Measuring and managing service productivity: a meta-analysis

Hofmeister, Johannes; Kanbach, Dominik K.; Hogreve, Jens Article — Published Version Measuring and managing service productivity: a metaanalysis Review of Managerial Science Provided in Cooperation with: Springer Nature Suggested Citation: Hofmeister, Johannes; Kanbach, Dominik K.; Hogreve, Jens (2023) : Measuring and managing service productivity: a meta-analysis, Review of Managerial Science, ISSN 1863-6691, Springer, Berlin, Heidelberg, Vol. 18, Iss. 3, pp. 739-775, https://doi.org/10.1007/s11846-023-00620-5 This Version is available at: https://hdl.handle.net/10419/318248 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Review of Managerial Science (2024) 18:739–775 https://doi.org/10.1007/s11846-023-00620-5 1 3 ORIGINAL PAPER Measuring andmanaging service productivity: ameta‑analysis JohannesHofmeister1 · DominikK.Kanbach1,2 · JensHogreve3 Received: 28 September 2022 / Accepted: 11 January 2023 / Published online: 3 February 2023 © The Author(s) 2023 Abstract Despite service productivity’s scholarly prominence and practical relevance, past research in marketing has primarily adopted isolated perspectives from which disjointed empirical findings reign supreme. As the acquisition of knowledge about service productivity accelerates, the collective evidence becomes more interdisciplinary but also more fragmented. This study uses a meta-analysis to integrate the substantial empirical record on service productivity. We formulate hypotheses on the moderators of service productivity-determinant relationships and meta-analyze 77 articles, relying on 81 independent samples with a cumulative sample size of 30,238 participants to test our predictions. Our meta-analysis provides empirical evidence that service quality and internal efficiency must be considered jointly, not in isolation, to maximize profitability. Thus, relying on one aspect in isolation is less appropriate for measurement purposes and might not lead to positive outcomes. This important finding should concern service scholars and managers because falling profit margins require service firms to move beyond the traditional manufacturing productivity that separates service quality from internal efficiency and consider service productivity as a profitability concept. In sum, our findings provide a viable model to explain the main service productivity determinants and moderating variables, offering valuable insights for practitioners that aim to deliver cost-efficient service quality and promising future research directions. Keywords Service productivity· Service efficiency· Service effectiveness· Service excellence· Meta-analysis Mathematics Subject Classification 00A05 * Johannes Hofmeister [email protected] Extended author information available on the last page of the article 740 J.Hofmeister et al. 1 3 1 Introduction Over the last two decades, literature in marketing has shown increasing interest in the service productivity concept as a key to creating growth in a rising service economy (Anderson etal. 1997; Wirtz and Zeithaml 2018). Since service productivity emphasizes “the transformation of inputs into economic results” (Grönroos and Ojasalo 2004: 414), many successful service companies strategically manage their service productivity levels to maximize profits (Rust and Huang 2012). Yet, a conceptual fuzziness has plagued the service productivity research area where two different schools of thought argue about how service productivity should be defined and measured. One school of thought characterizes service productivity as the efficiency of a firm’s services in its ongoing operations (Anderson etal. 1997; Rust and Huang 2012), where the dual nature of quality—standardization versus customization—determines whether there are tradeoffs between satisfaction, quality on the one and firm efficiency on the other side. The second research stream argues that customer satisfaction and firm efficiency are intertwined in services and therefore considers service productivity as a joint function of firms and customers’ contributions, where firms and customers are co-creators of value (Grönroos and Ojasalo 2004; Parasuraman 2002). Consequently, these two distinct schools of thought have produced a fragmented empirical landscape, making it challenging for research on service productivity to advance with a unified understanding and greater clarity. Moreover, developing a better understanding of how service quality and cost impact service productivity within different service industries is increasingly important for managers to achieve a competitive edge (Wirtz and Zeithaml 2018). A review of the literature reveals that the literature on service productivity has significantly evolved, especially as technological advancements have continued to accelerate. Automatized implementations of service processes, such as service robots (Wirtz etal. 2018) and artificial intelligence (Huang and Rust 2021), will be increasingly important to further enhance service business models’ productivity. However, while technological advancements progress and services become pivotal for economic growth, service productivity (Baumol and Bowen 1966; Brynjolfsson 1993) is declining in many developed countries (OECD 2021), suggesting that productivity-enhancing approaches at the firm level have yet to materialize. Service scholars have only recently addressed these puzzlingly low service productivity levels (Andreassen 2021)—and paradoxically, even after more than two decades of service productivity research, the service productivity concept remains far from fully understood despite its practical relevance and scholarly prominence. We must build a more cohesive knowledge base to better understand the factors influencing service productivity. Yet, research in this field remains fragmented, dominated by siloed and context-specific studies. Some studies have 741 1 3 Measuring andmanaging service productivity: ameta-analysis analyzed the concept by focusing on specific service productivity determinants in isolation, such as service standardization (e.g., Belanche etal. 2020), technology empowerment (e.g., Marinova etal. 2017), and corporate culture (e.g., Menguc etal. 2017). These studies have primarily focused on corresponding cost or quality effects but do not refer to combined productivity measures, such as financial measures, that provide complete information about a service provider’s performance (Grönroos 1984). Since service revenues and costs are closely intertwined, the separation of cost and quality perspectives provides information about only distinct productivity determinants but makes identifying valid measures to improve a firm’s entire service productivity difficult. Moreover, other scholars examined service productivity through case studies on specific companies (e.g., Wirtz etal. 2008) or industry-specific experiments (e.g., Jung etal. 2021) and obtained context-specific results employing different terminologies. Thus, the heterogeneous terminology used to describe service productivity complicates the comparison of individual studies to draw necessary conclusions and advance the field. Furthermore, service productivity effect sizes vary considerably between studies. For example, some scholars find evidence of positive effects of service innovation on service productivity and performance (e.g., Carbonell and Rodríguez Escudero 2015; Cheng and Krumwiede 2012), while other studies are unable to support such relationships (e.g., Melton and Hartline 2013) or find contrary evidence (e.g., Aspara etal. 2018). Thus, service research requires a reliable integration of the existing research on service productivity that accounts for service heterogeneity to not only meaningfully compare studies within different industries or strategic settings but also understand related strategies’ and measures’ actual productivity effects. Essentially, the existing research on service productivity must be summarized and integrated because scholars still cannot understand the concept fully, as academic research on individual and disjoint concepts reigns supreme. In spite of this, assessing the state of knowledge in the service productivity research area has become increasingly relevant due to the growing number of publications on service productivity. Therefore, we aim to connect the literature’s fragmented empirical landscape by conducting a comprehensive meta-analysis on service productivity that includes 77 articles, 81 independent samples, and 30,238 participants. This integration of the existing research enables us to examine the current academic knowledge base to combine quantitative information from across studies, drawing solid conclusions built on comparable research and creating a cohesive foundation for further theory development. In sum, this meta-analysis aims to evaluate the evidence of effects on service productivity that are not dependent on the specifics of a single study and to provide researchers, policymakers, and practitioners with a concise synthesis of the research results. Moreover, we aim to test moderators of direct effects on service productivity, such as the way service productivity is measured or what service type was provided, that may be of particular interest to researchers, policymakers, and practitioners. Our study offers several theoretical and practical contributions. First, from a theoretical perspective, we shed light on service productivity by synthesizing existing empirical research to develop and compare the determinants of service productivity 742 J.Hofmeister et al. 1 3 based on Grönroos and Ojasalo’s service productivity model (2004). We base the meta-analysis on the Grönroos and Ojasalo model since this model reflects both firm efficiency and service quality, allowing us to equally consider the two different schools of thought characterizing service productivity either as the efficiency of a firm’s services in its ongoing operations (Anderson etal. 1997; Rust and Huang 2012) or as the joint function of internal efficiency and external effectiveness (Grönroos and Ojasalo 2004; Parasuraman 2002). Thus, we group the main service productivity determinants into three different categories (i.e., employee support productivity levers, service process productivity levers, and external service quality productivity levers) while arguing based on a synergistic (rather than single) service quality and efficiency perspective (Parasuraman 2002). Our results show that, out of the three main determinants, external service quality and employee support have the strongest positive influence on service productivity, supporting studies that have called for those determinants as important service productivity levers (e.g., Menguc etal. 2016; Phyra Sok etal. 2018). Second, we extend the literature on service productivity by analyzing the effects of the way of productivity measurement and three service-type moderators to explain the literature’s inconsistent findings. Our results show that a dual measurement approach that jointly considers quality and cost perspectives positively moderates employee support’s direct service productivity effect. Thus, our findings indicate that service companies should combine cost and quality measurements when they seek to manage total productivity to “benefit from synergies that elude service businesses focusing on a single perspective” (Parasuraman 2002: 7). This finding is important for research and practice because the meta-analysis provides empirical evidence for service scholars and managers that they should go beyond traditional manufacturing-based productivity theory and view service productivity as a profitability concept. Analyzing existing empirical literature, we show that the majority of service productivity research adopts a siloed (manufacturing-based) perspective that separates service quality from internal efficiency. Although valuable, these studies can only offer somewhat limited recommendations for a few businesses that can afford to spend more or less money (Lovelock and Wirtz 2022)—i.e., allow productivity to decrease or increase—for better or worse service quality. However, since overall service productivity declines (OECD 2021) and margins for the majority of service firms become smaller, service businesses must be able to deliver service quality that is also cost-effective, meaning they must focus mainly on profitability as a strategic decision variable. Furthermore, we find that service types [i.e., the degree of intangibility, the degree of customer coproduction, or whether services relate to business-to-business (B2B) or business-to-consumer (B2C) services] moderate service-productivity determinants’ relationships. We, therefore, propose a theoretical foundation from which to incorporate both a service-productivity-measurement perspective and a service-type perspective into the existing theory of service productivity. Finally, we suggest avenues for future research to direct the service productivity domain toward new research areas. Therefore, this meta-analysis synthesizes and compares the collective evidence on service productivity in order to motivate research to identify apt measures for service productivity improvement and shed 743 1 3 Measuring andmanaging service productivity: ameta-analysis new light on puzzlingly low service productivity levels from novel perspectives (e.g., Jung etal. 2021). We link our results to current research trends relating to new service productivity measurement approaches (e.g., Brynjolfsson etal. 2019), new service designs (e.g., Carbonell etal. 2009), and B2B services (e.g., Wirtz etal. 2015) to address the recent call for more service productivity research in an increasingly digitalized service economy (Andreassen 2021). From a managerial perspective, our results show which tradeoffs organizations must consider when they seek to improve service productivity. According to our moderator analysis, evaluating service efficiency and service effectiveness separately, instead of jointly, underestimates the service productivity effect and, consequently, misdirects managers’ decision-making. As a direct consequence of this, closely intertwined quality and cost effects cannot be steered correctly. Instead, managers should use primarily combined metrics (such as financial measures) that consider quality and cost effects in strategic decision-making because it is the key challenge for any service business to provide cost-efficient service quality. However, managing the tradeoff between cost and quality aspects is difficult in competitive markets, and only very few (world-class) service organizations achieve “quantum leaps in service quality and productivity at the same time” (Lovelock and Wirtz 2022: 513). Furthermore, our service-type moderator analyses indicate that service firms must use caution when incorporating customers into service coproduction because the associated complexity increase reduces the service productivity effects of different service-productivity enhancement approaches. Our research also shows that service design (Patrício etal. 2011) is a promising way to increase the productivity of highly intangible services. Additionally, we show that back office enhancement particularly benefits B2B companies’ service productivity. Thus, our findings encourage organizations to carefully reflect on their measurement approaches and productivity initiatives when they seek to optimize firm performance by considering the determinants and moderators discussed herein. 2 Conceptual framework 2.1 Service productivity determinants We structure existing service productivity research based on Grönroos and Ojasalo’s (2004) service productivity model. This model is most suitable for our purposes as it reflects firm efficiency and service quality equally and also considers the important “inter-linkages among various components of the company-customer perspective of productivity” (Parasuraman 2002: 6). Furthermore, we follow extant research on service productivity (e.g., Aspara etal. 2018) that also uses the definition of service productivity derived from the Grönroos and Ojasalo model. Thus, we define service productivity as the efficiency with which a firm converts service input resources into customer-valued service outputs. As such, service productivity is conceptualized and measured using combined metrics (e.g., financial return) that account for company and customer perspectives on productivity (Parasuraman 2002). In Grönroos 744 J.Hofmeister et al. 1 3 and Ojasalo’s (2004) model, service productivity determinants are separated into an input perspective, a service process perspective, and an output perspective. The input perspective refers to employee support that enables service providers to deliver better services to customers (i.e., firm inputs such as personnel, systems, and technology, as well as customer inputs such as time and effort). The service process perspective refers to how these inputs are transferred into outputs (i.e., employee productivity levers, service design, or back office enhancement), whereas the output perspective refers to the external service quality aspect (e.g., customer perceived quality). Figure1 shows that our framework also features sub-constructs for employee support, the service process, and the external service quality dimensions so that we can delve deeper into each of the determinants’ drivers. Table1 lists all of the constructs’ definitions and most representative articles. 2.1.1 Employee support andservice productivity Researchers have investigated employee support (e.g., Mathwick etal. 2001) as an approach for service productivity enhancement. The employee support service-productivity determinant refers to all business-model decisions that support employees during customer interactions (Lechner and Mathmann 2020; Menguc etal. 2020) that directly influence service productivity. Figure1 shows that employee support can be improved in four different ways. First, service productivity can be achieved through systems for support in customer interfaces by reducing complexity, mainly through self-services (Belanche etal. 2020), service scripts (Victorino etal. 2012), and new technologies (Schepers etal. 2011) at the front line. Second, service productivity can be improved through enhanced customer relationship management support. Fostering (e.g., favoring longstanding customer relationships) long-term relationships with customers helps increase the quality of the relationships between Fig. 1 Meta-analytic framework 745 1 3 Measuring andmanaging service productivity: ameta-analysis Table 1 Construct definitions, common aliases, and representative studies Construct Definition Common aliases Most representative study Employee support Ways to enable employees to deliver better results to customers Systems for support Complexity reduction at the customer interface through service standardization Service augmentation, self-services Collier and Barnes (2015) Customer relationship management support Marketing and communication methods to establish long-term customer relationships Customer linking, customer orientation, service recovery performance Suhartanto etal. (2018) Assisted service personalization Add-on services to achieve highly customized services Inter-functional coordination, customer, competitor orientation Mathwick etal. (2001) Value co-design assistance Customer education and mutual learning experiences to achieve high service productivity Customer involvement, playfulness, customer readiness, assistive intent Zhao etal. (2018) Employee productivity levers Ways to improve the service workforce Employee development Development of employee competencies to increase service efficiency and/ or effectiveness Self-efficacy, employee productivity, trust, empowerment, education, personality Phyra Sok etal. (2018) Corporate culture Firm culture that promotes high service productivity Cross-selling initiative climate, service climate, supervisory guidance Menguc etal. (2017) Leadership Leadership styles required in a competitive service environment to foster trust and achieve interest alignment Rewards, developmental feedback, monitoring, leader autonomy support Jung etal. (2021) Talent selection Personality traits that promote high service productivity Social skills, agreeableness, conscientiousness, emotional stability Doucet etal. (2016) Service design Ways to improve the service design Service encounter design Activities that promote incremental service design changes at the frontend Information recording and reviewing, Information use, innovation incentives Aspara etal. (2018) Service system design Activities that promote high (radical) multilevel service design changes Abductive reasoning, experimentation, learning by failing, radical innovation Nakata and Hwang (2020) Back office Ways to streamline the back office (in isolation from the customer) 746 J.Hofmeister et al. 1 3 Table 1 (continued) Construct Definition Common aliases Most representative study Cooperation Supplier structures and balanced partnerships to achieve processual synergies Cooperation, supplier collaboration Heirati etal. 2016 Steering Steering models that break up silos and thereby employ a better processual business acumen among staff Implementation, measurement, analysis Olsen etal. (2014) Service productization Premises for target operating models that will help focus equally on efficiency and customer orientation Adoption level of customized IT, IT infrastructure, modular independence, service architecture Strydom etal. (2020) External service quality Ways to improve the value proposition or brand Cost-oriented value proposition Cost-oriented value proposition focusing on the core services offered Market entry time in cost-oriented market, focus on core services Pingjun Jiang and Talaga (2006) Quality-oriented value proposition Quality-oriented value proposition in which services satisfy customer needs entirely Social interaction quality, loyalty to the service provider, service expectations Habel etal. (2016) Productivity-oriented value proposition Dual quality and cost perspective from which modular services are offered at competitive prices Flexibility, market orientation implementation, learning orientation Calabuig etal. (2014) Customer feedback Customer feedback gathering, utilization, and monetarization Incorporation of customers personal needs, interpretation of E-SERVQUAL Herington and Weaven (2009) Dual emphasis Articles measuring service productivity by focusing on quality and cost impacts, by either measuring quality and costs combined through service productivity or measuring the perceived service quality and service cost separately within one study Market performance, service performance, firm performance, sales performance, new service performance, proactive service performance Aspara etal. (2018) Quality emphasis Articles measuring the quality impact by measuring only perceived service quality Customer satisfaction, customer perceived value, perceived service performance Phyra Sok etal. (2018) 753 1 3 Measuring andmanaging service productivity: ameta-analysis led to the identification of a total of 179 articles. Next, the articles were classified according to their methodological approach. If studies reported a correlation matrix or other measures that could be converted into a correlation coefficient, we considered them for our meta-analysis. When we came upon a study that gave us cause to believe that the authors had calculated correlations but had not presented any correlation data, we reached out to the authors to inquire about their respective correlation tables. In accordance with other meta-analyses of a similar nature, we used the classification of the articles to extract dependency and reliability data from the relevant quantitative empirical studies in order to compute the effect sizes of the main service-productivity determinants (e.g., Babić Rosario etal. 2016). Finally, we identified 77 articles, including 81 independent samples with a cumulative sample size of 30,238 participants, to test our model. Table5 in the Web Appendix list all studies included in this meta-analysis. Furthermore, Table8 in the Web Appendix lists all studies included in the systematic literature review to show which studies have been dropped (e.g., when they did not report a correlation matrix). Since we used two very large databases for keyword searches, no additional studies have been added after the snowballing check. 3.2 Coding procedures andcoded variables Our categorization and coding of the reviewed articles followed a structured approach. We first condensed the individual articles’ information, described as common aliases in Table1. Second, using this information, we combined articles based on their links and interactions to form construct sub-groups. Third, all of the current study’s authors further abstracted the information to cluster the sub-groups, based on the theory’s main perspectives on optimal service productivity, to finally form constructs representing the main service productivity-determinant-relationships, as Table1 shows. Fourth, for each study, we used the study samples’ industry information to determine the service-type moderators and differentiate between studies based on levels of service intangibility, customer coproduction, or business models (i.e., B2B versus B2C services). To determine the measurement moderators, we recorded the articles’ information to see whether the researchers of the examined studies had measured quality and cost effects combined through service productivity or by measuring perceived service quality and service cost effects separately or jointly. Finally, each of the current study’s authors reevaluated our coauthors’ coding assessment to achieve reliability and reduce individual bias. The final intercoder reliability was 90%, and differences in opinion were quickly resolved. Table5 in the Web Appendix shows the coding protocol, and Table7 in the Web Appendix shows how we generally coded different industries. Furthermore, Table1 displays the definitions of the service productivity determinants and moderators. To account for the reviewed studies’ individual cost and quality measurement effects, we integrated two measurement control variables. With the help of these variables, we tested whether either quality or cost measurement effects were 754 J.Hofmeister et al. 1 3 significant if a dual emphasis were not to ensure the robustness of our results. Additionally, we controlled journal quality using existing journal quality ratings. Table3 illustrates the measurement, service-type moderators, and control variables. 3.3 Meta‑analytic calculation We gathered each study’s raw observed correlations and corrected their bivariate correlations for measurement errors using reliability scores. If a study did not provide reliability scores, we used the average weighted reliabilities from studies referring to the same service-productivity determinant (Schmidt and Hunter 2015). Additionally, we transformed the correlation coefficients into Fisher’s Z effect sizes to ensure that different studies’ population effect sizes were randomly drawn from a normal distribution in order to account for the significantly varying effect sizes in some studies (Tully and Winer 2014). Furthermore, we weighted each effect size by its inverse variance (Babić Rosario etal. 2016) to calculate the average weighted reliability-corrected correlations (ρ)1 to smooth studies’ highly varying number of participants and reduce heteroscedasticity. To avoid overestimating the population value of z, we transformed the average weighted reliability-corrected correlations (ρ)2 back into their correlational form (Silver and Dunlap 1987). If a study reported more than one outcome measure, we built separate effect sizes for service productivity, external effectiveness, and internal efficiency to distinguish between the three main service productivity perspectives. We also calculated the standard deviation of the corrected correlations (SD) and their 95% confidence interval (95% CI). Finally, we calculated the Q homogeneity statistic to analyze whether moderating effects were present. For the moderator analysis, we simultaneously3 regressed the average weighted reliability-corrected correlations (ρ) on the defined moderator variables (Zablah etal. 2012) via a multilevel meta-analysis approach (Viechtbauer 2010). Within the multilevel regression model, correlations referring to the same study, sample, or service productivity measure were treated with the same random effect to account for the dependencies between multiple outcomes in a study, while all other studies, samples, and outcomes were assumed to be independent. These nested random 1 Akin to Babić Rosario etal. (2016), we calculated the weight w as follows: wi = 1/ (se2zt + v i), where se is the standard error of the effect size, which is calculated as sezt = − 1/ √ (n −3), and v i is the random-effects variance component. The average weighted reliability-corrected correlations ρ were calculated as follows: ρ = ∑ (w × zr)/ ∑w, where zr refers to the Fisher’s Z effect size. The standard error for ρ was calculated as follows: seρ = √ (1/ ∑w); the 95% CIρ confidence intervals were computed as follows: lower CI = ρ – 1.96 (seρ)/ upper CI = ρ + 1.96 (seρ). 2 The transformation of the average weighted reliability-corrected correlations back into a correlational form was calculated as follows: ρ = (e2z−1)/ (e2z + 1). 3 We also individually regressed the average weighted reliability-corrected correlations (ρ) of the different moderator variables to determine whether our results were stable. None of the effects changed except for the service productivity-service design determinant relationship. Here, the results lost statistical significance because we had to leave out industry-agnostic studies that did not allow for a service-type moderator classification. Thus, the findings for this service productivity determinant are subject to further scrutiny, and more research is needed to see whether our assumptions hold. 755 1 3 Measuring andmanaging service productivity: ameta-analysis effects are very helpful for modeling the dependence induced by outcomes derived from the same article or sample (Konstantopoulos 2011). We chose a multilevel model because the covariance between all raw observed correlation scores within a study did not need to be known since using between-sample variance automatically accounts for covariance (Moeyaert etal. 2017). Thus, by applying our multilevel model, we could also use partial correlations or studies that did not report the covariances between bivariate correlations. 4 Results 4.1 Bivariate meta‑analytic correlations Table2 displays the meta-analytic correlations of the service productivity-determinant relationships where we measured the effect sizes of the cost impact, the quality impact as well as the combined dual quality and cost impact (i.e., service productivity impact) to provide full transparency on all three perspectives defined in Grönroos and Ojasalo’s (2004) service productivity model. Our results show that the corrected service productivity effect sizes (ρ) for external service quality (ρ = .59) and employee support (ρ = .47) are the highest, followed by the service process (ρ = .32), which is further separated into service design (ρ = .37), employee productivity levers (ρ = .30), and back office (ρ = .30). Furthermore, all main service productivity determinants are significant at p < .05, as the results of our fail-safe N calculation (which refers to the number of studies required to refute significant meta-analytic results) using the Rosenthal approach (nfs) indicated. Thus, our sample is robust and resistant to a file drawer threat (these results have also been validated by checking the respective funnel plots) even though we did not collect unpublished research because our sample size is sufficient to entirely cover the most important literature on service productivity. Furthermore, comparable meta-analyses had similar sample sizes (e.g., Gelbrich and Roschk 2011; Tully and Winer 2014). As anticipated, the standard deviations of the mean true score correlations (95% CI) were relatively high, demonstrating that the dependencies could be moderated by different variables. Consequently, we calculated the Q homogeneity statistic of ρ. Since all main Q-tests were significant, we assume that the true effects are heterogeneous and potentially moderated by different variables. Overall, the results of our bivariate meta-analytic correlations show that, of the three categories of the service productivity model, the categories related to employee support and external service quality levers have a stronger direct impact on service productivity than service process levers (i.e., employee productivity levers, service design, and back office). More specifically, the results show that the service productivity effect sizes (ρ) for external service quality (ρ = .59) and employee support (ρ = .47) are the highest, followed by the corresponding effect sizes for service process levers (i.e., service design (ρ = .37), employee productivity levers (ρ = .30), and back office enhancement (ρ = .30)). In addition, Table2 shows, we also examined the service productivity effect sizes of the sub-constructs for each of the five main determinants. For external service quality, the quality-oriented value proposition 756 J.Hofmeister et al. 1 3 Table 2 Meta-analytic correlations Descriptives Service efficiency (cost impact) Service effectiveness (quality impact) Service productivity (combined cost and quality impact) Test k N ρ SD 95% CI ρ SD 95% CI ρ SD 95% CI nfs Q Overall 77 30,238 Employee support 12 12,225 − .04 .05 [− .14, .07] .51 .06 [.38, .63] .47 .05 [.26, .57] 12,377 612.88*** Systems for support 1 350 − .04 .05 [− .14, .07] 0 Customer relationship management support 5 1863 .37 .07 [.23, .52] .27 .07 [.14, .40] 563 110.71*** Assisted service personalization 3 1450 .66 .08 [.51, .82] .82 .06 [.38, .91] 2052 179.13** Value co-design assistance 3 8562 .48 .03 [.42, .55] .33 .03 [.26, .39] 1808 80.74*** Service process 55 14,313 .36 .28 [− .19, .92] .26 .10 [− .07, .47] .32 .06 [.20, .45] 20,415 919.32*** Employee productivity levers 33 10,207 .20 .11 [− .01, .41] .30 .07 [.18, .44] 6531 611.53*** Employee development 12 4601 .12 .11 [− .09, .33] .43 .06 [.30, .55] 474 264.20*** Corporate culture 9 3041 .31 .09 [.14, .49] .62 .06 [.50, .74] 1681 288.72*** Leadership 6 1417 .18 .09 [.00, .35] .01 .09 [− .14, .21] 69 15.56*** Talent selection 6 1148 .20 .14 [.08, .48] .16 .05 [.07, .26] 78 33.47*** Service design 12 2410 .44 .12 [.23, .71] .37 .05 [.28, .46] 1190 99.63*** Service encounter design 10 1863 .44 .12 [.23, .71] .23 .06 [.12, .34] 572 50.74*** Service system design 2 547 .51 .04 [.44, .58] 110 22.17*** Back office 10 1696 .36 .28 [− .19, .92] .25 .08 [.10, .41] .30 .07 [.15, .44] 737 173.50*** Cooperation 3 504 .55 .08 [.38, .71] .34 .08 [.19, .49] 186 25.85*** Steering 1 320 .28 .04 [.20, .36] 9 0 Service productization 6 872 .36 .28 [− .19, .92] − .04 .07 [− .19, .10] .27 .11 [.06, .49] 104 108.71*** External service quality 10 3700 .33 .09 [.17, .54] .59 .11 [.38, .81] 1746 497.60*** Cost-oriented value proposition 1 151 .00 .00 [.00, .00] .23 .12 [.00, .46] 2 0 Quality-oriented value proposition 3 956 .53 .08 [.36, .69] .89 .14 [.62, 1.0] 560 33.87*** Productivity-oriented value proposition 4 1949 .15 .10 [.03, .40] .66 .08 [.51, .82] 84 190.34*** 757 1 3 Measuring andmanaging service productivity: ameta-analysis Table 2 (continued) Descriptives Service efficiency (cost impact) Service effectiveness (quality impact) Service productivity (combined cost and quality impact) Test k N ρ SD 95% CI ρ SD 95% CI ρ SD 95% CI nfs Q Customer feedback 2 644 .33 .10 [.13, .52] .00 .00 [.00, .00] 49 .40 The bold figures show the main service productivity determinants.Service efficiency = effect sizes measuring the cost impact; service effectiveness = effect sizes measuring the quality impact; service productivity = effect sizes measuring the dual quality and cost impact; k = number of studies contributing to meta-analysis; N = total sample size for construct/ subconstruct; ρ = average weighted reliability-corrected correlations; SD = standard deviation of ρ; 95% CI = 95% confidence interval around ρ; nfs = Fail-save N calculation using the Rosenthal approach; Q = homogeneity statistic of ρ ***p < .01; **p < .05; *p < .10 758 J.Hofmeister et al. 1 3 sub-construct (ρ = .89) has the strongest service productivity effect. Whereas for employee support, the results suggest that the assisted service personalization subconstruct (ρ = .82) offers the greatest leverage to increase service productivity. For service design, service system design (ρ = .51) presents the strongest service productivity mean, and for employee productivity levers, corporate culture (ρ = .62) has the highest contribution to service productivity. Lastly, for the back office determinant, the cooperation sub-construct (ρ = .34) offers the biggest potential to improve service productivity. In sum, these findings reply to the initial call of Grönroos and Ojasalo’s (2004: 422) to test the “relative importance of the various components” of their model and therefore provide an important agenda for further research (see Table4 and our section on the discussion of future research). 4.2 Moderating effects Regarding the boundary conditions of the service-productivity-determinant–outcome relationship, we examined different moderating effects. Table3 displays the quantitative results of the associated meta-regressions, and Table 4 summarizes and explains those results. First, the results show that Hypothesis 1 is supported, such that the positive effect of employee support (β = .96, p < .05) is stronger when service quality and internal efficiency effects are measured jointly instead of separately. According to the findings of our study, using a dual lens that considers the effects of both quality and cost positively moderates the direct service productivity-determinant relationships. This finding suggests that when studies separately measure quality and cost effects, they underestimate the service productivity effect because they fail to account for the link between quality and costs. However, alternative explanations may challenge our measurement findings. We cannot assume that our meta-analytic measure of service productivity truly represents the actual service productivity measure that has guided the researchers and their studies within our sample since the most appropriate measure of service productivity (for most studies) would seem to be dependent on the purpose and goals of a given research project. Despite this, we looked at the specific characteristics of the various measurement techniques to determine whether researchers measured quality and cost effects combined through service productivity or whether they measured perceived service quality and service cost effects separately or jointly (see Table1). Supporting Hypothesis 2, our findings show that the positive effect of service design is stronger when services are intangible (β = .47, p < .01), confirming our initial theorizing that service firms offering highly intangible services can improve productivity through a new service design. Regarding Hypothesis 3, we find that the positive service productivity effect of employee productivity levers (β = − .20, p = .09) and service design (β = − .30, p = .06) is weaker when customer coproduction is high, which suggests that high customer coproduction might indeed increase complexity, in turn reducing the service productivity effect. Regarding Hypothesis 4, we find that the positive service productivity effect of back office enhancement is stronger for B2B services than for B2C services (β = .70, p < .05), which further 759 1 3 Measuring andmanaging service productivity: ameta-analysis Table 3 Moderator analysis Moderators Intercept Measurement moderator Service-type moderators Control variables Dual lens In-tangibility Co-production B2B versus B2C Quality lens Cost lens Journal quality Qm Employee support → service productivity − .31 .96** .28 .59 − .07 11.65** Systems for support Customer relationship management support .25 .18 .25* 3.11 Assisted service personalization Value co-design assistance Service process → service productivity .23 .03 .11 − .14 .16 .04 .12 − .18 12.39* Employee productivity levers → service productivity .10 .24 .03 − .20* .03 − .22 − .22** 7.96 Employee development .44*** .07 − .06 − .43* − .46** 7.88* Corporate culture .21 .34 .37 − .56 − .06 .10 2.96 Leadership .30 .13 − .12 − .14 − .14 1.20 Talent selection .07 .08 − .44 .40** − .02 4.86 Service design → service productivity .13 .47*** − .30* .29** 31.02*** Service encounter design .13 .47*** − .30* .29** 31.02*** Service system design Back office → service productivity .72* − .70 .26 .03 .70** − .64 .35 .20 9.21 Cooperation Steering Service productization .02 .27 .69 .34 .19 2.69 External service quality → service productivity .54* .17 .28 − .50* − .31 − .28 4.90 Cost-oriented value proposition Quality-oriented value proposition 760 J.Hofmeister et al. 1 3 Table 3 (continued) Moderators Intercept Measurement moderator Service-type moderators Control variables Dual lens In-tangibility Co-production B2B versus B2C Quality lens Cost lens Journal quality Qm Productivity-oriented value proposition .01 .71*** .25*** − .49*** 121.96*** Customer feedback The bold figures show the main service productivity determinants. We only ran regressions for sub-categories for which k > 3; service productivity = nested effect sizes measuring the cost, quality, and (dual) productivity impact that receive the same random effect on a study level to account for measurement dependencies within studies; dual lens = both quality and cost combined (1) versus quality or cost emphasis (0); quality lens = quality emphasis (1) versus no quality emphasis (0); cost lens = cost emphasis (1) versus no cost emphasis (0); intangibility = high intangible services (1) versus low intangible services (0); coproduction = high customer coproduction (1) versus low customer coproduction (0); B2B versus B2C = B2B services (1) versus B2C services (0); journal quality = high journal quality (1) versus low journal quality (0) ***p < .01; **p < .05; *p < .10; Qm = test for residual heterogeneity 761 1 3 Measuring andmanaging service productivity: ameta-analysis Table 4 Summary of main findings and future research questions based on the study results Hypothesis Findings Explanation Potential future research questions unfolding from our findings Hypothesis 1 Supported The positive service productivity effect of employee support is stronger when service productivity is measured using combined metrics that consider cost and quality effects The literature suggests that measurements and associated ways to improve employee support should focus on service quality since customers are pivotal drivers of firm profit (see Rust etal. 1995); however, to improve service productivity overall, productivity measures must consider the interrelationship between service quality and costs because they cover service productivity more accurately and therefore allow for a more productive steering of subsequent implementation efforts What changes if the actual productivity measurement approaches will be transferred to the digital world? How can policy makers and scholars more accurately measure the productivity contribution of (free) digital services–compared to other services’ contribution–to better understand what drives the puzzling low service productivity levels of major service economies? How do (dual) measurement approaches change when industry-specific strategies transform toward more technology-oriented applications? How is service productivity measurement affected by the presence of big data, different data types? How does the tracking of consumer and employee behaviors via sensors or similar devices affect service productivity and its measurement? Hypothesis 2 Supported The positive service productivity effect of service design is stronger when services are highly intangible When services are highly intangible, they are typically more knowledge intensive and lack sufficient potential to reduce internal costs. Thus, service design offers opportunities for service productivity improvements as innovative service designs can meet customer expectations more effectively How does service productivity differ for design efforts that either exploit or explore new opportunities for firms offering intangible services? How do usability-oriented, experience-oriented, or context-oriented service design configurations affect the productivity of intangible services? 762 J.Hofmeister et al. 1 3 Table 4 (continued) Hypothesis Findings Explanation Potential future research questions unfolding from our findings Hypothesis 3 Supported The positive service productivity effect of employee productivity levers and service design is weaker when customer coproduction is high High customer coproduction reduces the positive service productivity effects of employee productivity levers and service design because customer-coproduction creates more complexity How can firms educate customers to successfully outsource non-productive service tasks? Should firms invest in specific employee productivity levers or service designs on the customer side to lever productivity? How can service productivity be improved when a network of service providers coproduce services with its customers (e.g., within service-platform ecosystems)? Hypothesis 4 Supported The positive service productivity effect of the back office is stronger for B2B services than B2C services B2B service productivity requires operational excellence; reducing organizational and processual slack effectively increases service productivity What part of the service economy’s growth can be explained by either B2C or B2B service productivitycontribution? How can streamlining the back office help overcome B2B firms’ service productivity barriers? 769 1 3 Measuring andmanaging service productivity: ameta-analysis field, our initial keyword selection cannot cover all available studies even though our literature review (Snyder 2019) and meta-analysis (Schmidt and Hunter 2015) followed a thorough and comprehensive approach. Therefore, we are confident that the systematic and transparent filters we used to distill the vast literature on service productivity yielded a representative sample explaining service productivity’s traditional and new tenets. In conclusion, we reiterate that this article offers important contributions to advance the theory of optimal service productivity, suggesting that the most suitable way of measuring it is via financial measures combining quality and cost effects (Grönroos and Ojasalo 2004). By assessing the theory of optimal service productivity in different contexts, we have refined established theoretical measurement assumptions and developed novel ones for different employee support and external service quality as well as service process perspectives to advance knowledge in a field that deserves further research. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1184602300620-5. Funding Open Access funding enabled and organized by Projekt DEAL. Data availability All data generated or analyzed during this study are included in this published article. Declarations Conflict of interest The authors have no competing interests to declare that are relevant to the content of this article. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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Kanbach [email protected] Jens Hogreve [email protected] 1 HHL Leipzig Graduate School ofManagement, Jahnallee 59, 04109Leipzig, Germany 2 School ofBusiness, Woxsen University, Hyderabad, India 3 Ingolstadt School ofManagement, Catholic University ofEichstaett-Ingolstadt, Auf der Schanz 49, 85049Ingolstadt, Germany