Revenue and Profit Implications of Industrial Service Strategies Keywords: Industrial service strategies, financial performance, services supporting the supplier’s product (SSPs), services supporting the clients’ actions (SSCs), latent growth curve modeling Andreas Eggert (corresponding author) University of Paderborn Marketing Department Warburger Strasse 100 33098 Paderborn, Germany Phone: +49 - 5251 60 20 85 Email:
[email protected] Jens Hogreve Catholic University of Eichstätt-Ingolstadt Professor of Service Management Ingolstadt School of Management Auf der Schanz 49 85049 Ingolstadt, Germany Phone: +49-841 937 1861 Email:
[email protected] Wolfgang Ulaga IMD Professor of B2B Marketing Chemin de Bellerive 23 1001 Lausanne, Switzerland Phone: +41 - 21 618 02 40 Email:
[email protected] Eva Muenkhoff University of Paderborn Marketing Department Warburger Strasse 100 33098 Paderborn, Germany Phone: +49 - 5251 60 20 88 Email:
[email protected] Paper forthcoming in the Journal of Service Research Acknowledgement: The authors thank the participants of the Frontiers Pre-Conference on Service and Solution Innovation 2010 (Karlstad, Sweden), the participants of the ISBM Academic Conference 2010 (Boston, MA), and the participants of the AMA Winter Marketing Educators’ Conference 2011 (Austin, TX) for their helpful comments on previous versions of this article. The authors also thank Robert W. Palmatier for his valuable comments on this research project.
1 Abstract In many business markets, manufacturers seek service-led growth to secure their existing positions and continue to grow in increasingly competitive environments. Using longitudinal data from 513 German mechanical engineering companies and latent growth curve modeling, this study offers a fine-grained view of the financial performance implications of industrial service strategies. By disentangling the revenue and profit implications of industrial service strategies, findings reveal that such strategies increase both the level and the growth of manufacturing firms’ revenue streams. In contrast, they reduce the level but improve the growth of manufacturers’ profits. Results further suggest that services supporting the clients’ actions (SSC) and services supporting the supplier’s product (SSP) affect performance outcomes in different ways. SSCs directly affect revenue and profit streams. In turn, SSPs display only indirect effects on financial performance, mediated through SSCs. A moderator analysis identifies two organizational contingencies that facilitate service business success: Only companies with decentralized decision-making processes and a high share of loyal customers can expect favorable financial results from industrial service strategies. In summary, this research provides significant insights and managerial guidance for turning service strategies into financial successes.
2 Manufacturing companies increasingly seek service-led growth to secure existing positions and expand in business markets (Ostrom et al. 2010). Many suppliers indeed rely heavily on an installed base and may derive substantial revenues and profits from providing services over their products’ life cycles (Potts 1988). Service revenues frequently offer healthy profit margins that help compensate for the declining revenues and profitability in equipment sales (Reinartz and Ulaga 2008). Moreover, services might stabilize cash flows and provide increased visibility in revenue streams, a key benefit in economic slumps (Oliva and Kallenberg 2003; Fang, Palmatier, and Steenkamp 2008). Extending the service business thus promises greater firm revenues and profits (Wise and Baumgartner 1999). Yet increasing anecdotal evidence indicates mixed results at best. For example, according to a Bain & Co. study, only 21% of companies succeed with service strategies (Baveja, Gilbert, and Ledingham 2004). Goods-centric companies that enter service markets often cannot outperform their pure product counterparts in terms of revenue growth, profit margins, or returns on equity. Stanley and Wojcik (2005) find that half of all solution providers realize only modest benefits, and 25% actually lose money with their value-added services. Neely (2008) provides evidence that manufacturing firms offering industrial services enjoy higher revenues than traditional manufacturing firms, but they also generate lower profits. Take the example of Michelin, a leading manufacturer of truck tires. When the company launched its innovative offer Michelin Fleet Solutions (MFS), i.e., performance-based contracts aimed at selling kilometers instead of tires, the goods-centric company moved into the new world of services, which provided Michelin with a chance to differentiate itself in the mature tire industry. However, initial sales were disappointing, and costs exceeded projections, seriously jeopardizing the sustainability of the company’s innovative go-to-market approach. It took Michelin several years to understand the root cause of its financial difficulties: To be profitable,
3 MFS required a different business model, changes to the organizational structure, and a careful selection of targeted customers to generate satisfactory revenue while keeping service provision costs in check (Renault, Dalsace, and Ulaga 2010). Empirical research on the financial implications of industrial service strategies is still at an early stage. Most studies emphasize positive outcomes and focus on single measures of financial success, such as revenues, profits, or firm value (Antioco et al. 2008; Fang, Palmatier, and Steenkamp 2008; Gebauer 2007; Homburg, Fassnacht, and Guenther 2003). Yet, for a more fine-grained understanding of the financial implications of industrial service offerings, we need empirical research that specifically sheds light on both its revenue and profit effects in a single analytical framework. Such a framework needs to acknowledge two simultaneous challenges. First, manufacturing firms must create customers’ willingness to pay for their service offerings. Second, they must manage the costs of the service provision to turn service revenues into profits. Revenues and profits are interdependent, but they do not necessarily evolve in the same direction. Therefore, by disentangling the revenue and profit implications of industrial service strategies, we can capture implicitly the costs of service provision. A longitudinal perspective is needed to comprehensively explore the financial consequences of industrial service offerings. Moving into services typically represents an investment, which initially might lead to lower profitability levels for companies focusing on services. Over time though, industrial services might increase the rate at which profits grow causing companies with extensive industrial service offerings to realize financial advantages. To date, empirical research has not yet separated these financial implications. Understanding the effects of industrial services on the development of financial performance thus helps industrial suppliers better prepare for and effectively manage their service business.
4 Furthermore, with a few exceptions, empirical investigations treat industrial services as homogeneous. Yet evidence suggests a need for a differentiated view of how various service categories relate to firm performance (Antioco et al. 2008; Mathieu 2001; Ulaga and Reinartz 2011). We distinguish between services supporting the supplier’s product (SSPs) and services supporting the clients’ actions (SSCs) as one tactic to investigate how different types of services affect manufacturing companies’ revenue and profit trajectories. Finally, moderator variables, such as industry and service business characteristics, may affect the financial outcomes of industrial services (Antioco et al. 2008; Fang, Palmatier, and Steenkamp 2008). We build on this stream of research by shedding light on the moderating role of two organizational design characteristics, decentralization of decision-making authority and share of loyal customers. We contribute to service research and practice in three ways. First, by employing longitudinal data from the German mechanical engineering industry and a latent growth curve modeling approach, we provide a detailed view of the effects of industrial service strategies on the level and the growth rate of manufacturing companies’ revenues as well as profits. Although manufacturers with a broad industrial service portfolio possess higher levels and growth rates of revenue streams, they have lower levels but still enjoy higher growth rates of profit streams. Second, our results underscore the need to distinguish industrial service types. For example, SSCs directly affect manufacturers’ revenue and profit streams, whereas SSPs only display an indirect impact. Third, we contribute to a better understanding of the conditions in which industrial service strategies result in revenue and profit growth. According to our moderation analysis, the positive effect of SSCs on revenues and profit growth is strongest when manufacturers also exhibit significant decentralization of decision-making authority and possess a high share of loyal customers.
5 In the remainder of our article, we first review extant literature on the link between industrial service strategies and firm performance and provide a rationale for our distinction between SSPs and SSCs in industrial markets. Drawing on a resource-based perspective, we then develop our hypotheses, present the methodology, specify our model, and present our empirical findings. Finally, we discuss implications for practitioners and scholars, and conclude with limitations and research directions. LITERATURE REVIEW Industrial Service Strategies and Firm Performance Industrial service strategies have become an important topic in both the business-tobusiness (B2B) and service marketing fields (Jacob and Ulaga 2008; Kunz and Hogreve 2011; Ostrom et al. 2010). However, empirical research is still nascent and focused on two issues: (1) the effect of industrial service strategies on firm performance and (2) the identification of variables that moderate the effect of industrial service strategies on firm performance. Regarding the effect of industrial service strategies, empirical studies tend to emphasize positive outcomes (see Table 1), such as the positive effects on relative product sales (e.g., Antioco et al. 2008), service profitability (Homburg, Fassnacht, and Guenther 2003), and overall profitability (Gebauer 2007). However, in their analysis of the link between service revenue share and firm value, Fang, Palmatier, and Steenkamp (2008) confirm that industrial service strategies can also have negative effects, such as decreasing firm value at the beginning of the service transition. These authors also show that the effect becomes positive once the firm’s service share exceeds 20–30% of its total sales. In addition, several empirical studies test the assumption that performance outcomes depend on the alignment between the industrial service strategy and contextual variables. We find two types of moderating contextual variables in prior studies: industry or service business
6 characteristics. Fang, Palmatier, and Steenkamp (2008) demonstrate that industry growth has a negative effect on the service–performance link, but industry turbulence has a positive effect. With regard to service business characteristics, they identify service relatedness (the extent to which services are related to the firm’s core product business) and the availability of slack resources as positive moderators of the service–performance link. In addition, the service orientation of human resource management and corporate culture have attracted scholars’ attention (Antioco et al. 2008; Homburg, Fassnacht, and Guenther 2003). Both variables positively moderate the link between industrial services and firm performance. Management commitment to the service business and cross-functional communication between the service division and other departments foster positive effects of industrial service offerings too (Antioco et al. 2008), as we summarize in Table 1. – Insert Table 1 about here – This review reveals two main gaps. First, previous research has not separated the impact of industrial service strategies on the level versus the growth of firms’ revenue and profit trajectories. Doing so would provide a finer-grained view of the differential financial implications of industrial service strategies. Second, we need to gain a better understanding of the conditions under which industrial services contribute to financial success. In particular, previous research has not explored whether overall company characteristics (e.g., level of decentralization, share of loyal customers) support or hinder service success. Such insights could provide managers with actionable recommendations about how they should integrate services into their business logic to achieve overall revenue and profit growth. Industrial Service Types With the term “industrial services,” we refer to all services provided by manufacturing companies to organizational customers, irrespective of whether that service is independent or
7 combined with the companies’ goods (Homburg and Garbe 1999). Such services are not homogeneous: They differ substantially in their level of risk, level of competition, and potential to create competitive advantages (Oliva and Kallenberg 2003). Traditionally, industrial services have been classified according to stages of the industrial purchasing process (Samli, Jacobs, and Wills 1992). Frambach, Wels-Lips, and Guendlach (1997) suggest that product services can be classified as either transaction or relationship related. Boyt and Harvey (1997) instead call for a more fine-grained classification that distinguishes between elementary, intermediate, and intricate services according to six service characteristics: replacement rate, essentiality, risk level, complexity, personal delivery, and credence properties. Although their classification seems straightforward and plausible, Boyt and Harvey (1997) provide no rationale for using these criteria as a basis for categorizing industrial services. Mathieu’s (2001) service classification scheme distinguishes between services supporting the supplier’s product (SSPs) and services supporting the client’s actions (SSCs). The former support the installation and use of the supplier’s core products and ensure their proper functioning (Mathieu 2001). Thus SSPs typically include services, such as installation, product inspections, equipment repair or maintenance. The latter service category instead refers to the client’s action in relation to the supplier’s product. These services typically include offerings, such as process optimization, research and development, business consultancy, or the operation of entire processes on the client’s behalf. A number of qualitative and survey-based studies have relied on this classification scheme. For example, Antioco et al. (2008) distinguish between SSPs and SSCs to capture manufacturers’ industrial service strategies and their antecedents and outcomes. In the same vein, Ulaga and Reinartz (2011) differentiate SSPs and SSCs in a qualitative study of manufacturing companies that move from goods-centric to service-centric business models. These authors show
8 that SSPs and SSCs draw on different critical resources and capabilities. In addition, they explain how distinctive capabilities in each of these categories lead to different positional advantages, i.e., differentiation and cost leadership. Collectively, these prior studies suggest that it is reasonable to distinguish between SSPs and SSCs, because the distinction is not only grounded in managerial practice but helps explain differences in antecedents and outcomes of industrial service strategies. HYPOTHESES DEVELOPMENT We draw on the resource-based view (RBV) to analyze the revenue and profit implications of industrial service strategies. The fundamental proposition of the RBV is that firms constitute complex bundles of idiosyncratic resources and capabilities (Barney 1991) that support strategic actions designed to enhance firms’ competitive positions (Lee, Naylor, and Chen 2011). To the extent that these resources and capabilities are valuable, rare, inimitable, and nonsubstitutable (VRIN), they ensure the sustainability of competitive advantage and promote longterm financial success (Peteraf 1993). In addition to explaining how resources and capabilities generate sustainable competitive advantages, the RBV addresses the formation and development of resources and capabilities (Grant 1996). Effects of Industrial Service Strategies on Revenue and Profit Trajectories Manufacturers’ industrial service strategies can be characterized by the breadth (i.e., scope) and depth (i.e., focus) of the service portfolio (Fang, Palmatier, and Grewal 2010; Prabhu, Chandy, and Ellis 2005). The breadth of a service portfolio captures the number of services offered; the depth refers to the emphasis placed on each service, i.e., the degree to which the manufacturer proactively offers each service to its customers (Challagalla, Venkatesh, and Kohli 2009). We focus specifically on the breadth of the service portfolio and distinguish between SSPs and SSCs. This approach is in line with previous research that has shown that the breadth of a
9 (product) portfolio constitutes an important strategic dimension (Miller 1987; Varadarajan and Clark 1994). Implementing an industrial service strategy demands major organizational learning and change processes (Gebauer et al. 2010). Traditionally, manufacturers have operated in a goodsdominant mode and they must develop additional resources and capabilities to compete on services. When offering SSPs, firms can resort primarily to existing competencies in the product domain and must develop only a few service-specific capabilities (Kowalkowski, Brehmer, and Kindström 2009). In contrast, when offering SSCs, they must institute extensive organizational changes and investments to establish a corresponding resource base (Kowalkowski, Brehmer, and Kindström 2009). Because of the greater similarity between manufacturers’ established resources and the required resources for SSPs, manufacturers likely enter the service business by offering SSPs (Helfat and Lieberman 2002; Raddats and Easingwood 2010). By offering multiple SSPs, manufacturing companies gain the basic resources and capabilities needed to participate in a service business (Matthyssens and Vandenbempt 2008). In particular, SSPs should help manufacturers learn how to manage services and build a reputation as service provider (Ulaga and Reinartz 2011). The resources and capabilities thus acquired in turn function as stepping stones to the further development of more distant resources and capabilities that enable the provision of SSCs (Wernerfelt 1984). Both organizational learning from the experiences with basic services and the development of a reputation as service provider make it easier for manufacturers to offer advanced, knowledge-intensive SSCs. Therefore, we expect firms with a broader SSP portfolio to have a broader SSC portfolio compared to firms with a narrower SSP portfolio. H 1 : The breadth of the SSP portfolio positively influences the breadth of the SSC portfolio.
10 SSCs require resources and capabilities that are highly service specific, have a strong tacit dimension, and are often socially complex. Consequently, the underlying resources and capabilities satisfy the VRIN requirements for a sustainable competitive advantage (Peteraf 1993). Hence, SSCs provide a basis for sustainable competitive advantages, leading to increased revenues (Reed and DeFillippi 1990). With a broader scope of resources and capabilities, a broad SSC portfolio also can better ensure a sustained competitive advantage, compared with a narrow SSC portfolio (Grant 1996). We therefore expect manufacturing companies with a broader SSC portfolio to have a higher level and growth rate of revenue streams compared to firms with a narrower SSC portfolio. H 2 : The breadth of the SSC portfolio positively influences the (a) level and (b) growth of firm revenues. The development of service-specific resources and capabilities demands substantial investments (Reinartz and Ulaga 2008). In manufacturing firms, existing resources and capabilities typically are geared towards the product domain (Raddats and Easingwood 2010); the RBV depicts these resources as committed. Committed resources tend to slow down change processes and increase costs of change (Kraatz and Zajac 2001). As service initiatives are investments, the costs of the service business will initially outweigh revenue enhancements, leading to lower initial profit levels for firms offering industrial services (Fang, Palmatier, and Steenkamp 2008). These negative effects should be especially high for companies with a broad SSC portfolio. Over time though, organizational learning likely decreases the costs of resource accumulation and leads to growth in firm profitability. Because organizational learning tends to be greater with repeated uses of resources and capabilities (Hamel and Prahalad 1993), we expect stronger profit growth for firms with a broader SSC portfolio. Consequently, firms with a broader SSC portfolio will possess lower levels but higher growth rates in profits.
11 H 3 : The breadth of the SSC portfolio (a) negatively influences the level but (b) positively influences the growth of firm profits. Compared with SSCs, SSPs are less firm specific, less customized, and less knowledge intensive (Antioco et al. 2008). Consequently, we expect the tacitness, complexity, and specificity of the underlying resources and capabilities of SSPs to be relatively low (Reed and DeFillippi 1990). Therefore, it will be difficult to maintain barriers to imitation for SSPs (Antioco et al. 2008). Furthermore, SSPs mainly encompass basic services that manufacturers must possess to meet customer demands. In this sense, SSPs represent necessary requirements to participate in the market, rather than sources of differentiation. Because SSPs normally do not satisfy the VRIN requirements, we predict that they do not provide sustainable sources of competitive advantage either and fail to exert a direct influence on manufacturers’ financial outcomes, on average. However, we expect an indirect influence of SSPs, operating through SSCs. We thus extend our claim of a positive effect of the breadth of the SSP portfolio on the breadth of the SSC portfolio (H 1 ) to propose that SSCs mediate the impact of SSPs on firms’ revenue and profit trajectories. H 4 : The breadth of the SSC portfolio mediates the impact of the breadth of the SSP portfolio on the firm’s revenue and profit trajectories. Moderating Effects of Decentralization and Share of Loyal Customers Previous research indicates that industrial services’ success depends on the presence of a supportive organizational design (Bowen, Siehl, and Schneider 1989; Neu and Brown 2005). The organizational design encompasses all “decisions about how firms organize” (Homburg, Workman, and Jensen 2000, p. 460) and can be characterized by its structural and nonstructural dimensions (Workman, Homburg, and Gruner 1998). We focus on two dimensions that likely influence a manufacturer’s success in the service domain: decentralization (a structural dimension) and the firm’s share of loyal customers (a nonstructural dimension).
12 Decentralization. Decentralization reflects the dispersion of decision-making authority in an organization; it demands significant participation by organizational members at lower hierarchical levels (see Jaworski and Kohli 1993). Centralized firms enjoy greater efficiency because they engage in streamlined information processing and decision making (Auh and Menguc 2007). They are well adapted to environments with stable and simple market demands (Ruekert, Walker, and Roering 1985) and achieve the efficient provision of highly standardized market offerings. In contrast, a high level of customer orientation, which is needed to provide complex services such as SSCs, demands a decentralized organization (Auh and Menguc 2007; Gebauer et al. 2010), because such organizations can deal adequately with diverse, rich resources incorporated through their human capital (Auh and Menguc 2007). For example, employees with greater decision-making authority can react faster to changing customer needs, which should increase the firm’s service profits over time (Gebauer, Edvardsson, and Bjurko 2010; Grizzle et al. 2009). Decentralized firms thus should be better prepared to turn SSC-based service strategies into financial results, and we hypothesize: H 5 : The degree of decentralization positively moderates the relationship between the breadth of the SSC portfolio and firms’ (a) revenue growth and (b) profit growth. Share of loyal customers. Existing relationships with loyal customers provide an important resource for manufacturing companies (Matthyssens and Vandenbempt 1998; Raddats and Easingwood 2010) that might moderate the revenue and profit growth implications of industrial service strategies. Regarding revenue growth, we find arguments for both positive and negative effects. On the one hand, a high share of loyal customers should make it easier for firms to market their services successfully and create additional revenues. Long-term relationships with loyal customers can help overcome purchase uncertainty (Patterson, Johnson, and Spreng 1997) because they establish an existing foundation of trust. Loyal customers are more likely to source
13 services from a trusted manufacturer (Raddats and Easingwood 2010), which implies that a higher share of loyal customers could strengthen the positive impact of SSCs on revenue growth. On the other hand, long-term customers tend to have higher expectations (Pressey and Tzokas 2004) and may demand extra activities for free. They also pay lower prices than short-term customers (Reinartz and Kumar 2000). Consequently, a high share of loyal customers could negatively affect the relation between industrial service offerings and revenue growth. These opposing effects might cancel each other out, so we do not expect a significant net effect of the share of loyal customers on the link between SSCs and revenue growth. Regarding profit growth though, we hypothesize a significant moderating effect, driven by cost reductions. Deep customer insights are required to enhance the efficiency of service provision, and manufacturers with more loyal customers should have better insights into their customers’ experiences, capabilities, and needs (Dietz, Pugh, and Wiley 2004). This information helps companies improve their production and deployment of SSCs and thereby achieve cost reductions over time. Firms with a higher share of loyal customers therefore should be more successful in turning service revenues into profits: H 6 : The share of loyal customers positively moderates the relationship between the breadth of the SSC portfolio and firms’ profit growth. METHOD Sample We rely on panel data from the German mechanical engineering industry (Beck and Walgenbach 2005). With roughly a million employees and overall sales of €201 billion in 2011, the mechanical engineering industry represents the largest industrial sector in Germany (German Engineering Federation 2012). The data for this panel were gathered in annual, national mail surveys, sent to a population of approximately 5,500 manufacturing companies with more than 20
14 employees. In the first measurement wave, one questionnaire was mailed to the top management of each firm. In the cover letter, managers were asked to fill out the questionnaire themselves or pass it on to someone who had the in-depth knowledge to answer the survey. The respondents thus mainly represented top decision makers in their companies, such as chief executive officers, chief operating officers, and heads of administration. Respondents also indicated their name on the questionnaire. In the following waves, given that a specific contact person was known from the previous wave, the questionnaires were directly addressed to that person; therefore, the same persons answered the annual surveys. Only 4.9% of all cases experienced a change in the respondents between periods 1 and 2. We analyzed data from three consecutive, annual survey waves. The first wave contained 1,342 usable questionnaires. Annual attrition rates in the next two periods were 35.0% (t 1 to t 2 ) and 41.2% (t 2 to t 3 ). We thus ended up with 513 cases that provided data across all three measurement waves; we limited our analysis to these cases because more lenient inclusion criteria would have resulted in large amounts of missing values (Jaramillo and Grisaffe 2009). We conducted several analyses to test for biases due to initial nonresponse and attrition. First, for biases due to initial nonresponse, we compared the overall sample (all companies that responded at least once) with the total population of the German mechanical engineering industry. We used data that we gathered from the German Federal Statistical Office to determine overall revenues and number of employees. Goodness-of-fit tests indicated a similar composition of both groups in terms of revenue (χ2 emp. = 6.92, χ2 tab. (3; .95) = 7.81) and number of employees (χ2 emp. = 7.33, χ2 tab. (3; .95) = 7.81). Therefore, nonresponse bias does not appear to represent a serious problem for our study. Second, we tested whether our focus on cases that responded in all three measurement waves caused an attrition bias (Bentein et al. 2005) by comparing those cases included in the
15 analysis with excluded cases of firms that participated only once or twice. To find any differences between included and excluded cases, we conducted t-tests on the number of employees, overall revenue, SSPs, and SSCs. None of these analyses yielded significant results (p ≥ .10). Measures Data about industrial service strategies were collected in the first measurement wave. The questionnaire asked respondents to characterize their SSP and SSC portfolios. Specifically, each respondent selected, from two lists of core SSPs and SSCs, the services that his or her company offered. The list of SSPs encompassed product-related services, such as product repair, spare part delivery, product documentation, maintenance, and product recycling and dismantling; the list of SSCs featured customer process-related services, such as consulting, training, financing services, and research and development. To measure the extent of SSP and SSC provision, we counted the number of services in the respective category, which represented the breadth of the SSP and SSC portfolios. Similarly, Gebauer and colleagues (2010, p. 205) note that the number of services offered reflects the “scope of the service strategy in terms of strategic marketing offering.” The scale ranges from 0 to 5 for SSPs and 0 to 4 for SSCs. The substantial ranges of SSPs and SSCs we observed, as well as the variety of combinations of SSPs and SSCs in our sample, indicate meaningful variance in the composition of the overall service portfolio. To measure financial outcomes, respondents indicated, in monetary terms, the overall annual revenue of their respective firms. To correct for nonnormality in this measure, we applied a natural logarithmic transformation. Respondents also assessed the profit situation of their company on a five-point Likert-type scale (“poor” [1] to “excellent” [5]). The revenue and perceived profit data were gathered in all three measurement waves. For the moderator variables, we used data taken during the second measurement wave. With regard to decentralization, the respondents rated, on a five-point Likert-type scale (“very
16 low” [1] to “very high” [5]), the extent to which decision-making authority in their firms was delegated to lower hierarchical levels (Jaworski and Kohli 1993). The operationalization of the share of loyal customers relied on respondents’ indications of the percentage of revenue earned from repeat customers. We included company size as a control variable, captured as the number of employees. We estimated the natural logarithm for this measure to correct for nonnormality. The company size data came from the first measurement wave. We summarize all these measurement scales and items in the Appendix. Table 2 provides an overview of descriptive statistics and correlations between variables. – Insert Table 2 about here – Using a longitudinal study design and the documented measures, we attempted to minimize common method effects in two ways. First, we measured the predictors (i.e., SSPs and SSCs) and outcomes (i.e., revenue and profit growth) at different points in time. This temporal separation should prevent respondents from remembering responses they gave previously (Rindfleisch et al. 2008). Second, we measured predictors and outcomes using different formats and scales. This measurement separation helped reduce response biases, such as halo effects, consistency motifs, acquiescence, and implicit theories and illusory correlations, that otherwise could result in common method variance (Podsakoff et al. 2003). Data Analysis We employed latent growth curve modeling (LGCM) for data analysis. LGCM is an advanced application of structural equation modeling that analyzes longitudinal change (Chan 1998). Using measures observed across multiple time points that capture the level of a variable each year, LGCM calculates the latent intercept (i.e., level) and latent slope (i.e., growth) of the underlying developmental trajectory (Jaramillo and Grisaffe 2009).
17 Compared with more traditional longitudinal data analysis approaches (e.g., difference scores, repeated measures, panel regression), LGCM provides several advantages for change research (Chan 1998; Lance, Meade, and Williamson 2000). First, because LGCM is based on an analysis of means and covariances, it captures within- and between-subject changes in the same analytical framework (Byrne, Lam, and Fielding 2008). Thus it applies not only to analyses of the average parameters of a growth curve but also accounts for the variance of these parameters in the sample, such as inter-individual or firm differences (Byrne, Lam, and Fielding 2008). Second, using item-level information, LGCM accounts for measurement error in the estimation process (Ployhart and Vandenberg 2010). Because the parameters of the growth curve (i.e., intercept and slope) are also modeled as latent variables, the estimated growth curve represents the true nature of the change pattern (Chan 1998). Third, LGCM can handle violations of the basic assumptions (e.g., independence of residuals, homogeneity in residuals) that underlie general linear models (Ployhart and Vandenberg 2010). Specifically, it can compare the model fit of alternative growth curve specifications with independent or dependent residuals, as well as homoscedastic or heteroscedastic residual structures (Bollen and Curran 2006; Chan 1998). Fourth, the flexibility of LGCM enables researchers to model complex multivariate change models (Ployhart and Vandenberg 2010). Within these models, the latent intercept and slope variables can each serve as independent and dependent variables (Lance, Vandenberg, and Self 2000; Ployhart and Vandenberg 2010). Consequently, compared with traditional methodologies, LGCM is applicable to tests of interrelationships between the changes in two variables, that is, to tests of concomitant change (Ployhart and Vandenberg 2010).
18 Fifth, no precise representation of the functional form of the growth trajectory is possible with most methodologies, but LGCM can identify and compare different functional forms of trajectories (Chan 1998; Lance, Meade, and Williamson 2000). Each of the traditional longitudinal data analysis approaches offers some of these advantages, but LGCM is the only method that can fulfill all possible requirements for analyzing longitudinal change (Chan 1998; Lance, Meade, and Williamson 2000). MODEL SPECIFICATION We conducted all analyses using AMOS 19.0 (Arbuckle 2010). Similar to the successive specification of measurement models and structural models in SEM, we adopted a two-step process to build our LGCM (Bollen and Curran 2006). First, we tested two unconditional latent growth curve models for the revenue and profit outcome variables separately. We thus specified two models that adequately and parsimoniously described the respective growth trajectories of revenues and profits. In this within-individual step, the intercept and slope “constructs” were fit with the repeatedly measured variable to model intra-individual change (Jaramillo and Grisaffe 2009). It is also possible to determine interindividual differences in change, because we modeled the intercept and slope as random effects (Byrne, Lam, and Fielding 2008). Second, as the unconditional models demonstrated adequate model fit and significant variability in their intercept and slope, we merged the two unconditional models and built a conditional latent growth model (Bollen and Curran 2006). In this between-individual stage, we focused on explaining interindividual differences in the latent intercept and latent slope of the revenue and profit growth curves by implementing explanatory variables (Lance, Vandenberg, and Self 2000). Unconditional Latent Growth Models For the first step, we specified and tested two separate unconditional latent growth curve models for both dependent variables. The specification of an unconditional latent growth curve
19 model is similar to a factor analysis. The corresponding equations are shown in the Appendix. For each variable, we compared alternative latent growth curve models that varied in their functional form (no growth, linear growth) and the residual structure (homoscedastic, heteroscedastic) of the growth curve (Lance, Vandenberg, and Self 2000). Allowing heteroscedasticity among the errors of the repeatedly measured revenue and profit variables, we account for the common observation that the precision with which an attribute is measured is not identical across time periods (Willett and Sayer 1994). To select the optimal model specification, we relied on nested model comparisons and inspections of the fit indices (Bentein et al. 2005). The results of the model specification and nested model comparisons for revenue appear in Table 3, Panel A. The nested model comparisons indicated that the linear model with heteroscedastic error structure most adequately describes the data.1 For our research period, revenue shows a linear change over time, and the final model for revenue provides good model fit statistics (χ2 = 1.98, df = 2; normed fit index [NFI] = .999, confirmatory fit index [CFI] = 1.000, root mean square error of approximation [RMSEA] = .000). LGCM estimates firm-specific growth trajectories for every firm, which can vary in their intercepts and slopes. To characterize the final unconditional growth model for revenue, we inspect the means and variances of the random intercept and slope parameters. The means of intercept and slope reveal a positive average development in revenue across firms. The average level for revenue is 2.69 (p < .01), and the average slope is .03 (p < .01). Because we applied a natural logarithmic transformation for revenue, we can interpret the slope as the average percent increase in revenue per year. Moreover, the significant intercept (1.13, p < .01) and slope variances (.01, p < .01) indicate important interindividual or firm differences in both the level of revenue and its rate of change. Such evidence provides strong justification for incorporating predictor variables into the subsequent conditional latent growth curve model.
20 In Table 3, Panel B, we show the model specification and nested model comparisons for profit. The homoscedastic linear growth model and the heteroscedastic linear growth model produce good fits with the data. We choose the homoscedastic linear growth model as our final model, because it adequately and parsimoniously represents changes in profit (χ2 = 6.89, df = 3; NFI = .987, CFI = .993, RMSEA = .050). – Insert Table 3 about here – To characterize the final unconditional growth model for profit, we again inspect the means and variances of the random intercept and slope parameters. We find that the average level of profit in year 1 is 2.52 (p < .01), and profit then increases by .19 per year (p < .01). Both parameters are significant, so our results confirm a joint development of profit across firms. Significant variability in the intercept (.81, p < .01) and slope (.09, p < .01) indicate important inter-firm differences in both parameters, which can be analyzed with a conditional latent growth model. Conditional Latent Growth Model In the second analytical step, we built a conditional growth model to analyze the relationships between our predictor variables and the latent parameters of the revenue and profit trajectories. To do so, we incorporated the two unconditional growth models for revenue and profit into a multivariate latent growth curve model and included all possible cross-domain correlations between the parameters of the two growth curves. Next, we added predictors to the model that might explain variation in the revenue and profit trajectories. Specifically, we introduced SSPs and SSCs as potential antecedents of the latent intercept and the latent slope of the revenue and profit trajectories and controlled for company size (see Figure 1). The results for our conditional latent growth curve model indicate an adequate fit to the data (χ2 = 26.51, df = 19; NFI = .995, CFI = .999, RMSEA = .028), as we summarize in Table 4.
21 – Insert Figure 1 and Table 4 about here – HYPOTHESES TESTS Effects of Industrial Service Strategies on Revenue and Profit Trajectories In H 1 , we predict that firms with a broader SSP portfolio will have a broader SSC portfolio too. In line with this, we find a significantly positive relationship between SSPs and SSCs (β = .484, p < .01). Also in support of H 2a and H 2b , we find significant, positive paths between SSCs and both revenue level (β = .08, p < .01) and revenue growth (β = .15, p = .05). In Figure 2, Panel A, we illustrate exemplary revenue trajectories for firms with a narrow (mean – 1 SD) and a broad (mean + 1 SD) SSC portfolio. Consistent with our hypotheses, firms with a broader SSC portfolio possess a higher level of revenue and greater percent increase in revenue each year. Particularly, the average revenue increase per year is 4.65 % for companies with a broad SSC portfolio, and 1.75 % only for companies with a narrow SSC portfolio. Furthermore, we uncover a significant negative path between SSCs and profit level (β = – .13, p < .05) and a significant positive path between SSCs and profit growth (β = .20, p < .05), in support of both H 3a and H 3b . The profit trajectories for firms with a narrow and a broad SSC portfolio (mean ± 1 SD) appear in Figure 2, Panel B. Although companies with a broad SSC portfolio start from a relatively low level of profit, they slightly outperform companies with a narrow SSC portfolio at the end of the study period, i.e., after three years. In H 4 , we hypothesize that the breadth of the SSC portfolio mediates the effects of the breadth of the SSP portfolio on both revenue and profit trajectories. Although all the direct effects of SSPs on the revenue and profit trajectories are insignificant, we find significant indirect effects (.000 < p < .075) on the outcome variables through SSCs when we apply the bootstrap
22 procedure recommended by Preacher and Hayes (2008). We thus confirm that the effects of SSPs on revenue and profit trajectories are fully mediated by SSCs. Moderating Effects of Decentralization and Share of Loyal Customers To test our moderating hypotheses, we estimated multiple-group LGCM. Multiple-group analysis is a well-established, widely accepted method for detecting moderating effects in structural equation models (e.g., Homburg, Droll, and Totzek 2008; Palmatier, Scheer, and Steenkamp 2007).2 For each moderator, we used a median split to divide the sample into two subgroups, then tested whether the hypothesized path is moderated through a comparison of the two models. In the first model, we allowed the hypothesized path to vary freely across groups; in the second, we constrained the path to be equal across groups. To compare these models, we used a chi-square difference test. We summarize the results of the multiple-group analyses in Table 5.3 – Insert Table 5 about here – The moderation test reveals that firms with low and high decentralization differ significantly in terms of the impact of SSCs on their revenue growth (∆χ2(1) = 4.83, p < .05) and profit growth (∆χ2(1) = 6.28, p < .05). In particular, SSCs have a significantly positive effect on both revenue and profit growth for companies with high decentralization (β = .40, p < .01; β = .47, p < .01, respectively). Both effects become insignificant for firms with low decentralization (β = .01, p > .10; β = .01, p > .10, respectively). Accordingly, Figure 2, Panels C and D, reveal that the highest growth of the revenue and profit trajectories occurs for companies with a broad SSC portfolio and high decentralization. Thus, we find support for H 5a and H 5b . Finally, in support of H 6 , the share of loyal customers positively moderates the effect of the breadth of the SSC portfolio on firms’ profit growth (∆χ2(1) = 3.84, p < .05). When firms have a low share of loyal customers, SSCs exert no significant effect on profit growth (β = .03, p > .10), but for firms with a high share of loyal customers, SSCs have a significant and positive
23 effect (β = .36, p < .01). As we depict in Figure 2, Panel E, when companies have a high share of loyal customers, profit growth is highest if they also have a broad SSC portfolio. Although their profit trajectory starts from the lowest level, these companies achieve the highest level of profit at the end of the three-year period. In contrast, for companies with a low share of loyal customers, profit growth does not differ, regardless of the breadth of their SSC portfolio. – Insert Figure 2 about here – DISCUSSION A growing consensus among managers and scholars suggests that goods-dominant companies should seek service-led growth to secure their existing positions in business markets and continue to grow (Ostrom et al. 2010). Yet rather than just a general agreement about why manufacturers move toward services, we need a better understanding of how they can ensure that this move proves profitable over time (Reinartz and Ulaga 2008). Both anecdotal evidence (Renault, Dalsace, and Ulaga 2010; Stanley and Wojcik 2005) and emerging academic results (Fang, Palmatier, and Steenkamp 2008) suggest a need for further research on how service strategies contribute to firms’ success and the conditions in which these initiatives lead to revenue and profit growth. The present study makes several important contributions to the academic literature and managerial practice. First, we specifically disentangle the effects of industrial service strategies on different performance indicators. Prior studies have focused predominantly on single outcome variables, such as revenue (Antioco et al. 2008), profitability (Gebauer, Edvardsson, and Bjurko 2010), or firm value (Fang, Palmatier, and Steenkamp 2008), without differentiating among key levers of firm performance. We analyze the effects of industrial service strategies on manufacturers’ revenue and profit trajectories. Revenues and profits received from service strategies do not necessarily evolve in the same direction. Against this backdrop, we find that
24 firms with a broader service portfolio display higher levels and percent increases in revenues, but not per se more satisfying profit streams. Instead, firms with a broader service portfolio suffer from lower profit levels at the beginning of our study period. As we estimated the effects of industrial services on revenues and perceived profits simultaneously – while controlling for their correlations – we can ascribe this negative effect to higher costs at these firms. Higher costs can result for example from investments in required resources and capabilities or organizational and managerial difficulties with managing services. Over time though, manufacturing companies with a broader service portfolio can compensate for these costs and attain a profit advantage compared to firms with a narrower SSC portfolio. From a managerial standpoint, our findings suggest that industrial service strategies of goods-dominant firms take time to pay off; that is, they lead to lower profitability levels before firms can realize profit growth and compensate for initial losses. As the Michelin example illustrated, companies need experience to learn how to sell services (Renault, Dalsace, and Ulaga 2010). Our results indicate that firms with a broader service portfolio can more easily move along the learning curve and improve the rate at which revenues and profits grow over time. Companies that fail to continuously invest in their service business, e.g., by not sufficiently broadening their service portfolio, take the risk of missing an opportunity to increase revenues and profits and compensate for possible initial profit losses. In addition, revenue effects are as straightforward as we might expect, but a problem arises on the cost side. Hence, our results underscore that it is important for firms to specifically account for the costs of services in their control systems, monitor costs in service production and deployment, and align incentive systems to ensure that both revenues and costs remain on target during their pursuit of service strategies. As a second contribution, our study underscores the different roles of industrial service categories. Previous research has highlighted the need to account for different types of service
25 offerings (Ulaga and Reinartz 2011). By distinguishing the effects of SSPs and SSCs, we find that the latter directly affect the company’s revenue and profit streams, whereas the former display only an indirect impact. SSCs further appear to mediate the effects of SSPs on both revenue and profit trajectories. By identifying this key mediating role of SSCs for both revenue and profit trajectories, we can confirm SSPs’ status as a starting point for a successful service business. Therefore, manufacturers should concentrate on developing and consolidating their SSP portfolio first. A broad SSP portfolio can help them gain substantial insights into their service business, build fundamental service competences, and lay a foundation for more complex services. By systematically developing SSCs beyond SSPs, manufacturers then can reap the financial benefits of industrial service strategies. Specifically, our results confirm that firms with a broader SSC portfolio have higher percent increases in revenue and higher growth rates for profit. By offering a broad SSC portfolio, companies satisfy broader customer needs and ensure future revenue growth; at the same time, they can take advantage of complementarities between services offerings and consecutive learning effects that facilitate future cost savings. As a third contribution, our findings highlight the need for manufacturers to implement an appropriate organizational design across their entire business before they can reap the financial benefits of service offerings. First, to thrive, companies should decentralize their decision-making authority to lower levels in the hierarchy as they move from services designed around products toward services designed around customers’ processes. The moderating role of decentralization shows that industrial service strategies entail essential foundations of a company’s organizational structure. Second, our findings highlight the critical role of a loyal customer base for growing the firm’s industrial service business profitably. We find a particularly strong positive effect of SSCs on profit growth for companies with a high share of loyal customers. Loyal customers help
26 companies manage the costs of service provision and thus increase their profit growth. Because manufacturers have more experience working with loyal customers, they bear lower costs in serving them, compared with less loyal customers (Kalwani and Narayandas 1995). Rather than rolling out SSCs across their entire customer base, manufacturers might target loyal, core customers in their efforts to grow toward SSCs. The final contribution of our study refers to the methodology we used. We show that LGCM provides advantages over traditional approaches when measuring longitudinal change. Among other things, LGCM allows researchers to model latent intercept and slope constructs of developmental trajectories, distinguish intra- and inter-individual/firm change, test complex structural relationships, and account for measurement error. Despite its promise for marketing and service research, we find few previous applications of LGCM (e.g., Eggert et al. 2011, Jaramillo and Grisaffe 2009, Koehler et al. 2011). To promote this methodology in the service domain, we provide a step-by-step description of its specification; we hope this effort grants researchers greater familiarity with this promising methodology. LIMITATIONS AND RESEARCH DIRECTIONS Our research design is subject to several limitations, some of which offer fruitful avenues for research. First, we relied on self-reported measures to analyze profit trajectories. These subjective performance measures ensure some comparability across different types of companies and situations with varying standards for acceptable performance (Pelham and Wilson 1996). But when company representatives provide subjective performance ratings, they may overstate them (Haugland, Myrtveit, and Nygaard 2007). However, the longitudinal design of our study should reduce the likelihood of self-reported method effects (Podsakoff and Organ 1986) because response biases caused by performance overstatements affect only the level of profit, not its changes. Hence, the relationships among SSPs, SSCs, and profit growth should not be biased.
27 Nonetheless, further research could test the relationship between industrial services and financial performance using objective performance measures or multiple data collection methods, to determine the stability of our results. Second, by focusing on the mechanical engineering industry in Germany, we reduced the risk of uncontrollable factors that create noise in cross-industry studies (Haugland, Myrtveit, and Nygaard 2007). It is worth mentioning that this industry represents a heterogeneous group of organizations that span different subsectors (e.g., machine manufacturers, precision instruments, optical instruments, medical equipment manufacturers). Our model fits this heterogeneous sample well, which implies that the effects are robust. However, the results are not necessarily generalizable to all industrial sectors. Third, we operationalize companies’ industrial service strategies by measuring the number of different services they offer within SSC and SSP categories. Two issues may be related to this measurement approach. First, although the breadth of a portfolio represents a major strategic dimension (Miller 1987; Varadarajan and Clark 1994), previous research has highlighted the importance of both breadth (i.e., scope) and depth (i.e., focus) in marketing activities (Fang, Palmatier, and Grewal 2010). Therefore, further research should attempt to capture the role of focus in manufacturers’ industrial service strategies. For example, it could be interesting to investigate the extent to which manufacturers proactively contact customers to sell industrial services (Challagalla, Venkatesh, and Kohli 2009). Second, we did not intend to create an exhaustive directory but to compile a list of common industrial services representing SSPs and SSCs. Future research might provide a more detailed view by adding additional services to the measures. Fourth, we were able to show the revenue and profit changes over a period of three consecutive waves (i.e., years). Thus our data provides a first glimpse into the effects of industrial
28 service strategies on manufacturing companies’ revenue and profit development over time. To better understand the success of industrial service strategies, a longer observation period would be insightful. Extending the time frame could further enrich our findings and probe into the robustness of our results. Last, our research referred to one organizational design element, i.e., decentralization of decision-making authority. Yet, we can think of other organizational contingencies, such as a firm’s service culture or the presence of a process management, which might also facilitate service growth. By analyzing their moderating role, researchers could further enhance our understanding of the consequences of industrial service strategies. These limitations must be kept in mind when considering our results and implications, yet our findings provide new insights that we hope stimulate further research in this important, underresearched domain.
29 NOTES 1
For parsimony, we fix the insignificant covariance between the intercept and slope to equal 0.
2
In LGCM, multiple-group analysis is typically used to detect moderating effects. First, this is due to model complexity restrictions of the LGCM approach. In many cases, adding multiple interaction terms to LGCM leads to estimation problems because LGCM uses many degrees of freedom (Li, Duncan, and Acock 2000). Second, multiple-group analysis provides advantages for interpreting the results. In LGCM, the interaction effect of two predictor variables on the slope of a growth curve technically needs to be interpreted as a three-way interaction (Curran, Bauer, and Willoughby 2004), because the interaction effect itself is influenced by time.
3
We performed post hoc moderation tests to check for the stability of our results. First, we conducted multiple-group LGCM in which we dichotomized our moderator variables with the top and bottom one-third of the sample, instead of a median split. Second, we employed a regression analysis as a “sanity check,” in which we calculated the differences in revenue and profit between time 3 and time 1 as dependent variables. The moderator variables were modeled as continuous variables and interaction terms were built with SSCs. The results remain consistent across all analyses.
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39 BIOGRAPHICAL INFORMATION Andreas Eggert is Chaired Professor of Marketing at the University of Paderborn, Germany. His research interests focus on strategies for the creation and appropriation of value in business relationships. Dr. Eggert’s work has appeared in the Journal of Marketing, the Journal of Service Research, the Journal of Supply Chain Management, the Journal of Business Research, the European Journal of Marketing, the Journal of Marketing Theory and Practice, Industrial Marketing Management, the Journal of Business-to-Business Marketing and the Journal of Business and Industrial Marketing among others. Jens Hogreve is Professor of Service Management at the Catholic University of EichstaettIngolstadt, Germany. He received his doctoral degree from the University of Hagen, Germany. His research focuses on service issues such as service recovery and service guarantees, service innovation, industrial services, and customer co-creation. The results of his work appear in the Industrial Marketing Management, the International Journal of Research in Marketing, Journal of Service Research, Journal of Retailing, German-language journals, and edited book chapters. Wolfgang Ulaga is Professor of B2B Marketing at IMD Lausanne, Switzerland. His research focuses on how firms understand, (co-)create, communicate, deliver, and capture value in B2B markets. His current research focuses on how goods-centric firms grow beyond their traditional core and investigates how industry leaders successfully implement service-growth strategies in industrial markets. His work has appeared in Journal of Marketing, Harvard Business Review, Journal of Business Research, Industrial Marketing Management, European Journal of Marketing, Journal of Services Marketing, Journal of Business-to-Business Marketing, and Journal of Business and Industrial Marketing, among others. Eva Muenkhoff is Assistant Professor of Marketing at the University of Paderborn, Germany. She received her PhD from the University of Paderborn. Her research interests are in business-tobusiness and services marketing. Current projects focus on the profitability of industrial services and the transition of manufacturing companies toward service and solution providers. Her work has been published in Industrial Marketing Management and several conference proceedings. For her work, she received the best overall conference paper award 2011 from the American Marketing Association.
40 Table 1 EMPIRICAL RESEARCH ON FINANCIAL OUTCOMES OF INDUSTRIAL SERVICES Financial Performance Measure Authors (Year)
Sample, Method Revenue
Profit
137 manufacturing firms located in Belgium, the Netherlands, and Denmark, SEMa
Fang, Palmatier, and Steenkamp (2008)
477 U.S.-based, publicly traded manufacturing firms, fixed-effects panel regression
Gebauer (2007)
212 German and Swiss machinery and equipment manufacturing companies, SEM
+ (overall profitability)
Gebauer, Edvardsson, and Bjurko (2010)
302 German and Swiss machinery and equipment manufacturing companies, SEM
+ (average return on sales)
Homburg, Fassnacht, and Guenther (2003)
271 German companies from three industrial sectors: electrical engineering, mechanical engineering, and metalworking, SEM
+ (direct service profitability, overall profitability)
This research
513 German companies from the mechanical engineering industry, LGCM
SEM = structural equation modeling.
Moderating Effects Service training Cross-functional communication of service employees Service technology
Antioco et al. (2008)
a
Firm Value
+ (relative product sales)
–/+ (U) Service relatedness (Tobin’s q) Resource slack Industry growth Industry turbulence
+ (revenue)
–/+ (profit)
Top management recognition of the potential of customer support services Service orientation of corporate culture Service orientation of human resource management Type of organizational structure of the service business (integrated vs. separated service organization)
Decentralization Share of loyal customers
41 Table 2 DESCRIPTIVE STATISTICS AND CORRELATIONS Variable
1
2
3
4
5
6
7
8
9
10
11
1. ln REV t 1 2. ln REV t 2
.98**
3. ln REV t 3
.96**
.97**
4. Profit t 1
.03
.07
.07
5. Profit t 2
.03
.07
.09*
.66**
6. Profit t 3
.05
.07
.10*
.50**
.60**
7. SSP
.23**
.24**
.22**
-.04
.01
.02
8. SSC
**
.39
**
.40
**
.40
*
-.10
-.02
.01
.53**
9. ln CS
.92**
.92**
.90**
-.03
-.01
.01
.19**
.35**
10. DEC
.11*
.12**
.13**
.05
.05
.06
.05
.09
.12**
11. SLC
-.06
-.06
-.05
.04
.00
.00
-.14**
-.18**
-.04
.00
M
2.695
2.719
2.764
2.524
2.69
2.914
2.673
1.667
4.282
3.185
72.98
SD
1.068
1.082
1.11
1.062
1.06
1.053
1.28
1.036
.875
.876
21.283
*
p < .05. p < .01. Notes: REV = revenue, CS = company size, DEC = decentralization, SLC = share of loyal customers. **
42 Table 3 MODEL SPECIFICATION AND NESTED MODEL COMPARISON FOR THE TWO UNCONDITIONAL GROWTH CURVES
Model Specification
χ2
df
Model Comparison
∆ χ2
∆ df
NFI
CFI
RMSEA
.950
.976
.224
A: Revenue Trajectory Model M0 (no growth; homoscedastic residual structure)
159.51
6
Model M1 (linear growth; homoscedastic residual structure)
35.89
3
M0 vs. M1
123.62**
3
.993
.993
.115
Model M2 (linear growth; heteroscedastic residual structure)
1.91
1
M1 vs. M2
33.98**
2
.999
1.000
.042
Model M3 (linear growth; heteroscedastic residual structure, no covariance)
1.98
2
.999
1.000
.000
.796
.806
.184
B: Profit Trajectory
*
Model M0 (no growth; homoscedastic residual structure)
109.59
6
Model M1 (linear growth; homoscedastic residual structure)
6.89
3
M0 vs. M1
102.70**
3
.987
.993
.050
Model M2 (linear growth; heteroscedastic residual structure)
.79
1
M1 vs. M2
6.10*
2
.999
1.000
.000
p < .05. p < .01.
**
43 Table 4 CONDITIONAL LATENT GROWTH MODEL: TEST FOR DIRECT EFFECTS Path tested
Standardized Path Coefficient β
p-Value
SSP → SSC .484*** .000 SSP → revenue level .022 .267 SSP → revenue growth -.066 .397 SSP → profit level .028 .644 SSP → profit growth -.010 .915 SSC → revenue level .075*** .000 * SSC → revenue growth .151 .054 SSC → profit level -.125** .041 SSC → profit growth .198** .027 *** Company size → SSP .193 .000 Company size → SSC .252*** .000 .898*** .000 Company size → revenue level * p < .10 (two-tailed). ** p < .05 (two-tailed). *** p < .01 (two-tailed). Note: Double-dashed line indicate no relationship was hypothesized.
Hypothesis
Hypothesis Direction
Result
H1 ----H 2a H 2b H 3a H 3b ----
Positive ----Positive Positive Negative Positive ----
Supported ----Supported Supported Supported Supported ----
44 Table 5 MULTIPLE-GROUP LGCM: TEST FOR MODERATING EFFECTS Path tested
Standardized Path Coefficient β
∆ χ2 (∆ df = 1)
Hypothesis
Result
Decentralization SSC → revenue growth SSC → profit growth
Low
High
.01
.40***
4.83**
H 5a
Supported
.01
***
6.28
**
H 5b
Supported
3.84**
H6
Supported
.47
Share of Loyal Customers SSC → profit growth *
Low
High
.03
.36***
p < .10 (two-tailed). p < .05 (two-tailed). *** p < .01 (two-tailed). Notes: β represents the standardized path coefficient for that group; ∆χ2 represents the difference in χ2 between the restricted and the general model for the path being tested. **
45 Figure 1 CONDITIONAL LATENT GROWTH MODEL
46
Figure 2 GROWTH CURVES UNDER DIFFERENT CONDITIONS
Notes: DEC = decentralization; SLC = share of loyal customers.
47
Appendix MEASUREMENT SCALES AND ITEMS Construct
Items
Services supporting the product
Which of the following services did your company offer in year X?
(SSPs)
a
(multiple answers possible) 1. Customer services / hotline 2. Product documentation 3. Product repair and spare parts delivery 4. Product recycling and dismantling 5. Maintenance services
Services supporting the clients’ actions (SSCs)
a
Which of the following services did your company offer in year X? (multiple answers possible) 1. Training 2. Consulting 3. Financing services / leasing 4. Research and development
Revenue
What was the overall revenue of the company in year X?
Profit
How would you judge the profit situation of the company in year X? (five-point scale, 1 = “poor,” 5 = “excellent”)
Decentralization
Please rate the level of decentralization within your company (five-point scale, 1 = “very low,” 5 = “very high”)
Share of loyal customers
Which share of revenue did your company earn from loyal, repeat customers in year X?
Company size a
How many employees did your company have in year X?
Formative measure. Notes: The original questionnaire was in German. Items were translated and back-translated by two people who were experts in English and German.
48
MODEL EQUATIONS AND DEFINITIONS We can represent the unconditional growth curve model for a repeatedly measured variable 𝑦 as follows: (1) (2) (3)
𝑦𝑖𝑡 = 𝜂𝛼𝑖 + 𝜆𝑡 ∙ 𝜂𝛽𝑖 + 𝜀𝑖𝑡 𝜂𝛼𝑖 = 𝜇𝛼 + 𝜁𝛼𝑖 𝜂𝛽𝑖 = 𝜇𝛽 + 𝜁𝛽𝑖
Level 1 (𝑌-measurement) model Level 2 (structural) model for the intercept factor Level 2 (structural) model for the slope factor
𝑦𝑖𝑡 : observed score on measure y (e.g., revenue or profit) for individual 𝑖 at time 𝑡 𝜂𝛼𝑖 : intercept of the trajectory for individual 𝑖 𝜂𝛽𝑖 : slope of the trajectory for individual 𝑖 𝜆𝑡 : factor loading at time 𝑡 (i.e., value of time) 𝜀𝑖𝑡 : individual- and time-specific residual 𝜇𝛼 : mean intercept 𝜇𝛽 : mean slope 𝜁𝛼𝑖 : individual-specific deviation from mean intercept 𝜁𝛽𝑖 : individual-specific deviation from mean slope
