new instruments to measure service productivity (Grönroos & Ojasalo, 2004). Buntz (1981) suggests that outputs and inputs of service processes can be used to measure efficiency, whereas outcomes and impacts are useful for effectiveness calculations. Martin et al. (2001) argue that any measure of service productivity must include some components that focus on the customer side of the service encounter. Gummesson (1998) points at the problem that the provider’s inputs and outputs of the service process can be monetized, but the customer’s cannot. This affects the accuracy of service productivity measurement, since the customer “can do more or do less” (Gummesson, 1998, p. 8). Grönroos and Ojasalo (2004) distinguish three basic alternatives for measuring productivity: 1) physical measures, 2) financial measures, and 3) combined measures. Physical measures represent the traditional approach borrowed from manufacturing. However, both physical and combined measures are said to have some flaws when it comes to represent cost and revenue as well as variations in quality appropriately. Financial 8

measures are seen as the only valid measures available, since they are able to “incorporate the quality variations caused by the heterogeneity of services and the effects on perceived quality by customer participation in the service process” (Grönroos & Ojasalo, 2004, p. 421). With regard to productivity evaluation of human services, Green (2005) suggests measuring if changes sought by the customers are achieved instead of counting the number of services or the extent of service activities. In this approach, productivity is greater the more of the desired changes are achieved. His approach recommends first the identification of a small number of primary changes. In a second step, he suggests the calculation of the weighted average direction taken by the changes.

Encourage and facilitate customer feedback Vuorinen et al. (1998) recommend the systematic use of customer feedback for measuring service quality which in their view is needed for the determination of service productivity. They recommend using the Gap Model which compares customer experience with customer expectations as it leads to a good ratio giving relevant information. This information is important to reveal possible inefficiencies or misalignments within the service processes that otherwise would remain undiscovered. However, customers usually do not give any direct feedback to the service provider as long as they are satisfied. Empirical studies show that customers who are dissatisfied by a service are more willing to evaluate on a process. Another way to achieve a direct measurement during service execution is to observe and document the service process itself (Vuorinen et al. 1998). Learning about the customer enables the service provider to implement actions that improve efficiency of service delivery and enhance productivity (Grönroos & Ojasalo, 2004).

4.2.

Seizing

Use a structured procedure According to Das and Canel (2006), service process design is the design of a system that delivers low cost, customized and high quality services by organizing a set of activities. Prior to the actual process design, the process objectives (e.g., levels of quality and productivity, empowerment and learning of workers) need to be determined (Das & Canel 2006). In this context Buntz (1981) recommends productivity planning which includes the development of a productivity proposal and its presentation to all key actors in the organization. The plan for productivity improvement should describe the steps required for implementation as well as monitoring and evaluation procedures. Many authors stress the importance of a structured procedure model for designing a meaningful service. According to Das and Canel, the first step of such a model should include prioritization of objectives by management (Das & Canel 2006). Next, the processes to be designed need to be defined with the help of supporting tools like flow charts, process design software, and simulation. Then, relevant design factors (i.e., type, layout, environment, capacity, and quality of service process and supporting IT) have to be selected and defined. In this step, process designers can also brainstorm about new approaches for the process. For each design factor, several design choices have to be considered (Fitzsimmons & Sullivan 1982). The accomplished design may then provide the basis for a pilot project. 9

Think lean Bowen and Youngdahl (1998) describe the transfer of lean thinking from manufacturing to service and give examples from three case studies. Abdi et al. (2006) repeat this idea and suggest a framework for implementing the lean approach, which originally stems from the Japanese automotive industry, in service organizations. The core of lean thinking is to avoid every non-value adding activities (waste). At all levels of the organization, people should be given the skills and means to systematically reduce waste by designing better ways of working, improving connections and easing flows within supply chains, i.e., achieving better processes. The framework consists of four steps which are 1) think lean about your service, 2) setting the expectation by avoiding the mean service, 3) benchmarking your operations with service role models (i.e., best practices), and 4) navigate using the practitioners and consultants experiences. Obviously, this framework also touches all the other phases of the BPM lifecycle to a certain degree. Moreover, Abdi et al. (2006) describe some concrete measures of designing lean services. However, they rather provide a general description of lean thinking and a plea for process orientation in services. They emphasize that the whole service process needs to be designed from the end-customer view transcending functional silos within the service firm. Furthermore, Abdi et al. (2006) discuss the five lean principles as proposed by Womack and Jones (1996) and show that they can be transferred to the service industry. Although criticized in recent literature, Abdi et al. (2006) argue in favor of continuing the transfer of such production-line approaches from manufacturing to service. Standardize and customize for masses Armistead et al. (1993) identify the volume of demand, the variety of services to be offered, and the variation in the volume and nature of demand over time as three vital influences on service systems productivity. The need for a variety of services is determined by the variety of expectations of customers. It is argued that variety reduces system utilization and efficiency and also increases input costs, all in all leading to a decrease in productivity. Therefore, the volume of demand has to be maximized, the number of variations has to be minimized, and the variation in the volume and nature of demand has to be grinded. Reporting on a case study of BPM at the Royal Postal services, Armistead and Machin (1998) describe variety in operations, unless managed, as a possible constraint on improving productivity. From their point of view, BPM helps counter these effects by facilitating better information flows, and helping establish responsibility at interfaces in the process. In order to be able to produce varied and even individually customized products and services at the low cost of a standardized, mass production system, the concept of mass customization is suggested (Bowen & Youngdahl 1998). This concept is seen as the convergence of basic manufacturing and service principles, i.e., the individual customization associated with service and the efficient volume associated with manufacturing (Bowen & Youngdahl 1998). Consider customer input In service processes, customers participate as co-producers of value (Vargo & Lusch 2004). Fitzsimmons (1985) argues that service processes can be designed to permit greater customer involvement in order to achieve productivity gains. Customers 10

should not be ignored as a productive resource. He presents three strategies to design service processes that allow for an active participation of customers. First, customer labor can directly substitute for provider labor (e.g., ordering and food carrying in fast-food restaurants). The second strategy is to smooth service demand through letting customers wait, making appointments and reservations, or giving price incentives for off-peak capacities. Third, technology can substitute personal attention. Gummesson (1998, p. 14) recommends that service managers should “consider if more of a service can be produced by the customer, thus unloading costs – but not at the expense of service quality.” Similarly, Vourinen et al. (1998) describe the opportunity to utilize customers as free inputs thereby increasing productivity of the service provider. When determining customer involvement in service process design, the service blueprinting approach can be used (Glushko & Tabas 2009). Service blueprinting distinguishes between the “front stage” and the “back stage” of the service encounter (Shostack 1982). At the front stage the interaction between the customer and provider takes place. The back stage comprises other preceding activities needed to make this interaction possible. Glushko and Tabas (2009) mention that front and back stage process designers may look at service design from different and conflicting perspectives, often leading to little collaboration and communication between these two stages. Go digital and connect Hsu and Spohrer (2009) argue that service quality and productivity can be increased by implementing digital connections between stakeholders (e.g., customers and providers) and resources. Their main proposition is that digitization “reduces the cycle time and transaction cost for service systems and service co-creation, and scaling these connections decreases the marginal cost for new value propositions and new value co-creations, as well as the average cost for individual services.” (Hsu & Spohrer, 2009, p. 273) They call this approach “digital connections scaling”. It is expected to achieve three types of economies of scale: accumulation effect, networking effect, and ecosystem effect. Accumulation effects arise through the linear joining of customers, resources, and/or providers, which can be shared and re-used among service systems to reduce cycle time and transaction costs. Networking effects result out of peer-to-peer expansion among stakeholders. The ecosystem effects refer to the total expansion of system-wide interactions (Hsu & Spohrer 2009). In addition, Vourinen et al. (1998) describe the opportunity to replace the personnel through IT. Hence, designers of a service process have to examine if it is really possible to substitute technology for labor.

4.3.

Transformation

Arrange service agreements The configuration of a service system can be established by a formal service agreement. Service agreements “commit the service providers to certain configuration, execution, and delivery of their processes” (Dausch & Hsu 2006). Service agreements are not one-time transactions but long-term commitments. They are needed to allow customers to evaluate and benchmark the services they obtain. Founding on the idea of mass-customization, Dausch and Hsu (2006, p. 50) present a reference model for developing service agreements which they expect to yield “the opportunity for major productivity gains in service.” 11

Handle variety and volume A service system is dependent upon the demands and influences from its environment. One task of the configuration phase is to ensure that market changes do not disrupt the operational service processes in a dysfunctional way (Armistead et al. 1993). Armistead et al. (1993) underline that volume and variety of demand as well as the variations over time are vital influences to service systems productivity and therefore need to be managed. According to Blumberg (1994), levers to improve productivity in field services can be found within the process of customer request handling. Initial screening and in-depth analysis of customer requests should be used to 1) fully understand the service requirement, 2) evaluate the request to determine whether or not an on-site service engineer is required, 3) determine, through on-theline diagnostics and utilizing historical information, if the user can be instructed to correct the service problem directly, and 4) identify the formal requirement to dispatch and optimally assign a specific service engineer in terms of specific skill levels and parts needed, based on the diagnostic process. Moreover, the concept of yield management has been introduced to describe the “process of allocating the right type of [service] capacity to the right kind of customer at the right price.” (Kimes, 1989, p. 15) This concept is especially popular in the airline and hotel industry where available capacities are limited and perishable and customers are charged different prices for consuming otherwise identical services. The overall goal is to understand, anticipate, and influence customer behavior in order to maximize revenue (Mauri 2007).

5.

Discussion

The approaches presented above are recommended practices and tools to improve the productivity of service processes. Taking a closer look at the ten statements, future research should help to close gaps in productivity-driven service process management and therefore should address the following areas. Understanding IT Support for Service Processes As IS researchers, we intuitively strive to develop and analyze IT support for business processes. But only few of the analyzed papers address IT support for service processes in a detailed manner. Nevertheless, it was mentioned that technology can substitute labor (Vuorinen et al. 1998) and that service processes can be automated by WFMS (Doherty & Perry 1999). Hsu and Spohrer (2009) see potential in connecting stakeholders through IT to reduce costs and thereby improve productivity. Following up these ideas, further research seems necessary to better explore, which IT assets and/or IT capabilities (Wade & Hulland 2004) service firms should acquire to effectively support their business processes. Existing empirical studies that researched the influence of IT on productivity came to conflicting results (Brynjolfsson & Yang 1996; Grover et al. 1998). As supported by the study of Ray et al. (2005) a distinction between technical assets and firm-specific capabilities/knowledge seems helpful for better understanding how service processes can benefit from IT. Taking a BPM perspective on services can help to abstract from purely technical issues and focus on capabilities how to utilize IT successfully. Also, the applicability of IT support to different types of services needs to be researched more thoroughly. Often, the var12

ious requirements for IT support originating from different types of services are hardly discussed. Cross-boundary BPM Obviously, it is an imperative to consider the customer when designing service processes. The design of service processes is expected to be in accordance with customer needs. Furthermore, direct customer feedback is supposed to be gathered during enactment. Hence, including external opinions, perspectives, and experiences into service design appears to be important for reaching a higher level of service productivity. In this view, service processes can be regarded as cross-organizational and thus call for collaborative BPM. Collaborative BPM is a growing trend in IS research that still bears great potential for future research (Niehaves & Plattfaut 2011). We see this potential especially in the service industry, where the co-production of value by provider and customer forms a kind of extended enterprise (Hsu & Spohrer 2009). Hsu and Spohrer (2009) see the potential to reduce inter-enterprise transaction cost and cycle time along the demand and supply chains by employing innovative virtual organizations. However, interpreting the results of this review, the collaboration with stakeholders external to the service firm seems to be rather restricted to the customer. This raises the question whether there are no other external stakeholders to be regarded when designing service processes. The service sector offers a great plurality of potential collaborators in BPM. The concept of Lean Management as put forward by Bowen and Youngdahl (1998) and Abdi et al. (2006) provides a first step toward the better inclusion and utilization of further external stakeholders, e.g., suppliers of products and services. Contributions of Design Science Apart from gaining a better understanding of the influence of IT on service process performance, we also see research opportunities in the conceptual design and implementation of innovative IT support for business processes. The emerging discipline of Service Science, also termed Service Science Management and Engineering (SSME), is said to recognize the need for building and evaluating IT artifacts that are of utility to the service industry (Becker et al. 2009). Among the reviewed articles, however, little contribution is made with regard to the design of innovative artifacts which is subject to design science research (Hevner et al. 2004). In line with Becker et al. (2009), we see a yet unexploited potential for IS researchers within the field of SSME. Also, Hsu and Spohrer (2009) are convinced that design science for services will play a central role for the new Service Science field. Therefore, we argue that the service industry would benefit from the development of new artifacts, i.e., constructs, models, methods and software instantiations (Becker et al., 2009; March & Smith, 1995). These artifacts should be designed to help service managers improve the productivity of service processes systematically. We should develop especially the kind of IT artifacts that can help to digitally connect service systems thus creating the opportunity to better scale service delivery and improve productivity (Hsu & Spohrer 2009).

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6.

Conclusions and Limitations

Our research objectives were the conceptualization of BPM as dynamic capability and the identification of practices that can help service managers to increase productivity of service processes. Using the BPM lifecycle from literature, we were able to show that dynamic capability theory can be a valuable perspective on BPM and that it matches with current research on this topic. To address the second research objective, we analyzed 31 articles from the service and IS field. We used our dynamic capability lens as a structuring device for the analysis. The review resulted in 10 general recommendations that were assigned to the three phases (sensing, seizing and transformation) as identified by dynamic capability literature. Implications for Research. Within section 2, we introduced our understanding of BPM as a dynamic capability. Here, we conceptualized BPM as the ability of an organization to integrate, build, and reconfigure business processes to address rapidly changing environments. Moreover, we discussed the three abilities sensing, seizing, and transformation with respect to the BPM lifecycle. This understanding opens for a novel theoretical understanding of BPM. Thus, our contribution is valuable for future research in this field as on, e.g., the development of BPM capabilities (Rosemann 2010) (ad research objective 1). Building upon the recommendations identified in the analysis, we outlined three interconnected areas of future research from which the service industry could benefit (ad research objective 2). First, we see the need to broaden the view on service process management beyond the boundaries of service firms and their interaction with customers. Process designers should utilize external knowledge, not only from customers, but also from other external partners. Therefore, a better understanding of opportunities and prospects of collaborative BPM in services is needed. Second, the use of IT to support service processes is only marginally discussed in the papers we were able to identify. Hence, further research on the influence of IT assets and IT capabilities on service productivity seems promising. Third, a better understanding of the relationship between IT and service productivity would support the design of new IT artifacts that are of utility to service managers. Here, the design science paradigm could provide a sound basis for future research. Implications for Practice. Service managers from practice may utilize the set of recommendations we identified in this review to assess and (re-)design their service processes in the light of service productivity. This review can provide the impulse to introduce productivity measurement and benchmarking for existing processes in service organizations in the first place and also help service managers to include productivity considerations in the design of new service offerings. Limitations. Our findings are, however, beset with some limitations. Although we employed a database search, including different key words in our search phrase and selectively conducted backward search, we admit that our sample of 31 papers cannot be labeled exhaustive. On the one hand, we focused on journal articles from the service and IS field. Neither did we direct our search to other sources like conference proceedings or books (except for sources accessed within the course of the backward search) nor to other adjacent research fields. However, we feel confident that our search provided us with a good insight into the relevant body of knowledge and a kind of common sense of managing service processes.

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Acknowledgements This paper was written in the context of the research project KollaPro (promotional reference 01FL10004) which is funded by the German Federal Ministry of Education and Research (BMBF).

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