CROSS-FUNCTIONAL innovation teams can make novelties between functional domains [1], integrate up and downstream knowledge to facilitate the transition to production [2], and break down knowledge barriers between functional departments [3]. In the innovation literature, the general assumption is, therefore, that cross-functionality contributes tothe performance of innovation teams [4][6].However, the results from empirical data and meta-analyses that have looked at cross-functionality of innovation teams andtheir impact on performance are mixed. Brown and Eisenhardts meta-analysis [4] shows that cross-functional teams contribute to performance. As does the more recent meta-analysis ofEvanschitzky et al. [7]. However, Sivasubramaniam et al. [8]found no impact of functional diversity on any new product development team performance indicator, neither did Henard andSzymanski [9]. The latter even report a significant negative relationship between cross-functional teams and performance in service firms. Sivasubramaniam et al. suggest that the wide credibility interval they found in their study hints at the presence of moderators. The findings of Henard and Szymanski suggest that firm characteristics, i.e., the organizational context in which innovation teams operate, may moderate the relationship between cross-functionality and performance. Studies that have looked into moderators that affect the relationship between cross-functionality of the team and new product development performance have focused either at the team level, i.e., factors that enhance the communication within teams, or at the firm level, i.e., factors that facilitate new product development performance in general. What is missing are multilevel studies that investigate organizational factors that moderate the team level relationship between cross-functionality and innovation team performance. The research question of this study is whether there are too many organizational contexts that make multifunctional teams more or less effective, regardless of the level of collaboration at the team level. This study is unique in several ways. First, it focuses on the organizational context in which cross-functional teams operate. In spite of the many calls for such studies [6], [10], [11],multi-level studies that include the context in which the team operates are rare [6]. Second, in the innovation literature, there has been a preference for studying manufacturing firms [12]; the focus of this paper is on both manufacturing and service firmsin the construction, IT, engineering, and allied sectors. These industries are expected to demonstrate a wide variation in organizational contexts in which innovation activities take place. Third, it uses a large sample (N = 142) of innovation teams in95 firms. Such large sample sizes are uncommon in team studies[6]. Fourth, performance is assessed as the perceived commercial outcome of the innovation project. This is consistent with how performance is defined in the innovation literature [4], [12]and is closely aligned with actual performance [13]. In the team literature, however, the focus is more often on the teams performance, which does not necessarily coincide with successful commercial outcomes [14], [15].As a result, this study provides a unique perspective on the benefits and limits of cross-functional innovation teams. Although heralded in the innovation literature and widely prevalent in practice, cross-functional innovation teams are shown to be less relevant for some types of firms. Cross-functionality contributes to the performance of innovation projects in more functionally organized firms, with a separate innovation unit, and above-average levels of organizational connectedness. These characteristics match the organizational context of the traditional manufacturing firm, prevalent in the innovation management literature [12]. However, the firm of the future tends to be more project-based [16], with innovation activities dispersed II. INNOVATION TEAM AND ORGANIZATIONAL CONTEXT Manufacturing firms have been most frequently used in innovation management research [12]. Most of these firms have similar organizational structures, i.e., functional units with a dedicated research and development department; hence, the organizational context in which innovation takes place is rather homogeneous for these types of firms. On the contrary, in the services industry, many alternative organizational forms exist[21], and in these alternative types of organizations, innovation is often more integrated in the day-to-day activities of these firms [17], [22]. How do these various types of context in which innovation teams operate impact their performance? Based on the organizational management and knowledge transfer literature, three organizational variables are defined that capture difference aspects of the various context in which innovation teams operate, and that potentially could impact how teams interact with their context to handle cross-functional knowledge. Organizational type captures the organizational structure of the organization at the firm level. A firms organizational structure [23], [24], its project-management capabilities [25], and the kind of outputs produced [21] together are used to provide an indication about the formal interaction between functional units at the firm level. Degree of separation of the innovation activities describes whether innovation activities take place in separate innovation units or are more integrated within the firms daily activities. Organizational connectedness captures the informal knowledge sharing patterns regarding cross-functional knowledge in an organization. These informal communication structures are found to be more powerful at explaining communication behavior at the organizational level than formal organization structures Cross-functionality of the innovation team is the perception of the project leader regarding the cross-functionality of the innovation team. This practical approach was chosen, because innovation teams typically have fluid boundaries [6], which makes it difficult to create a composite measur based on the background of individual team members. Cross-functionality of the innovation team takes into account the participation of different disciplines, functional departments and the involvement of marketing ( = 0.74). Marketing is singled out as a discipline. Teams consisting of various technical disciplines only could already be considered cross-functional in more project-based types of organizations. In the context of innovation projects, cross-functionality typically implies not only technical disciplines, but also the involvement of marketing [60]. The confirmatory factor analyses shows excellent fit (2 = 0.40, df = 2, GFI = 0.997, SRMR = 0.02, CFI = 1.00), with significant factor loadings for all items. Type of organization captures whether an organization is more functional or more project-based. It assesses the organizational structure of the firm, its outputs, and skills [21], [25]. A formatively indicated construct was used to model these various aspects in one construct [61], [62]. Innovation leaders answered which organizational form described their organization best; an operational processes resembling a project-based production process (reverse coded), having a mass production process, delivering customized goods and/or services (reverse coded), or delivering standardized goods. Project-management capabilities and the organizational structure of the organization were used as global indicators to identify the model [62], [63]. The confirmatory factor analysis shows good fit (2 = 6.64, df = 2, GFI = 0.99, SRMR = 0.04, CFI = 0.93) or [2 = 4.60, df = 2, GFI = 0.99, SRMR = 0.02, CFI = 0.98]. All item loadings are significant, with the exception of delivering customized goods and/or services. Leaving out items because of insignificant loadings is not considered good practice for formative constructs, as it alters the meaning of the construct [61], [63]. For that reason, all items were included in the construct. Degree of separation of the innovation activities is measured at the organizational level and assessed by the innovation manager. The operationalization of this concept is in line with the notion of integration-separation by Lawrence and Lorsch [64]. It covers the extent to which the innovation unit has autonomy over its own budget, to which extent innovation activities are formalized, and to which extent the innovation activities are primarily the responsibility of this innovation unit. Cronbachs alpha for this construct is [0.75] 0.72. The confirmatory factor analysis shows significant factor loadings for all items and excellent fit (2 = 3.36, df = 1, GFI = 0.99, SRMR = 0.04, CFI 0.97) [2 = 7.26, df = 1, GFI = 0.98, SRMR = 0.04, CFI = 0.95]. Organizational connectedness is measured at the organizational level and assessed by the innovation manager. The operationalization of this construct is taken from Pinto and Pinto [65]. It is operationalized as the degree to which functional departments and disciplines acknowledged each others expertise and how common this type of communication is within the organization.Methodology A. Sample and Questionnaire Design The sample used for this research consists of various types of firms, i.e., project-based and more functionally organized firms, with integrated and separated innovation activities, in the construction, information technology, and engineering industries. From the Reach database, across these industries, 1200 firms were selected, all firms with more than 50 employees. The innovation managers of these firms were invited by telephone to participate in this research. To make comparisons across industries and different types of firms possible, an innovation project was defined as a projecting which a new product or service was developed and commercialized for more than one customer. Project-based types of organizations also develop innovations in the projects executed to customer order, for example, an engineering firm builds anew factory for a customer. These types of innovations were excluded from this research. B. Response Rate and Sample BiasTwo different internet-based questionnaires were used. The first was sent to the innovation manager. The second questionnaire was sent to project leaders. Of the 1200 firms, 720 innovation managers agreed to participate by phone. Of these managers203 (28%) filled out the online questionnaire. These innovation managers named 257 innovation projects and provided thee-mail addresses of 213 project leaders. One hundred and forty eight of these project leaders responded (69%). Some projects were deleted because the name provided by the project leader did not match the name mentioned by the innovation manager. The used dataset contains information of 142 projects. Of these projects, 96 are pairs within the same firm. Using MANOVA, no overall significant difference was found between firms that provided one or two projects (Wilks lambda = 0.83, F 1.62p > 0.05). Variables Originally, each variable was derived from the literature. However, during the pretests in the various industry settings, a substantial number of questions had to be adapted to be applicable, relevant and clear to the respondents in the different industries. Several tests were applied to verify the validity of the modifications. Cronbachs alpha was used to verify the reliability of each reflective construct. For the formatively indicated type of organization construct, the procedure described by Mackenzieet al. [51] was used. The unidimensionality and discriminant validity of the constructs was verified using confirmatory factor analyses. Discriminant validity was tested using a method that is assigned to Anderson and Gerbing as well as Bagozzi and Phillips [52]. This method compares the difference in chi-squ are between pairs of constructs for an unconstrained and constrained model, using covariance based structural equation modeling. To support discriminant validity at the 5% significance level, the difference in chi-square value between the constrained and unconstrained model needs to be larger than 3.84 [52].If applicable, data from all 203 firms were used to verify the validity of the variables. These results are shown in square brackets. Performance is the projects commercial outcome, as perceived by the innovation manager. Asking for objective performance measures negatively impacts the response rate in innovation surveys [13], while using perceptions thereof leads to similar results [13], [53].To avoid common method bias, the innovation managers scores of performance were used [54]. Innovation managers typically have a broader perspective [55] and, therefore, provide a more reliable assessment of external market-related aspects of innovation projects [15].Performance is measured using the items for financial and customer-based performance of Griffin and Page [56]: the perceived impact of the new product or service on competitive advantage, the match with client needs, adherence to profit targets, and adherence to revenue targets. Market share was omittedafter the pre-test, as it appeared difficult to answer for the more project-based type of firms [see also 57, p. 46]. Perceived gain in reputation in the area of the new product or new service was added. This is an important aspect of performance in project based [58] and in service firms [59]. The Cronbachs alpha of the performance construct is = 0.86. A.Limitations Although the R, R2 , and R2 adjusted values in this study are typical for team studies, or studies using independent evaluators to assess the dependent variable, these values indicate that a lot of variance is left unexplained. Commercial performance is a distant outcome measure, which may explain why the R2 is rather low. Less distant, proxy outcomes such as project performance could reduce the level of unexplained variance, however, project performance is not a good indicator for commercial success of an innovation project [15]. To reduce the unexplained variance, we recommend that future studies use a sample that is less diverse, for instance by focusing on various types of firms within a single industry such as IT. B.Practical Implications Cross-functional knowledge is important for all innovations. However, the effectiveness of cross functional innovation teams, as a mechanism to provide access to such knowledge, turns out to be context specific. The bias in the literature for manufacturing firms [12]which typically have a functional organization and a separate innovation unitmay have led to the impression of cross-functional innovation teams as best practice [80]. Project-based types of organizations that benefit less from cross-functional innovation teams may need to integrate their innovation efforts to make the expert innovation teams effective. Alternatively, organizations which cannot separate their innovation from their other activitiesas is often claimed for service firms [18]may be better off not using cross-functional teams. At the same time, Thomke [81] shows how the Bank of America operates its innovation department as dedicated separate units. Service firms with a more functional organization may be better off having a separate innovation department and using cross-functional teams to cross the boundary between the innovative and operational activities of a firm, than by using a more integrated approach to structure their innovation activities. summerize the article are thoughtful, and analyze the content or question asked make connections to other content and real-life situations extend discussions already taking place, or pose new possibilities or opinions not previously voiced.