Researchers have studied employee support (eg, Mathwick et al. 2001) as an approach to improve service productivity. Service planning (Patrício et al. 2011) is another determinant of service productivity in the service process group.
External service quality and service productivity
1 shows, service encounter design and service system design are two crucial ways to improve service productivity that have received significant scholarly attention (e.g., Nakata and Hwang 2020; Shaner et al. 2016). Research related to the service process perspective therefore shows that back office improvements are an important way to improve service productivity.
Service productivity measurements’ moderating role
Service types’ moderating role
Thus, organizations that aim to strongly engage customers increase customer effort and should reward customers for any resulting inconvenience (Andreassen et al. 2018). Additionally, the theory of optimal service productivity (Grönroos and Ojasalo 2004) shows that different service industries require different degrees of customer co-production; cross-country studies show that firm competitiveness varies according to the extent to which organizations can engage with their customers (Sekhon et al. 2016), making customer co-production an essential moderator of the direct impact of service productivity determinants (Janeschek et al. 2013). However, when customer co-production is high, complexity increases and service firms need to invest significantly in employees (Yu et al. 2013) to reduce the competence gap between the service provider and the customer.
Although customer involvement and its impact on productivity are subject to debate, we propose that high customer co-production reduces the positive effects of employee productivity levers and service design on service productivity because customer co-production processes create more complexity ( Carbonell et al. 2009). Finally, we argue that the distinction between B2B and B2C services moderates the effect of some determinants of service productivity. On the contrary, for B2C services, we propose that dealing with a broader and more heterogeneous customer base requires an additional focus on service quality, employee development (Chan and Wan 2012) and corporate culture to improve service productivity. improve.
We argue that the positive service productivity effect of back office is stronger for B2B than for B2C services.
Literature search strategy
If studies reported a correlation matrix or other measures that could be converted to a correlation coefficient, we considered them for our meta-analysis. When we encountered a study that gave us reason to believe that the authors had calculated correlations but did not present any correlation data, we contacted the authors and asked for their correlation tables. In line with other meta-analyses of a similar nature, we used article classification to extract dependency and reliability data from relevant quantitative empirical studies to calculate effect sizes of the main determinants of service productivity (e.g., Babić Rosario et al. 2016).
In addition, Table 8 in the Web Appendix lists all studies included in the systematic literature review to indicate which studies were dropped (e.g., when they did not report a correlation matrix). Because we used two very large databases for keyword searches, no additional studies were added after the snowball check.
Coding procedures and coded variables
Meta‑analytic calculation
We chose a multilevel model because the covariance between all raw observed correlation scores in the study did not need to be known, as the use of between-sample variance automatically accounts for covariance (Moeyaert et al. 2017). Thus, partial correlations or studies that did not report covariances between bivariate correlations could also be used using our multilevel model.
Bivariate meta‑analytic correlations
Service efficiency = effect sizes measuring cost impact; service effectiveness = effect sizes measuring quality impact; productivity of services = effect sizes measuring the dual impact on quality and costs; k = number of studies contributing to the meta-analysis; N = total sample size for construct/subconstruct; ρ = mean weighted, reliability-adjusted 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. While for employee support, the results suggest that the supported service personalization sub-construct (ρ = .82) provides the greatest leverage to increase service productivity. For service design, service system design (ρ = .51) has the strongest average on service productivity, and for employee productivity levers, corporate culture (ρ = .62) has the highest contribution to service productivity.
Finally, for the back office determinant, the collaboration sub-construct (ρ = .34) offers the greatest potential to improve service productivity.
Moderating effects
Service type moderators Control variables Double lensIntangibilityCo-productionB2B versus B2CKQuality lensCost lensJournal qualityQm Productivity-oriented value proposition Customer feedback The bold figures show the main determinants of service productivity. We only ran regressions for subcategories for which k > 3; services productivity = nested effect sizes measuring cost, quality and (dual) productivity impact that are given the same random effect at the study level to account for measurement dependencies within studies; dual lens = both quality and cost combined (1) versus emphasis on quality or cost (0); quality lens = quality emphasis (1) versus no quality emphasis (0); cost lens = emphasis on costs (1) versus no emphasis on costs (0); intangibility = high intangible services (1) versus low intangible services (0); co-production = high customer co-production (1) versus low customer co-production (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. How is the measurement of service productivity affected by the presence of big data and different data types?
How tracking the behavior of consumers and employees through sensors or similar devices affects the productivity of the service and its measurement. How service productivity can be improved when a network of service providers co-produces services with their customers (eg within service platform ecosystems). Finally, for journal quality, we find significant effects for the associations between employee productivity levers and service productivity (β = − .22, p < .05), including employee development and service productivity (β = − .46, p < .05 ). ).
In addition, we find significant effects for the links between productivity-oriented value propositions and service productivity (β = − 0.49, p < 0.01).
Theoretical implications
Determining between different measurement contexts, our results show that when studies measure quality or cost effects in isolation, they underestimate the effect of service productivity by overlooking the link between quality and cost. Third, we test the service productivity model using a service type perspective to investigate whether there are trade-offs to consider within the service productivity model (Anderson et al. 1997). We find that customer co-production, intangibles, and B2B or B2C status of services moderate the service productivity outcome for certain service productivity determinants.
We also find that the positive service productivity effect of service design is more pronounced when services are intangible, indicating that service design is a promising avenue for service productivity improvements. Moreover, the moderator analysis of the sub-constructs reveals that the service productivity effect of a productivity-oriented value proposition becomes stronger when services are more intangible (see Table 3). Furthermore, our results provide evidence that the positive effect of back office service productivity is more beneficial in B2B contexts than in B2C contexts because B2B firms use more industrialized service processes.
This finding contributes to the service operations literature (Levitt showing that B2B organizations can increase service productivity levels by reducing organizational complexity and process variability.
Managerial implications
We therefore advise managers to use measures that combine quality and cost perspectives to implement practices that can improve the quality and efficiency of the front-end service and thus improve service productivity. Furthermore, our results show that service design influences service productivity more strongly when services are intangible. Therefore, service models offering intangible services (e.g., professional or knowledge-intensive service firms) should strategically emphasize improving service productivity through new service design, as our results provide evidence that this approach leads to higher productivity levels.
We find that the positive effect of the levers of employee productivity, service design, and external service quality on service productivity is weaker when co-production with customers is high. Therefore, for firms engaged in customer co-production, the levers of employee productivity, service design, or external service quality do not appear to significantly increase service productivity. To conclude this subsection, B2B service managers are advised to streamline their back office to further increase service productivity.
Process-based methods, such as service blueprinting (Bitner et al. 2008), are particularly useful in allocating appropriate resources, designing organizations, and predicting cost structures; therefore, they represent promising approaches to improve service productivity.
A roadmap for future service productivity research
In addition to these findings and research questions summarized in Table 4, we, third, encourage research that captures the longitudinal effects of service productivity. Our literature review revealed that a limited number of studies have so far addressed service productivity from a longitudinal perspective. Thus, service productivity research aims to empirically analyze the impact and outcomes of service productivity measures using cross-sectional data.
Because firms tend to gradually industrialize their services as they mature, an interesting future research direction would be to compare how different relationships that determine service productivity change with the life cycle of a firm. Fourth, similar to Hogreve et al. 2017), we call for more research that explains the relationships between employee support, external service quality, and service process resources used to improve service productivity. Since we primarily focused on the productivity outcomes of employee support and external service quality, as well as on service process resources, an interesting future research direction could be to analyze the effect between these determinants (e.g., using structural equation modeling techniques). .
Therefore, we strongly encourage future research to determine how service productivity output changes with varying inputs for each of the defining service productivity categories.
Limitations
Therefore, we are confident that the systematic and transparent filters we used to distill the large service productivity literature provided a representative sample that explains the traditional and emerging principles of service productivity. Harv Bus Rev Cheng CC, Krumwiede D (2012) The role of service innovation in market orientation - new service. Grönroos C, Ojasalo K (2004) Service productivity: towards a conceptualization of the transformation of inputs into economic outcomes in services.
Janeschek S, Hottum P, Kicherer F, Bienzeisler B (2013) Dynamics of service productivity and value creation: a service life cycle perspective. Menguc B, Auh S, Yeniaras V, Katsikeas C (2017) The role of climate: implications for employee engagement and customer service performance. Theoharakis V, Sajtos L, Hooley G (2009) The strategic role of relational capabilities in the inter-firm service profit chain.
Tully SM, Winer RS (2014) The role of the beneficiary in willingness to pay for socially responsible products: a meta-analysis.
