Service Productivity Management Status Quo and Directions for the Design of Conceptual Modeling Grammars

Transcription

1 th Hawaii International Conference on System Sciences Service Productivity Management Status Quo and Directions for the Design of Conceptual Modeling Grammars Jörg Becker, Daniel Beverungen, Ralf Knackstedt, Hans Peter Rauer, Daniel Sigge University of Muenster European Research Center for Information Systems (ERCIS) {becker; daniel.beverungen; ralf.knackstedt; hans.peter.rauer; Abstract The emergence and proliferation of the service economy greatly amplifies the need to thoroughly manage service productivity. The purpose of this study is to evaluate the expressiveness of conceptual modeling grammars for service productivity management as well as to provide directions for future research. Based on authoritative theories on service productivity, we identify a set of evaluation criteria with which we benchmark a selection of conceptual modeling grammars. Our analysis yields two major insights: First, literature on service productivity provides only limited theoretical support for designing conceptual modeling grammars. Second, the concepts contained in service productivity theories have found little recognition in the grammars meta models. We derive two core implications: First, extended theory support is needed to inform the design of conceptual modeling grammars. Second, additional constructs need to be included in conceptual modeling grammars to facilitate productivity-oriented analysis and design in the service sector. 1. Introduction A conceptual modeling language is frequently used to represent relevant knowledge about a domain. It is the set of all models that can be built with a conceptual modeling grammar (CMG) [3], i.e. a set of graphical constructs and a set of rules that describe how the constructs can be used in order to create wellformed statements about the world [26]. The language constructs are typically represented by graphical symbols. The graphical symbols that represent the language constructs establish the concrete syntax, i.e. the representational aspect of the language, whereas the rules that govern the use of its constructs constitute the abstract syntax. The semantic meaning of the modeling constructs is only partly formally defined in CMGs. In most cases it is established by assigning natural language terms, which are normally not part of the language specification, to the model constructs [11]. The abstract syntax of CMGs is typically defined in a language-based meta model which describes the constructs available in the language and the syntax of their relationships. The focus of this study is advancing CMGs for designing conceptual models in the area of service performance benchmarking. An adequate measure for this purpose is the concept of productivity as suggested in [15]. Quantifying productivity typically comprises dividing amounts of output by input. Calculating this measure for services is far more complex than in goods manufacturing. Reasons for this complexity are systematized in the so-called IHIP criteria, that characterize services in contrast to physical goods as intangible, heterogeneous, inseparable, and perishable [27]. These attributes render the common numerator-denominator-approaches inappropriate for service productivity management as they are, e.g., hard to identify and difficult to depict in a quantitative manner. For instance, service quality is an aspect of productivity that is judged subjectively by the beneficiary and is, therefore, hard to quantify [8,18]. As a consequence, service productivity management requires other IT artifacts (i.e., language constructs, models, methods, and software implementations) than productivity management for physical goods does. Conceptual models can represent static phenomena, such as things and their properties, and dynamic phenomena, such as events and processes. In order to conceptualize service productivity, we focus on product models here. This focus is set to capture how well organizations make use of their resources, whereas process models would focus on evaluating the dynamic performance of particular work steps [20]. Product models are conceptual models that represent the static properties of a product. For instance, the socalled bill of material (BOM) is a product model that specifies the components and structure of a physical good. In order to represent the properties of a service that impact on its productivity, it is necessary to adapt traditional conceptual modeling grammars to the particular needs of services /12 $ IEEE DOI /HICSS

2 Input Achievementpotential Precombination Endcombination Output Further internal input factors External input factors Productivity of pre-combination Productivity of end-combination Product models comprise master data about a physical good or a service that can inform service productivity management in various respects. First, they define the structure of a service as a nested hierarchy of unambiguously defined components and attributes. This facilitates the identification of productivity-relevant input and output factors on a disaggregated level of atomic components. Second, a product model can serve as a blueprint to transfer productivity metrics between organizations. Third, product models provide means for integrating data residing in different information systems [7]. Although we perceive advancing CMGs for services as a pressing need against the backdrop of the rise of a world-spanning service economy [16], this topic has only been sparsely addressed in previous literature. The purpose of this study is to evaluate the ontological expressiveness [26] of a set of CMGs for product models in the light of the theoretical requirements of service productivity management. While [26] propose such analyses to be done by comparing the modeling constructs of a CMG against the constructs in an ontology, sufficient ontology support seems to be still unavailable in the area service productivity. For instance, the Universal Service Description Language (USDL) that has been put forward as ontology for describing services falls short on accounting for service productivity issues [19]. Therefore, we develop our own set of ontological constructs based on reviewing a selection of authoritative service productivity theories. The paper proceeds as follows. In Section 2, the theoretical approach of utilizing theory for the purpose of evaluating and informing the design of IT artifacts is discussed. In addition, a selection of theories that has been derived from a literature review is presented to introduce the theoretical constructs needed in the domain of service productivity management. In Section 3, a set of evaluation criteria is derived from the reviewed theories. This is necessary since adequate ontology support in this domain is yet to be established. In Section 4, a selection of three CMGs that stem from service marketing, information systems, or computer science backgrounds is evaluated with the Fig. 1. Service productivity according to [19] proposed criteria in order to rate their ontological expressiveness. In Section 5 the findings are discussed to identify directions for further research. 2. Theoretical background Authoritative literature in the Information Systems (IS) discipline emphasizes that the design of IT artifacts be informed by kernel theories [12]. This is in recognition of a cyclic research process that comprises alternating steps of analysis and synthesis (i.e., design & evaluation). Kernel theories represent validated knowledge that can be used to inform design processes. Consecutively, the design needs to be evaluated to validate the adequacy of the design, as well as to deduct theoretical knowledge as to why the design works or is superior to similar approaches. Since design is conceptualized as a search process [13], additional cycles of design and evaluation might be conducted until saturation is reached. In this study, we utilize theories on service productivity management as analytical lenses to identify design requirements that need to be addressed by CMGs for product models. To identify the seminal theories for service productivity management a comprehensive literature search was conducted in which various service productivity theories were identified [5,8-10,13,14,18,25]. Four of these theories were selected as the theoretical fundament reported here. This selection was made to represent different theoretical streams in the literature. Corsten [5] claims that a service provider needs to establish the capabilities to provide a service before the service is created in a subsequent step of value cocreation with the customer. Accordingly, two types of productivity are distinguished: the productivity of the pre-combination and the productivity of the endcombination (Fig. 1). The pre-combination is created autonomously by the service provider. Input resources are employed to prepare for service provision. This is called the achievement potential. The productivity of the pre-combination is determined by the achievement potential divided by the input factors needed. 1523

3 Being the output of the pre-combination, the achievement potential also serves as an input factor for the end-combination. In case a customer purchases a service, a certain fraction of the achievement potential is channeled into the service delivery process. In addition, the service provider usually has to provide other inputs, such as service technicians or spare parts. Since services require a degree of customer involvement (i.e., co-creation of value) external input factors have to be taken into account during the endcombination, such as objects, people, or information inputted by the customer. The ratio of the service output to the different input factors constitutes the productivity of the end-combination. The achievement potential is further detailed. First, it can be represented by quantitative and qualitative indicators, all of which can be weighted based on customer or expert surveys and then be combined into an overall quality index. Second, a distinction is made between the offered achievement potential and the utilized achievement potential. This takes into account that a customer s demand can be lower than the capacity offered by the service provider. Therefore, the portion of utilized on offered number of transactions or actual service hours [25]. The the output may consist of highly customized services ( ), and the definition of the service output becomes a more laborious task. This task is often complicated by the intangible nature of services. [25]. Qualitative aspects are examined for both input factors and service outputs. Output quality is not assessed from an internal, technical or functional viewpoint but rather from the customer s perspective. On the input side, tangible and intangible quality elements are distinguished. The layout of a store, for instance, constitutes a tangible element of input quality that conveys a customer s expectations of the service. Parasuraman [18] also argues against service quality being included into the productivity calculus as a concept in its own right, but rather as a mediating variable between the provider and the customer (Fig. 2). From a service provider s perspective, input factors such as labor, equipment or technology are transformed into outputs such as sales, profits, or market share. Output refers to the incentives and goals of the service provider to offer the service that is beyond the traditional notion in a goods-dominant logic, since customers often play a co-production role, achievement-potential is incorporated into the productivity ratio of the pre-combination. Vuorinen, Järvinen, and Lehtinen [25] view service productivity as focused at a certain point in time, i.e. from a static perspective, and state that service productivity constitutes conventional productivity metrics and service quality considerations. Consequently, service productivity is conceptualized as the ability of a service organization to use its inputs for providing services with quality matching the expectations of customers. [25]. Therefore, quantitative as well as qualitative factors are considered when assessing service productivity. Quantitative aspects represent the traditional output/input conceptualization of productivity. Input factors can be further subdivided into raw materials, capital, and labor. Output quantity refers to the service volume provided, such as the Fig. 2. Service productivity according to [7] providing some amount of direct or indirect input [18] in service provision. The dashed arrows in Fig. 2 indicate relationships between the two productivity perspectives and service quality. Higher levels of inputs provided by a service company will likely increase service quality, while higher levels of customer inputs have the inverse effect. Higher service quality, in turn, has a positive effect on outputs of both perspectives. Moreover, there are three direct effects between the two perspectives, indicated by solid arrows and marked with encircled numbers: (1) indicates substitution effects between the input factors of service providers and customers, based on the division of labor between both actors. The intensity of this effect depends on the allocation of company resources (2), since increasing the company s inputs in the wrong place might have no or only little impact on the amount of customer input needed. On 1524

4 the output side, an increase of output from the customer s perspective is claimed to have a positive effect on the output from the company s perspective (3), such as increasing a customer s willingness to pay. Grönroos and Ojasalo [8] perceive service processes as open systems in which customers are directly and often simultaneously involved. Standardization in such systems is only partially possible, leading to varying service quality and precluding quantitative output metrics. In addition, activities contained in a service process are distinguished into three sub-processes according to the level of customer involvement. Accordingly, activities can either be conducted by the service provider in isolation, cooperatively by the provider and the customer, or by the customer only. These subprocesses are directly (solid arrows in Fig. 3) or indirectly (dashed arrows in Fig. 3) influenced by different input factors and affect, directly or indirectly, different aspects of the service output. On the input side, the contribution by the service provider is distinguished from the contribution by the customer. Customer input is further subdivided into inputs provided by the actual customer and inputs provided by other customers. Arguably, such inputs are important with regard to the emergence of services provided on web-based service marketplaces. On the output side, quantitative and qualitative aspects are distinguished. Quality partly manifests itself in the production and delivery process, as well as in the outcome generated in this process. The former is called interaction-induced quality, while the latter is called outcome-induced quality. Both quality dimensions are filtered by the company s image, leading to the customer perceived quality. Thus three types of efficiency are derived. Internal efficiency refers to how efficient a company transforms resources into internal outputs. External efficiency refers to the ability of a company to produce a certain level of perceived quality, i.e. how effectively it creates external interest in the output [8]. Capacity efficiency is about superior resource utilization, which is challenging since services cannot be stored or produced in advance of customer demand. 3. Identification of evaluation criteria From the presented theories a set of criteria for evaluating the expressiveness of CMGs in the area of service productivity management is derived. Notably, other important factors, such as compliance with the information systems in service companies or the abilities of service professionals to correctly apply CMGs in modeling projects, have been excluded to keep the analysis focused on how well existing CMGs can represent the domain requirements of service productivity management. In line with [5], a CMG has to provide constructs to separately depict the pre- and the end-combination of service provision ([1.1]). A sophisticated way to do this is to provide different modeling contexts for both phases and for the association of multiple endcombinations to one pre-combination. In addition, CMGs need to contain constructs for depicting inputs and outputs of both the pre- and end-combination of service production ([1.2], [1.3]). Whereas the inputs for the pre-combination need not be further specified, the inputs of the end-combination need to be distinguished based on their origin. A modeling grammar should be able to distinguish internal from external input factors to enable productivity analyses from both perspectives as well as the impact of shifting service activities ([1.3.1]). The achievement potential deserves special attention due to its mediating role between the two phases ([1.1.1]). Firstly, a modeling language needs to support quantitative ([ ]) and qualitative ([ ]) aspects of the achievement potential. Different quality indicators should be supported and weighted to display the overall quality needs in the models. Second, a differentiation between the offered and the utilized achievement potential is important to enable comprehensive analyses that incorporate the degree of resource utilization. Since the concrete parameterization of outputs is unknown before the service is actually performed, a CMG can only address the added value for the customer ([1.4]). In line with [25], CMGs need to provide constructs for the inputs channeled into the production process and for outputs that are created by a service. Both quantitative and qualitative characteristics need to be identified ([2.1] [2.4]). For inputs, language constructs for labor, raw material, and capital should be incorporated ([2.1.1] [2.1.3]). Second, CMGs need to account for tangible and intangible elements of input quality ([2.2.1], [2.2.2]). This is reasonable, as both quality aspects demand different parameterization and measurement approaches. Output is conceptualized as the service quality perceived by a customer ([2.3], [2.4]). The framework of [18] describes productivity in services from a time-related, retrospect view. A CMG, therefore, needs to support the modeling of input factors ([3.1]) that need to be distinguished by their origin ([3.1.1]). 1525

5 Only if modeling of internal and external input factors is supported a meaningful analysis of their relationship can be conducted. The remaining exemplary input factors, labor, equipment, etc. are, in contrast, rather unambiguous and thus included as requirements ([3.1.2 e]-[3.1.5 e]). The letter e denotes that the requirements stem from non-exhaustive examples provided by [18]. On the output side, the derivation of requirements From [8] several criteria can be derived. First, the resources channeled into the production process need to be addressed ([4.1]). In order to analyze the contribution of customers and service providers separately, a further distinction between these two input types is necessary ([4.1.1]). Moreover, a differentiation between direct customer inputs and indirect customer inputs (i.e. provided by other fellow customers) should be designed into the CMG Fig. 3. Service productivity according to [6] on CMGs is not that evident. This is due to the fact that especially the outputs from the service company s perspective, exemplified by the indicators sales, profits and market share, are not defined in a product-related way. In combination with the transactional data of past service provisions, this information is essential for calculating, for example, the profits generated in a certain period of time. Thus, the depiction of economic indicators of the service provision (especially of costs and prices) is considered the best approximation ([3.2]). Furthermore, a separate depiction of outputs from the customer s perspective needs to be possible ([3.3]). It is important to clearly differentiate between the internal and external view on the service output to be able to assess a customer s productivity. Thus, the emphasis lies on the differentiation of output perspectives rather than on external output factors. Although outputs from the customer s perspective, such as service performance or satisfaction, seem quite ambiguous, they are transferred into requirements in order to make sure that the strong focus on such soft output factors is properly reflected in the requirements ([3.3.1 e], [3.3.2 e]). Quality plays an important mediating role between both productivity perspectives ([3.4]). ([4.1.2 e] [4.1.6 e]). The output of the service process needs to be educible by taking into account both quantitative and qualitative aspects. The quantitative aspect, transformed to the level of single products (i.e. on the type level of product models), is interpreted in the sense of quantitative service characteristics ([4.3]). In addition, customer perceived quality is included ([4.2]). It is disputable, however, whether a further specification of outcome and process quality aspects is necessary or even a meaningful concept in product models. However, a distinct treatment of both aspects can be formulated that builds upon the general requirement of depicting customer perceived quality ([4.2.1]). In addition, the inclusion of capacity efficiency is included in the form of forecasted figures or planning data [4.4], since such information in combination with capacity information is necessary to compute the expected capacity utilization. 4. Evaluation To evaluate the ontological, we focus on identifying what [2] and [20] describe as construct deficits. 1526

6 Fig. 4. Meta model of H2-ServPay [28] These are identified if ontological constructs that exist expressiveness. The rating levels comprise the in the theories are missing in the set of modeling following values: constructs proposed in a CMG. The CMGs analyzed The rating level ++ indicates, that a requirement were selected based on an existing evaluation of is explicitly covered by the modeling language modeling grammars for the service sector [21]. To with a sophisticated language construct. If the keep the argumentation focused, we report on the constructs provided are rather incomplete or evaluation of three CMGs here. Since service science rudimentary, the rating level + is used. is a multi-disciplinary research field [22], this selection In case a requirement is not explicitly supported, it was made to include a computer science perspective might still be possible to use other constructs such (UML, Unified Modeling Language), an information as textual annotations to emulate the feature systems perspective (H2-ServPay), and a service provisionally (rating level - ), or the criterion marketing perspective (Molecular Model). cannot be addressed at all (rating level -- ). For each criterion derived in the previous section, Just like in [2], the meta models of the CMGs are four rating levels are defined. The fundamental utilized as basis for the rating procedure. Meta models criterion for the demarcation of the different rating are frequently employed for assessing and comparing levels is whether each of the requirements is explicitly modeling languages, e.g. in [24]. A review of different supported by the modeling grammar or not. Although methods for the evaluation of information modeling this rating procedure is inherently vulnerable to methods and meta modeling can be found in [23]. For subjectivity (which might limit its repeatability in the analysis the meta models of the grammars were future studies), we argue that our approach is sufficient reconstructed in the extended Entity-Relationship to make a general appraisal of a CMG s ontological Model (eerm) notation [4,21]. Due to space 1527

7 constraints, we refrain from stating an UML meta model here and refer to [17], whereas we depict the meta models of H2 and the Molecular Model. This decision was made, since both meta models are not as well-known as the UML meta model H2-ServPay The H2-ServPay modeling grammar [1] has been developed for modeling, configuring, and pricing physical goods and services. The calculation of cost data and the estimation of a customers willingness to pay for a service were central design objectives. These calculations presuppose language constructs for resources, prices, outcomes and lifecycle data, which also are relevant in service productivity management. The meta model of H2-ServPay is depicted in Fig. 4. Concerning the input side, the element Resource is the most important construct. It can be used to model arbitrary input factors. However, a comprehensive resource concept is not provided. Merely human resources (i.e. the input factor labor) possess a special status. They can be specified and clustered in Business Units that in turn can be structured in nested hierarchies. Qualitative aspects in contrast cannot be modeled in a meaningful way. H2-ServPay provides a differentiation with the construct Customer Resource. However, it only distinguishes provider s and customer s resources, but ignores further idiosyncrasies of internal and external inputs. The value proposition of a single service or a bundle can be thoroughly depicted by using the construct Outcome and its self-referential relationship (Outcome Hierarchy). It allows creating a detailed model of the different components of the service offering, although a further differentiation of output types is not provided. Quantitative and qualitative output aspects can be included by using the generic construct Attribute. This allows depicting essentially any output-related information that is deemed relevant by the modeler. H2-ServPay fails to consistently separate the customer s and the service provider s perspective. Consequently, more differentiated modeling constructs would be desirable for this purpose. However, such detailed constructs are provided by the H2-ServPay modeling language on other occasions. The possibility of modeling the economic results of a service offering needs to be highlighted in this regard. The costs of value bundles can be comprehensively depicted throughout their whole lifecycle and be set into relation with the market prices. Thus, H2-ServPay allows for the depiction of the company s output in broad terms [18] UML Class Diagrams The assessment of Class Diagrams based on the catalog of requirements reveals that its universality can be considered as its prime strength and weakness. The UML aspires to be useable for a preferably broad bandwidth of application areas within the context of software development [17]. Nonetheless, the concepts provided by UML Class Diagram are generic enough to depict other phenomena (e.g. social or sociotechnical systems) as well. Consequently, UML has successfully diffused into research and practice and has been used to facilitate the exchange of product data [12]. However, in terms of service productivity management, our analysis shows that none of the requirements formulated in the developed catalog are fully covered by dedicated language constructs. As the UML has been designed for software engineering projects, aspects that pertain to domain aspects such as service productivity management have not been a design requirement during its inception. On the other hand, when looking at the UML Class Diagrams in more detail, one can observe that they are sufficiently flexible to emulate all the specific constructs that are requested in the catalog of requirements. For example, the construct Class can be used to model service components as well as input factors. A differentiation of both element types can be included by utilizing principles of inheritance. In a next step, Associations can be used to describe which of these input factors are channeled into the production of certain service components. The construct Multiplicity can be used to further detail the quantitative aspect of such a relationship. Also the constructs Attribute and Operation that allow specifying structural and behavioral aspects of a Class, create high degrees of freedom for the modeler. They can be used to model various qualitative and quantitative aspects of a service component or input factor. Furthermore, the UML meta model can be easily extended with stereotypes to include specific service oriented constructs into the language as done in [6]. The lack of focus on service productivity management issues inhibits high scores in any of the evaluation criteria. The highest rating level ( ++ ) is not assigned at all. On the other hand, every requirement can be somehow depicted due to the universality of the provided UML language constructs. Consequently, the lowest rating level ( -- ) is also not assigned as well. Thus, the coverage of every requirement is classified by one of the two medium rating levels ( + and - ). In some cases the decision between these two is not unequivocal and difficult to make. Due to the fact that 1528

8 the specifications of the rating levels often demand for language constructs that are explicitly defined for a given purpose, the assessment tends, overall, more towards the lower rating level ( - ). However, it has to be underlined that this does not mean that Class Diagrams are coercively unsuited for the purpose of illustrating structural service characteristics and productivity-related information. Depicting such issues may simply demand for more creativity and effort in the modeling project. The modeler is forced to integrate semantics on the model level (rather than on the meta model level) by using powerful but complex to use constructs such as inheritance. Therefore, the resulting models can be complex and hard to understand. An option for mitigating this effect is extending the UML meta model with additional service stereotypes for service productivity management. flight ticket representing a transportation service) but has no or only little value on its own. The latter cannot be possessed by the customer but may nonetheless have a profound impact on the service purchase. Between the aforementioned types of Elements, relationships can be modeled by using the construct Bond. They generally depict decisions, affiliations, mutual influences, correlations etc. To these Bonds, and also to the Elements themselves, an attribute weight can be attached. This construct reflects the possibility of emphasizing certain aspects, for example according to customer preferences, or to exhibit clusters of components having a high correlation [22]. Since the Molecular Model has originated from Service Marketing, it incorporates all four elements of the classical marketing mix: product, price, placement and promotion. The former is embodied in the already Fig. 5. Reconstructed meta model of the Molecular Model 4.3. Molecular Model The Molecular Model postulates the concept of market entities, i.e. atomic (inseparable) services and products can act simultaneously to form a larger, connected and unique "molecular" configuration correlation [22]. It provides means to make such market entities explicit in models. Since no meta model has been supplied, we reconstructed it from a selection of models instantiated with the grammar (cf. Fig. 5). It depicts a Market Entity (e.g. automobile or maintenance service ) that consists of several (or at least one) Elements. That is either a Service Element or a Physical Object. The latter construct is further subdivided into Product Element and Service Evidence. The Product Element (e.g. vehicle ) is the tangible complement of the Service Element (e.g. transportation ). The construct Service Evidence represents physical objects which cannot be categorized as true product elements. [22] It is again further subdivided into the constructs Peripheral Evidence and Essential Evidence. The former is possessed by the customer as a part of purchase (e.g., a described construct Market Entity (i.e. the Elements and their mutual relationships) and clearly focused by the Molecular Model. The other three aspects are only superficially supported by the constructs Distribution Strategy, Pricing Strategy (i.e. costs and prices) and Advertising/Promotion Strategy. In an instantiated model, they are more or less described in natural language [22]. A detailed assessment of the modeling constructs provided by the Molecular Model reveals that is not suitable for depicting productivity related information. Virtually none of the items listed in the catalog of requirements are covered by language constructs designed for this purpose. Moreover, the Molecular Model does not provide any generic constructs (e.g. flexible attributes that can be attached to model elements) that could be used to depict information like resource origin or quality indicators provisionally. The only aspect where the Molecular Model obtains an acceptable rating level is modeling the service outcome. Due to its focus on marketing, the Molecular Model is specialized to convey a profound understanding of the single components that make up a 1529

9 Market Entity. The constructs Peripheral and Essential Evidence can moreover be used to outline the components that have vital impact on service perception. This is a distinct feature of the modeling language at hand. Pricing Strategy can be used to depict economic indicators of a service. Table 1: Comparison of CMGs Comparison of CMGs with the proposed evaluation criteria [5] [25] [18] [8] H2-ServPay UML Mol. Model [1.1] Individual modeling of pre- and end-combination [1.1.1] Achievement potential, especially its intermediate role [ ] Quantitative aspects of the achievement potential [ ] Qualitative aspects of the achievement potential [1.2] Input factors for the precombination [1.3] Input factors for the endcombination [1.3.1] Demarcation of internal and external input factors [1.4] Service output [2.1] General depiction of quantitative input factors [2.1.1] Labor input [2.1.2] Raw material input [2.1.3] Capital input [2.2] General depiction of qualitative input factors [2.2.1] Intangible quality elements [2.2.2] Tangible quality elements [2.3] Quantitative service output [2.4] Customer perceived quality [3.1] Input factors [3.1.1] Demarcation of internal and external input factors [3.1.2 e] Labor (internal) [3.1.3 e] Equipment (internal) [3.1.4 e] Technology (internal) [3.1.5 e] Time (external) [3.2] Economic indicators [3.3] Separate depiction of outputs from customer s perspective [3.3.1 e] Customer satisfaction [3.3.2 e] Service performance [3.4] Service quality [4.1] Input factors [4.1.1] Demarcation of internal and external input factors [ ] Demarcation of direct and indirect external input [4.1.2 e] Personnel/labor (internal) [4.1.3 e] Information (internal) [4.1.4 e] Systems (internal) [4.1.5 e] Technology (internal) [4.1.6 e] Time (internal) [4.2] General depiction of customer perceived quality [4.2.1] Outcome (technical) quality [4.2.2] Process (functional) quality [4.3] Quantitative service output [4.4] Demand (and capacity utilization) aspects Discussion With respect to the ontological expressiveness of product modeling grammars, it was shown that none of the identified CMGs was able to adequately depict all language constructs needed in service productivity management. Since the set of grammars investigated was selected from a broad study on service modeling languages, we are confident that no more adequate CMG were excluded from the analysis. This observation points at a crucial research need to design more adequate modeling grammars by inserting additional language constructs into the meta models of existing CMGs. In this paper, however, we could only perform a narrowly-focused review of the expressiveness of CMGs, which leaves plenty of research opportunities related to other areas of empirically evaluating conceptual models. For instance, [26] identify three additional types of grammatical deficiencies, as well as point to the importance of evaluating and designing modeling methods, modeling scripts, and modeling contexts, whereas [3] develop four guidelines for empirical evaluations of CMGs that open up an exhaustive array of further research opportunities. In addition, future research shall strive to develop sound ontology support to more comprehensively conceptualize the language constructs needed for service productivity measurement. In the absence of such support, we contributed a first set of constructs that were inspired by reviewing a selection of authoritative service productivity theories. The discussed theories have in common that they identify quality as an important factor for service productivity. In addition, almost all theories emphasize the importance of customer involvement into services and include external input factors or an external view on productivity. Another aspect is the effect of the utilization of capacities on service productivity. However, the discussed theories feature a rather high level of abstraction and fall short on providing sound guidance for design science endeavors, such that much remains to be done to develop a sound ontology. 6. Acknowledgements The German Federal Ministry of Education and Research (BMBF) funded this work in the scope of the research project ServDEA, promotion sign 01FL

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