Partners for Business-to-Business Service Innovation - IEEE Xplore

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 60, NO. 1, FEBRUARY 2013

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Partners for Business-to-Business Service Innovation Stephan M. Wagner

Abstract—Firms that open up their organizational boundaries and access valuable external sources of knowledge can create new opportunities for innovation. However, little is known about this conjecture in the business-to-business (B-to-B) service context: whether B-to-B service firms utilize external knowledge and ideas for innovation, and if so, which types of partners they collaborate with in their innovation activities. This research is a step toward filling this void. Tobit regression analysis on secondary data was performed using various types of external partners as sources of innovation to determine the innovation performance of transportation and logistics service firms. The results show that customers, suppliers, and competitors (in descending order) contribute to service improvement, and customers contribute to the development of services that are new to the firm. In contrast, the use of universities and consultants as sources of innovation does not seem to immediately affect innovation performance in the transportation and logistics service industry. Index Terms—Absorptive capacity, business-to-business (B-toB) service innovation, knowledge-based view, open innovation, partners, secondary data, Tobit regression, transportation and logistics services.

I. INTRODUCTION N open innovation, firms open up their firm boundaries, access valuable external knowledge, and create opportunities for cooperative innovation with external actors. Successful innovators assimilate and exploit external knowledge and ideas and collaborate interactively with other organizations. They establish ties with partners to absorb or jointly develop new technologies, products, services, or processes [13], [15]. Various types of external partners can be such sources of innovation: suppliers, clients or customers, competitors, private and public research institutes (including universities), consultants, or other public sector organizations [16], [28], [81]. Previous studies of open innovation with external partners have focused on manufacturing or high-tech firms (see, e.g., [47], [51], and [78]). Although the study of processes and structures for “stimulating service innovation” has been identified as one overarching research priority for the science of service and “from a systems perspective, research is required to develop methods for integrating partners’ resources and activities to cocreate services” [63, p. 25]; little is known about how service firms utilize external partners as sources of knowledge and ideas in innovation processes or in cocreating services. In

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Manuscript received April 19, 2011; revised November 22, 2011 and April 16, 2012; accepted April 29, 2012. Date of publication June 11, 2012; date of current version January 16, 2013. Review of this manuscript was arranged by Department Editor C. Tucci. The author is with the Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich, 8092 Zurich, Switzerland (e-mail: [email protected]). Digital Object Identifier 10.1109/TEM.2012.2198066

Chesbrough’s [14] recent work on open service innovation, he claims that “the role of the customer, the interaction between customer and supplier, and the design of the supply chain may have to change in a services-oriented business model” [14, p. 9], underlining that service firms collaborate differently with external partners in innovation projects from manufacturing and high-tech firms. We also expect to find differences in external partnering for innovation between manufacturing and businessto-business (B-to-B) service firms for several reasons. B-to-B service firms (offering, for example, logistics services, facility management services, or security services) innovate through cocreation of services, not through internally focused R&D activities [14], invest less in internal R&D than do manufacturing or high-tech firms (as indicated by a firm’s R&D intensities) [35] and have traditionally lower knowledge requirements than do high-tech and manufacturing firms [68]. Therefore, studying how partners can help firms to stimulate innovation in a B-to-B service context is warranted and contributes to the literature. Another study of external partner collaboration for service delivery innovation found no relationship between external partner collaboration and innovation performance. Chen et al. [12, p. 48] argued that “such issues are generally examined in the context of manufacturing firms, which focus heavily on research and development and thus may have more interaction with external partners than do service firms.” Furthermore, they did not distinguish among different types of external partners in their model (instead, they combined the contribution of several external partners in a single construct), which does not allow for the differentiation of the contributions of the various types of partners as sources of innovation. They recognize that “different types of collaborative relationships may be better or worse for developing innovation practices” [12, p. 50]. This suggests that not all types of potential partners can make valuable contributions to the innovation activities of service firms. Therefore, in this study, we make a first attempt to shed light on the contribution of different types of partners to service innovation. In the B-to-B service context, we 1) show that the utilization of external sources of innovation can enhance firms’ innovation performance and 2) study which type of external source of innovation (which partner) will be related to new and/or improved service offerings. We have chosen the transportation and logistics service industry in Germany as our empirical setting for several reasons. First, innovation is critical for transportation and logistics service firms. Offering simple transport and warehousing services with a short-term operational approach is no longer sufficient for a firm to satisfy and keep its customers, much less to capture larger shares of the market and continue its growth. Therefore, successful firms, possessing the capability for continuous change and innovation, are capable of developing new services and of improving their existing processes and services [66], [85], [86].

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Second, despite the criticality of being innovative and becoming an innovator, the management of innovations has assumed only a marginal role in many transportation and logistics service firms, and the innovation activities of firms in this industry and the share of innovators are much lower than they are in other industries [62], [82]. That is, the transportation and logistics service industry would benefit from industry-specific insights on how to foster innovation. Third, in terms of revenues and employment, the transportation and logistics service industry ranks among the largest in Germany and, therefore, constitutes an important economic sector [43]. In sum, given the criticality of innovation in transportation and logistics service provision, coupled with the low level of innovation realized by firms in the industry, and the importance of the industry, studying innovation from an open innovation perspective will yield valuable knowledge for theory and practice. This paper is structured as follows: in Section II, we provide a situational background on innovation activities in logistics service firms, review the literature on collaboration for innovation, and formulate our hypotheses. Section III describes our secondary data sources and measures. Section IV presents the analysis and results, which are discussed in Section V. The paper concludes with implications and limitations.

competitive advantage, and improving the financial performance of logistics service firms, empirical research indicates that LSPs are not very innovative, do not engage in innovation management, and do, therefore, not exploit the full potential of innovation. Flint et al. [24] remark that most firms fail to engage in customer value-oriented logistics innovation processes since the scale means of these innovation processes are not toward the high end. Oke [62] investigates innovation performance across service industries and finds the lowest industry average for transportation service firms. Likewise, Wagner [82] shows that transportation and LSPs score lower than other industries on both innovation expenditures and innovation output. For example, the proportion of companies in the transportation and logistics service industry that successfully completed at least one innovation project within the past three years, thus enabling the introduction of new products or services into the market or the initiation of new operational processes in the company (so-called innovators), stood at a mere 30%, compared to 75% in the mechanical engineering and 72% in the chemical/pharmaceutical industry. Furthermore, very little research has been conducted on the management of innovation in LSPs: strategies, structures, processes, and human resources in innovation management [82]. Therefore, a much better understanding of how LSPs can generate innovation and manage the surrounding processes is needed.

II. BACKGROUND AND HYPOTHESES A. Innovation in Logistics Services

B. Interorganizational Collaboration for Innovation

Research on innovation at transportation and logistics service firms is just beginning to emerge. From this research, we can obtain initial insights into the positive effects of logistics service innovation and the level of innovation among logistics service providers (LSPs). Some empirical research has already investigated the relationship between “customer-oriented, relationship-specific innovation by an LSP” [85, p. 76] (which the authors label “proactive improvement”) and customer loyalty. The authors show that proactive improvement is positively related to several dimensions of customer loyalty, such as the retention of the transportation and logistics service firm’s customers, the extension of the business with the transportation and logistics service firm, and the referral of the firm to other customers [9], [85]. An empirical study of the customer value-oriented logistics innovation process which includes logistics service firms also found that this innovation and learning process, innovation performance, and overall performance are positively related [24]. The results of a survey of Hong Kong-based logistics service firms show that innovation in the relationship between the transportation and logistics service firm and the shipper has a strong positive impact on the logistics service firm’s effectiveness in the supply chain (in terms of its ability to fulfill orders, meet standards, and solve problems) and supply chain performance (in terms of cost and financial improvements) [66]. Furthermore, two surveys have demonstrated that a LSP’s innovation capability has a positive effect on logistics service performance which in turn improves the LSP’s performance [65], [86]. Even though innovation is critical for strengthening the LSP– customer relationship, generating customer loyalty, achieving

It is widely accepted that sources of innovation are now dispersed and reside more than ever outside of the firm, with other partners or in networks [16], [67], [69], [81]. Tushman [75, p. 410] observed that “much of the value created in both product and service industries are created outside a particular firm’s boundaries” and that this “will push our research to be more interorganizational.” Several empirical studies have hinted at the types and the potential benefits of utilizing partners as sources of innovation or collaborating with partners to create innovations. Such studies tend to focus on the development of hardware and technology (instead of services) and the manufacturing and high-tech industries, including biotechnology and pharmaceuticals (instead of B-to-B service firms) [63]. Based on their survey of 1800 German manufacturing firms, Fritsch and Lukas [28] conclude that 61% of these firms cooperate on R&D with customers, 49% with manufacturing suppliers, 33% with publicly funded research institutions, and 32% with other institutions. The propensity to cooperate on R&D with external actors is higher for firms that are large, have ambitious R&D goals, and a high R&D intensity. Furthermore, they are likely to have a “gatekeeper”: someone who systematically screens the environment for information that could foster the firm’s innovation activities. While Fritsch and Lukas [28] identify numerous variables that distinguish firms that cooperate on R&D from firms that do not, their study does not mention the performance implications of collaborative innovation. Gemünden et al. [29] study the types (discussing, generating new product ideas, conceptualizing new products, developing new products, and testing new products) and intensities (no importance–very high importance) of technological

WAGNER: PARTNERS FOR BUSINESS-TO-BUSINESS SERVICE INNOVATION

interweavement with external partners of 321 German high-tech companies. They show that customers and suppliers are of primary importance, and universities, engineering offices and consultants are of secondary importance to these high-tech firms. However, like Faems et al.’s [23] study of 221 Belgian manufacturing firms, this research emphasizes that firms should adopt a portfolio perspective and not manage its partners separately. Instead, “it is a strategic task to develop, manage, plan, and exploit a company’s network as a whole” [29, p. 460]. Based on a sample of 2707 manufacturing firms in the U.K., Laursen and Salter [47] study the breadth and depth of external search strategies for innovative ideas across eight knowledge sources or partners (suppliers, customers, competitors, consultants, commercial laboratories, universities, governmental research organizations, and private research organizations) and link them to innovation performance. They find that searching widely and deeply is curvilinearly related to innovation performance, indicating that “oversearching” might hinder innovation performance. Li et al. [51] analyze partner selection, governance structure, and alliance scope in a sample of 1159 R&D alliances in the high-tech industry. They find that firms choose alliance partners with whom they had multiple previous interactions and with whom they had developed a high level of trust when the alliance strives for a more radical innovation. Furthermore, the firms use partner selection, governance structure, and alliance scope to protect technological assets in the R&D alliance. Un et al. [76] study the impact of universities, suppliers, customers, and competitors on the number of product innovations in 781 manufacturing firms. Their theoretical arguments are based on the ease of knowledge access and knowledge breadth provided by the different collaboration partners. The authors find “that undertaking R&D collaborations is a rare event: about two-thirds of firms do not undertake any at all” [76, p. 682]. Furthermore, their results reveal that R&D collaborations with universities and suppliers have a positive impact on product innovation, while collaborations with customers have no impact, and collaborations with competitors have a negative impact. They conclude that ease of knowledge access is a more important driver for collaboration success than breadth of knowledge. While these studies inform us about the benefits and types of collaborative innovation and partner firms as sources of innovation in manufacturing and high-tech industries, there is limited empirical support in the B-to-B service context. Do B-to-B service firms benefit in similar ways from collaborative innovation with partners? Do they utilize other firms as sources of innovation? Which partners are more likely to influence the innovation output of B-to-B service firms? In the following section, we develop several hypotheses that shed light on such questions. C. Open Innovation, Knowledge-Based View, and Absorptive Capacity Several theoretical perspectives lend support to the study of external sources of innovation: the open innovation concept [13], [14], [21], the knowledge-based view [2], [31], [68], and the related concept of absorptive capacity [15], [46], [80].

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According to the open innovation literature, useful pools of external knowledge reside increasingly among various companies, such as customers, suppliers, universities, research labs, industry consortia, and start-up firms [42]. Therefore, firms can no longer afford to rely only on their own internal ideas. Innovation must shift from closed innovation, where firms do everything themselves and the success of innovation is determined by internal ideas and the control of internal resources, to open innovation, where firms combine internal with external ideas. Firms following the open innovation model are more innovative and are better able to grow on the market and achieve sustainable competitive advantage [13]. In the knowledge-based view of the firm, knowledge is a major source of competitive advantage [31]. When firms shift to open innovation and strive for the involvement of external partners as sources of innovation and the collaboration with external partners in their innovation activities, they need to transfer and integrate external knowledge. Menon and Pfeffer [59] demonstrate that firms value external knowledge more highly than internal knowledge, because it is “more scarce, which makes it appear more special and unique” [59, p. 497]. An organization’s knowledge requirements must match its knowledge investments. A simple knowledge fit exists if a firm’s knowledge requirements and its knowledge investments are low. In contrast, a complex knowledge fit exists if a firm’s knowledge requirements and knowledge investments of firms are both high. Knowledge investments only create value if such a knowledge match exists [68]. Traditionally, firms offering B-to-B services in general, and transportation and logistics services in particular, had lower knowledge requirements compared to most manufacturing and high-tech firms. The LSPs offered simple transport and warehousing services with a short-term operational approach. With increasingly higher service requirements from customers, more complex and value-added services, more volatile demand (e.g., due to the economic crisis), and competition on a global scale, the environment has become more dynamic and competitive. Grant [32] argues that with a dynamically competitive environment, the knowledge requirements of the firms grow, and this must be supported with higher knowledge investments in order to maintain a knowledge fit [68]. When firms “face a complex and dynamic environment, it becomes more important for them to acquire the information and know-how to be innovative” [68, p. 393]. Therefore, for LSPs to be innovative and perform well in today’s environment necessitates that they invest more into knowledge acquisition from external partners, and in developing absorptive capacity. Absorptive capacity as the ability to recognize, assimilate, and apply knowledge from external sources [15] goes beyond selecting the right external partners and being exposed to external sources of innovation. Absorptive capacity consists of the internal processes and capabilities which enable the firm to exploit the potential partners as sources of innovation and to collaborate with them in innovation activities [46], impact innovation outcome, and firm performance [26]. In terms of familiarity with the organization’s existing knowledge, innovation has been dichotomized into exploitative and

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explorative forms [6], [37], [57]. Exploitative learning with partners, which relies on the application of familiar skills and existing know-how [10], develops innovations which “improve established designs, and expand existing products and services” [37, p. 1662]. Exploitative innovations are designed to meet the needs of existing customers [18]. For example, transportation and logistics service firms regard service improvements made to an existing service as exploitative innovations. Unlike exploitative innovations, explorative innovations require departures from existing skills and the application of new knowledge [6]. Explorative innovations are meant to attract emergent customers and markets [18]. From the transportation and logistics service firm’s perspective, the introduction of services that the firm has not previously offered is an exploratory innovation. From the literature on open innovation, knowledge management, and absorptive capacity, we can conclude that firms’ efforts to utilize and learn from partners as sources of innovation can give them the ability to innovate through exploitation (service improvement) and exploration (new service development). Partners provide knowledge that can be used to supplement existing and prevailing knowledge, thereby enabling the achievement of service improvement. Similarly, firms can learn and garner knowledge from external partners that will enable them to question prevailing knowledge and create new ideas to deliver new services. In sum, we hypothesize the following: Hypothesis 1a: The utilization of external partners as sources of innovation is positively related to innovation performance (service improvement). Hypothesis 1b: The utilization of external partners as sources of innovation is positively related to innovation performance (new services).

While the positive effects of the utilization of external sources of innovation per se are evident, it is difficult to predict which sources (e.g., suppliers, customer, competitors, consultants, or universities) are likely to have a positive impact on innovation performance and are used by transportation and logistics service firms in corporate practice in order to achieve innovation output. Research in the manufacturing or high-tech industries shows that these partners are used as sources of innovation to different degrees (see, e.g., [7], [16], [28], and [81]), indicating that these partners also play several roles in B-to-B service innovation. As Argote et al. [2] argue, the transfer of knowledge between units in dyadic social relationships and the integration of this knowledge depends on a number of contingency factors such as social similarity, intensity of the connection, contact frequency, and level of communication. The potential partners with whom LSPs can collaborate vary substantially concerning these contingencies. Customer, competitor, and supplier firms (and their employees) are more similar to the LSPs than consultants and universities (and employees, faculty, staff, and students). While the former are mostly logistics experts operating in the same industry, the latter are frequently not industry experts and also work with partners in other industries. Likewise, the former typically have similar types of education and training—less academic

and more on-the-job training. In contrast, the latter typically hold postgraduate university degrees. Contact frequency will be higher, intensity of the connection will be stronger, and communication will be more intensive between LSPs and their customers and suppliers as opposed to collaborations between LSPs and competitors, consultants, and universities. In innovation projects, external partners’ private (i.e., soft, nonstandard and idiosyncratic) knowledge is more valuable than public knowledge. Intensive connections, frequent contacts, and intensive communication—as they prevail in embedded ties with more frequent social interactions—are favorable for transferring private knowledge and new innovations to an exchange partner [77]. That is, tie strength will influence which type of external sources of innovation will be used and how these external sources of innovation will influence the development of new services and improvement of existing services [34]. Tie strength will vary by type of partner with LSP–customer relationships being the strongest, and LSP–competitor relationships being the weakest. Suppliers, consultants, and universities are somewhere in the middle. Other than tie strength, the type of contribution that each source brings will affect the type of innovation that LSPs achieve. For example, because of their knowledge of and closeness to the market, customers are likely to offer knowledge about new markets and new service offerings. Suppliers, on the other hand, are likely to offer knowledge about service improvements because of their detailed knowledge of processes. In sum, given these arguments from the knowledge-based view, we can formulate the following more exploratory hypotheses: Hypothesis 2a: The benefit of utilizing external partners as sources of innovation for service improvement depends on the type of partner. Hypothesis 2b: The benefit of utilizing external partners as sources of innovation for new services depends on the type of partner.

III. METHODS A. Data For this study, we draw on secondary data [41], [70] on the innovation behavior of German firms. The German innovation survey [i.e., the Mannheim Innovation Panel (MIP)] has a long history and in 2005 was again part of the wider Community Innovation Survey which has been conducted since 1993 in several member states of the European Union. For a more extended description and discussion of the German innovation survey and some background information on its development since 1993, we refer to [3] and [38]. The standardized and large-scale survey was administered in 2005 by the Centre for European Economic Research (ZEW) on behalf of the German Federal Ministry of Education and Research (BMBF). The Community Innovation Survey adheres to the generally accepted conventions of scientific research. It is based on the core methodology and measures defined and documented in the third edition of the Oslo Manual which was issued in 2005 by the Organisation for Economic Co-operation and Development [61]. The Oslo Manual provides meticulous


and well-established guidelines for collecting and interpreting innovation data. Prior to implementation, the questionnaire was subjected to extensive pilot testing in order to ensure its understandability, reliability, and validity [22]. The sample frame for this research consisted of all firms with five or more employees in Germany’s transportation and logistics service industry: the Nomenclature générale des activités e´ conomiques dans les Communautés Européennes (NACE) classification 60 to 63 and 64.1. The minimum firm size was deliberately set by the executing and funding bodies—the ZEW and the BMBF—to account for the importance of small- and medium-sized enterprises in the German economy. The sample was randomly drawn from the Creditreform database and included 2219 transportation and logistics service firms. The questionnaires were sent out by postal mail in March 2005 and returned by July 2005. The sample provided by the ZEW contains 390 firms for a usable response rate of 17.6%. In order to test for a possible bias in the firms’ response behavior, 264 firms were selected at random from the nonresponding firms and interviewed by telephone regarding the survey’s core variables (August/September 2005) [84]. From the analysis, the ZEW concluded that nonresponse bias was not present in the data. The data were collected on the firm level. To protect the confidentiality of the participating firms, the ZEW—like the other national organizations responsible for the individual EU countries’ Community Innovation Survey—does not provide further details on the responding firms’ profiles or industry breakdowns beyond the NACE classifications presented above. B. Measure of Variables All measures used in this study (see the Appendix) were drawn from the 2005 Community Innovation Survey questionnaire—which solicited information pertaining to innovation activities in 2004. For reasons of confidentiality, the ZEW releases the data in microaggregated form and builds categories for a few selected measures that might reveal the identity of individual firms. Mairesse and Mohnen [56] have shown that microaggregation does not confound the analysis compared to the nonaggregated original data. Innovation performance: Following Leiponen [49], we measure innovation performance as the transportation and logistics service firms’ ability to generate innovations that result in: 1) improved service offerings; and 2) new to the firm services. These two variables were measured as the percentage of the firms’ annual sales pertaining to services improved and services new to the firm [47]. Data were available for nine categories (0%, 0% < x < 5 %; 5% ≤ x < 10%; 10% ≤ x < 15%; 15% ≤ x < 20%; 20% ≤ x < 30%; 30% ≤ x < 50 %; 50 ≤ x < 75; 75 ≤ x ≤ 100 %) (0-1-2-3-4-5-6-7-8 coding). Together with the services that were unchanged, the sales add up to 100%. Innovation source: Newly created variables reflect the degree to which the transportation and logistics service firms use each of the potential partners (i.e., suppliers, customers, competitors, consultants, and universities) as a source of innovation. The variables are constructed as a combination of: 1) the use of the potential partner as a source of knowledge or infor-

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mation for innovation; and 2) the involvement of the firm in interorganizational collaborations with the partners in innovation projects. The rating of the firm’s knowledge or information usage from each partner (not used, low, medium, high) (0-1-2-3 coding) and the rating of the firm’s involvement in interorganizational collaborations with the partners (no collaboration, collaboration with German partner, and/or collaboration with foreign partner) (0-1-2 coding) were summarized. The increase of the score with the extension from German to foreign partners conforms to the idea that firms scan, search, and explore opportunities, both “locally” and “distant” [58], [60]. Hence, the variables range from 0 to 5, reflecting the degree of use of each potential partner as a source of innovation. In addition, we calculated the mean usage of external sources of innovation over all five potential partners. C. Control Variables We control for variables that can confound the results. Firm size can influence the outcome of the transportation and logistics service firms’ innovation activities [72]. On the one hand, the human and financial resources available to larger firms may make them more successful in their innovation efforts. On the other hand, smaller firms might be more entrepreneurial and bring about more inventions than larger firms. Likewise, greater firm size significantly reduces the risk of cooperation failures in innovation cooperations [50]. Since the purpose of this research was to ascertain the effects of transportation and logistics service firms’ innovation apart from firm size effects, we eliminated this undesirable source of variance by including number of employees as a measure of firm size. Data were available for three size classes (fewer than 50 employees; 50–249 employees; 250 employees or more) (1-2-3 coding). We investigate the influence of our focal constructs independent from the transportation and logistics service firms’ linkages with global markets, because a firm’s geographical reach of its services and perhaps the reach of its partners could affect innovation performance [26], [47], expecting that “firms which operate in international markets are exposed to a higher level of competition, which may affect both the propensity to innovate and the innovation performance” [27, p. 1130]. Therefore, we included the control variable geomarket [40], [47] that measures whether the transportation and logistics service firm perceives its market to be local/regional, national, European, or beyond Europe (1-2-3-4 coding) and another control variable that captures its export intensity [11], [26] calculated as the total exports to sales ratio. A firm’s competitive strategy will also have a great bearing on the necessity and meaningfulness of innovation activities [52]. Transportation and logistics service firms that compete mainly on price will emphasize innovation to a lesser degree than do firms that compete on the variety of service offerings or service quality. We included a variable that controls for the competitive strategy of the firm which measures the importance of price as a competitive factor on the firm’s market. The variable ranges from 1 (price is the most important competitive factor) to 6 (price is the least important competitive factor).

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TABLE I DESCRIPTIVE STATISTICS AND CORRELATIONS Construct

Mean Std Dev

(1) Firm size

1.57

0.73

(1)

(2)

(4)

(3)

(5)

(6)

(7)

(8)

(9)

(10)

(2) Geomarket

2.03

1.03

0.01

(3) Export intensity

0.06

0.18

-0.07

0.51**

(4) Competitive strategy

1.65

1.07

-0.06

0.05

0.02

(5) Innovation source – All partners

0.71

0.94

0.30** -0.02

-0.02

0.10*

(6) Innovation source – Suppliers

0.81

1.11

0.32** -0.02

0.02

0.13*

(7) Innovation source – Customers

0.99

1.26

0.29**

0.01

0.03

0.05

0.89**

0.72**

(8) Innovation source – Competitors

0.69

1.01

0.19** -0.02

0.02

0.10

0.86**

0.65**

(9) Innovation source – Consultants

0.35

0.75

0.29** -0.03

-0.03

0.11*

0.77**

0.59**

0.48 **

0.45**

(10) Innovation source – Universities

0.23

0.63

0.25** -0.05

-0.03

0.15**

0.71**

0.43**

0.47 **

0.51**

(11) Service improved

1.20

2.19

0.17**

0.08

-0.03

0.08

0.70**

0.76**

0.75 **

0.67**

0.49**

0.39**

(12) Service new to firm

0.29

1.00

0.11

0.07

-0.06

-0.02

0.32**

0.34**

0.39 **

0.30**

0.25**

0.18**

(11)

0.87** 0.78 ** 0.56** 0.49**

** Correlation is significant at the 0.01 level (1-tailed). * Correlation is significant at the 0.05 level (1-tailed).

TABLE II RESULTS OF THE TOBIT ANALYSES FOR TESTING HYPOTHESES 1a AND 1b

Model Dependent variables Independent variables Intercept

1

2

Service improved Coefficient

SE

-6.116***

1.555

Service new to firm Coefficient

SE

-9.464**

3.386

Firm size

0.169

0.483

0.281

0.712

Geomarket

1.095**

0.405

1.418†

0.741

Export intensity

-4.873

3.400

-6.539†

3.601

Competitive strategy

-0.016

0.278

-0.543

0.687

Innovation source – All partners

4.183***

Censored observations Log likelihood

0.546

2.751**

0.954

118

160

-173.2494

-98.1367

6.8594*

31.8774***

Chi-square *** Significant at the 0.001 level. ** Significant at the 0.01 level. * Significant at the 0.05 level. † Significant at the 0.10 level.

IV. ANALYSIS AND RESULTS A. Descriptive Statistics Table I includes means and standard deviations of the dependent, independent, and control variables. Two noteworthy observations emerge from the descriptive statistics. First, the innovation output is rather low. The mean of improved services of 1.20 translates into 6.0% of the firms’ annual sales which are accounted for by improved services. The mean of services new to the firm of 0.29 translates into 1.5% of the firms’ annual sales which are accounted for by services new to the firm. Second, customers are used most intensively as sources of innovation (0.99 on a scale ranging from 0 to 5), followed by suppliers (0.89), competitors (0.69), consultants (0.35), and universities (0.23). However, these low values indicate that external sources of innovation, on average, are very little used.

B. Tobit Regression Constructing a model for explaining improved logistics services and services new to the firm was complicated by the potential for no improved or new to the firm services and the upper limit of 100% on both dependent variables. Ignoring the existence of truncation in the dependent variable Y and using ordinary least square regression of Y on the independent variable X using the positive observations on Y only would lead to biased regression estimates β. For this reason, we used a censored Tobit regression technique that allows for estimating linear relationships between variables when there is censoring in Y [33, pp. 871–874], [54, pp. 196–216], [74]. Table II presents the results of the test for hypotheses 1a and 1b, stating that the use of external partners as sources of innovation is positively related to innovation performance. Model 1, explaining the extent to which services were improved, shows


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TABLE III TOBIT ANALYSES FOR TESTING HYPOTHESES 2a AND 2b

Model Dependent variables Independent variables Intercept Firm size Geomarket Export intensity

3

4

Service improved

Service new to firm

Coefficient -7.266*** 0.194 0.944*

SE

Coefficient

SE

2.179

-12.969*

6.240

0.459

0.491

0.884

0.420

2.120*

0.942

-5.071

9.083

-50.747

75.629

Competitive strategy

0.308

0.362

-0.739

1.069

Innovation source – Suppliers

1.642***

0.384

0.830

1.012

Innovation source – Customers

1.849***

0.440

3.182*

Innovation source – Competitors

1.003*

0.487

-0.595

1.542 0.893

Innovation source – Consultants

-0.306

0.509

-0.262

1.219

Innovation source – Universities

-0.675

0.707

0.785

1.470

Censored observations Log likelihood Chi-square

117

152

-121.5675

-66.7524

14.2156***

29.4187***

*** Significant at the 0.001 level. ** Significant at the 0.01 level. * Significant at the 0.05 level.

that the estimated effect of partners as sources of innovation is highly significant with a positive sign (standard coefficient = 4.183; p < 0.001). Likewise, Model 2, explaining the relationship between the use of external partners and services new to the firm, shows that the estimated effect is also statistically significant with a positive sign (2.751; p < 0.01). Both models were statistically significant with a chi-square of 6.860 (p < 0.05) and 31.877 (p < 0.001). In sum, Hypotheses 1a and 1b are supported. Table III contains the results of the test for hypotheses 2a and 2b, stating that the benefits of utilizing external partners as sources of innovation vary by the type of partner. Model 3, explaining the extent to which services were improved, shows that the estimated effects of suppliers (standard coefficient = 1.642; p < 0.001), customers (1.849; p < 0.001), and competitors (1.003; p < 0.05) as sources of innovation were statistically significant with positive signs. Model 4, explaining the extent of services new to the firm, shows that the estimated effect of customers as a source of innovation was statistically significant with a positive sign (3.182; p < 0.05). Both models were statistically significant with a chi-square of 14.215 (p < 0.001) and 29.419 (p < 0.001). In sum, while customers, suppliers, and competitors are sources of innovation that are positively related to improved services (and consultants and universities are not), and only customers are sources of innovation that are positively related to services new to the firm, the analysis clearly supports hypotheses 2a and 2b. V. DISCUSSION AND IMPLICATIONS We drew on open innovation [13], [14], [21], the knowledgebased view [2], [31], [68], and the concept of absorptive capacity [15], [46], [80] to extend our understanding of how B-toB service firms can enhance their innovation performance by

opening up the boundaries of the firm and using external partners (suppliers, customers, competitors, consultants, and universities) as sources of knowledge and innovation. We advanced the notion of open innovation in the B-to-B service context. The theoretical and practical inferences that can be drawn from our results are based on an empirical study of transportation and logistics service firms’ practices. The discussion of the results and implications is, again, organized around the hypothesized relationships and analysis. First, researchers have recently hinted at the importance of involving partners in service innovation and development [1], [63] and highlighted that a better understanding of the different types of relationships and contributions of various partners is necessary for enhancing the service firms’ innovation performance [12]. Our study adds to the literature by showing that external partnering and the exchange of external knowledge [59] can indeed be a valuable source of innovation for B-to-B service firms that improves innovation output and results in improved or new services. Provided that improved and new services will lead to better market and financial performance of the firm, this study underscores that knowledge transfer [31] and the recognition, assimilation, and application of external knowledge [46] can potentially explain differences in LSPs’ competitive positions. Second, customers, suppliers, and competitors seem to be important sources of innovation for the improvement of B-toB services. Therefore, if LSPs aim to enhance the share of improved services (e.g., because service improvement leads to customer satisfaction and customer retention) [36], [45], they should intensify their internal innovation activities and their collaboration with customers, suppliers, and competitors. Third, to enhance the development of new B-to-B services, partnering with customers is beneficial. In other words, customers can assist LSPs in generating more radical innovations which go beyond service improvement. This important

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finding supports the conceptual idea of logistics innovation as a customer value-oriented social process where logistics innovation occurs through interorganizational learning at the LSP– customer interface [25]. Furthermore, it supports the concepts of coservice creation [79] where engagement with customers “becomes pivotal in the development, design, and delivery of innovative products and services” [1, p. 434] and lead user collaboration where potential new service designs are sought from customers located at the leading edge of important markets [53], [73]. As such, LSPs that intend to increase the share of new services should add the use of customers as sources of innovation and the collaboration with customers to their internal innovation activities. The criticality of customers as sources of innovation is in contrast to the findings of an earlier study in the manufacturing industry that found that customer collaboration in R&D did not have an impact on product innovation. Un et al.’s [76, p. 679] argument is that “accessing knowledge from customers to innovate products is difficult for a number of reasons.” Customer needs cannot be easily articulated (to be integrated in the supplier firm’s product development activities). Furthermore, customers do not have an incentive to share tacit knowledge with suppliers. This is unlike in the LSP–customer relationship, where LSPs can more easily access the customers’ knowledge, and where customers have an incentive to share, because logistics customers will immediately benefit from often idiosyncratic (customer-specific) improvements of services or new services. While the low degree of interweavement and the small contribution of universities (or research institutions) and consultants—compared to that of other partners—has also been shown to be the case in manufacturing and high-tech industries (see, e.g., [28], [29], and [81]), the insignificant relationship between partnering with consultants and universities and service improvement and new service development might be surprising at first glance. However, there are several reasons why the collaboration with universities and consultants is not related to innovation performance. First, from a knowledge-based view, consultants and university researchers are quite dissimilar to employees of LSPs (e.g., with respect to industry know-how or education) which leads to less intensive and less valuable knowledge transfer between the organizations [2]. For example, universities and consultants as innovators in the field of transportation and logistics services are neither the customers nor the users of the services once they are developed. That means that universities and consultants might have little knowledge of markets and customers [4], [55], which is necessary to develop and sell an improved or new service and to increase their share in total sales. Second, consultants and universities will be a valuable source of innovation for the development of innovation strategies, processes, and structures that are supportive of future service improvement and new service development efforts at the transportation and logistics service firm. However, they will not immediately contribute to the development or improvement of a specific service. Third, there might be conflicts of interest and misalignments of incentives between universities (e.g., culture of relatively free

inquiry, requirements of publication, and demands for intellectual property rights) and LSPs (e.g., interest in the fast implementation of service solutions in order to generate additional sales and profits) [16], [17]. The fact, that on average, the innovation output of the transportation and logistics service firms in terms of the share of improved as well as new services in relation to total sales is very low accentuates the importance of our study’s results. The finding of a low degree of innovativeness is in line with recent research [62], [82]. If firms partner with suppliers, customers, and competitors for service improvement and with customers for new services, they will be able to foster innovation and set themselves apart from the majority of transportation and logistics service firms that do not avail themselves of these sources of innovation. Furthermore, since the current use of partners as sources for B-to-B service innovation is also very low, potential partners working with the transportation and logistics service industry will not be “overloaded” with requests to establish links with transportation and logistics service firms. Therefore, firms that are able to build relational capabilities and form partnerships with customers, suppliers, and competitors [19], [39] will have a huge reservoir of partners from which to draw. In addition, as shown by Flint et al. [24], few LSPs engage in logistics innovation with their customers. Therefore, customers might be very receptive to innovation activities with the handful of LSPs that are seeking collaboration and want to transfer knowledge for the development of service innovations. VI. LIMITATIONS AND CONCLUSION This study is limited by several factors that could be addressed in future research. First, although our causal inferences are grounded strongly in the open innovation, knowledge-based view, and absorptive capacity literature, the results of this crosssectional study need to be confirmed by longitudinal studies. Second, our sample is confined to transportation and logistics service firms in Germany. Future studies should examine whether the relationships reported here differ across countries and hold in other B-to-B service firms. Third, the analysis of large-scale secondary data collected in the MIP innovation survey leaves many questions unanswered; these can only be investigated with more direct observational research methods [70], [71]. Since the empirical base underlying this research is confined to the variables collected in the MIP innovation survey, it would be useful to conduct additional studies, both by collecting primary data through large-scale surveys or by conducting in-depth case studies with transportation and logistics service firms where interview guidelines and survey instruments can be tailored specifically to additional research questions. Two such important extensions of the current study are discussed next. Fourth, additional insights need to be generated under which conditions external partners as sources of innovation more likely will enhance service improvement or new service development. That is, future studies should investigate how contingencies such as the B-to-B service firm’s organizational design (e.g.,


LSPs are frequently more decentralized) and communication within the firm and with the potential partner influence the selection of a partner and the collaboration with partners (see, e.g., [5] and [30]). Furthermore, the influence of relationship antecedents, connectors, or mediators, such as relationship quality, commitment, trust, relationship tenure, or relationship diversity (see, e.g., [8], [20], [44], and [64]) in the context of external partnering for B-to-B service improvement and development should be studied. Fifth, despite the initial insights provided in this research on the benefits of accessing valuable knowledge outside the firm for improving and creating services, research should be devoted to the tension between knowledge transfer and knowledge appropriation as well as value creation and value appropriation in interorganizational service development [48], [83]. Questions to be answered include: Who (the B-to-B service firm and/or the partner) will benefit from the improved or new service benefits? How do value creation and value appropriation mechanisms influence the service firm’s willingness to tap outside sources of innovation and the openness of the partner to share knowledge? Although subject to verification and refinement through additional research, our study integrates the open innovation and the science of service concepts to elucidate how B-to-B service firms can enhance innovation performance by utilizing external partners as sources of innovation.

APPENDIX QUESTIONNAIRE ITEMS The following questionnaire items of the 2005 Mannheim Innovation Panel/Community Innovation Survey were used for this study. Dependent variables: 1) Please estimate how your total sales in 2004 were distributed between the following categories: (Percentage) a) Products/services introduced during 2002–2004 that were significantly improved. b) Products/services introduced during 2002–2004 that were new to your firm. Independent variables: 1) How important to your firm’s innovation activities during the three-year period 2002–2004 were each of the following information sources? (Not used; Low; Medium; High) a) Suppliers of equipment, materials, services, or software. b) Clients or customers. c) Competitors or other firms in your industry. d) Consultants, commercial labs, or private R&D institutes. e) Universities or other higher education institutions. 2) Did your firm cooperate on any of your innovation activities with other firms or institutes during the three-year period 2002–2004? If so, which types of cooperation partner did you use and where were they located?

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(No; Germany; Other European countries; United States; Other countries) a) Suppliers of equipment, materials, services, or software. b) Clients or customers. c) Competitors or other firms in your industry. d) Consultants, commercial labs, or private R&D institutes. e) Universities or other higher education institutions. Control variables: 1) What was your firm’s total number of employees in 2004? 2) In which geographic markets did your firm sell goods and/or services during the three year period 2002-2004? (Local/regional—within 50 km; National—within Germany; European Union; Other countries) 3) What was your firm’s total sales for 2004? (Euros) 4) What was your firm’s total exports for 2004? (Euros) 5) How important were the following factors for the competition in your main market? (1: Most important; 6: Least important) a) Price. ACKNOWLEDGMENT The author is grateful to the Centre for European Economic Research for granting access to the data. REFERENCES [1] R. Agarwal and W. Selen, “Dynamic capability building in service value networks for achieving service innovation,” Dec. Sci., vol. 40, no. 3, pp. 431–475, Aug. 2009. [2] L. Argote, B. McEvily, and R. Reagans, “Managing knowledge in organizations: An integrative framework and review of emerging themes,” Manage. Sci., vol. 49, no. 4, pp. 571–582, Apr. 2003. [3] B. Aschhoff, T. Doherr, B. Ebersberger, B. Peters, C. Rammer, and T. Schmidt, Innovation in Germany: Results of the German Innovation Survey 2005. Mannheim, Germany: Centre for European Economic Research, 2006. [4] K. Atuahene-Gima, “An exploratory analysis of the impact of market orientation on new product performance: A contingency approach,” J. Product Innovation Manage., vol. 12, no. 4, pp. 275–293, Sep. 1995. [5] Y. F. Badir, B. Büchel, and C. L. Tucci, “The performance impact of intrafirm organizational design on an alliance’s NPD projects,” Res. Policy, vol. 38, no. 8, pp. 1350–1364, Oct. 2009. [6] M. J. Benner and M. L. Tushman, “Exploitation, exploration and process management: The productivity dilemma revisited,” Acad. Manage. Rev., vol. 28, no. 2, pp. 238–256, Apr. 2003. [7] L. Berchicci, “Heterogeneity and intensity of R&D partnership in Italian manufacturing firms,” IEEE Trans. Eng. Manage., vol. 59, no. 4, pp. 674– 687, Nov. 2011. [8] E. Briggs and D. Grisaffe, “Service performance-loyalty intentions link in a business-to-business context: The role of relational exchange outcomes and customer characteristics,” J. Service Res., vol. 13, no. 1, pp. 37–51, Feb. 2010. [9] D. L. Cahill, Customer Loyalty in Third-Party Logistics Relationships: Findings from Studies in Germany and the USA. Heidelberg, Germany: Springer, 2007. [10] L. B. Cardinal, “Technological innovation in the pharmaceutical industry: The use of organizational control in managing research and development,” Organization Sci., vol. 12, no. 1, pp. 19–36, Jan./Feb. 2001. [11] B. Cassiman and R. Veugelers, “In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition,” Manage. Sci., vol. 52, no. 1, pp. 68–82, Jan. 2006.

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Stephan M. Wagner received the M.B.A. degree from Washington State University, Pullman, and the Ph.D. and Habilitation degrees from the University of St. Gallen, St. Gallen, Switzerland. He is currently a Full Professor, holds the Kuehne Foundation-sponsored Chair of Logistics Management, and is the Director of the Executive MBA in Supply Chain Management at the Swiss Federal Institute of Technology Zurich (ETH Zurich), Zurich, Switzerland. He is author and editor of ten books and more than 100 book chapters and articles. He has published in journals such as Journal of Operations Management, Decision Sciences, Academy of Management Journal, Journal of Management, California Management Review, or IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. His research interests include the areas of supply chain management, purchasing and supply management, logistics and transportation management—with a particular emphasis on strategy, networks, relationships, behavioral issues, risk, innovation, and entrepreneurship. Prof. Wagner is a member of the Academy of Management, the American Marketing Association, the Production and Operations Management Society, the Decision Sciences Institute, the Council of Supply Chain Management Professionals, the International Purchasing and Supply Education and Research Association, and the German Operations Research Society. He serves as an Associate Editor of Decision Sciences, the Journal of Supply Chain Management, the Journal of Business Logistics, and the Journal of Purchasing & Supply Management.