The moderating role of power asymmetry on the relationships between alliance and innovative performance in the high-tech industry

Technological Forecasting & Social Change 78 (2011) 1268–1279

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Technological Forecasting & Social Change

The moderating role of power asymmetry on the relationships between alliance and innovative performance in the high-tech industry Chao-Hung Wang ⁎ Department of Marketing and Logistics Management, Ling Tung University, 1 Ling Tung Road, Nantun, Taichung, 40852, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 28 October 2010 Received in revised form 7 March 2011 Accepted 16 March 2011 Available online 8 April 2011

Keywords: Innovative performance Power asymmetry Alliance Learning Experience

a b s t r a c t This study outlines a model of why and how the respective influence of alliance learning and alliance experience on innovative performance is likely to be moderated by the level of power asymmetry. The results are based on a sample of 316 high technology firms' alliances. By testing our model using hierarchical regression, the results generally support the proposed hypotheses, in that power asymmetry has a significant negative moderating effect on the impact of alliance learning on innovative performance. In contrast, we find that power asymmetry has a significant positive moderating effect on the impact of alliance experience on innovative performance. Implications for management theory and practice are discussed. © 2011 Elsevier Inc. All rights reserved.

1. Introduction In the focus of recent literature on performance research, an attempt to explain performance differences between firms has shifted from firms' internal elements to industry-level external relationships [1]. Alliances with business partners can be necessary in a fiercely competitive environment. The topic of alliance performance and its determent has been dealt with extensively in recent years [2]. When focusing on innovative performance rather than alliance performance, there is a great deal of room for further investigating how an alliance impacts a firm's innovative performance. Innovative performance may be difficultly developed by a high-tech firm without some relationships with alliance partners [3]. In this study, innovative performance is referred as bringing to market goods and services that are new or substantially improved against main rivals [4,5]. Triggered by dissatisfaction with the unpredictable innovative performance, this study examines the innovative performance in alliance firms in order to better understand relationships between alliance and innovative performance. There is growing research examining strategic alliances as pivotal strategic tools to differentiate firm performance [6]. The term strategic alliance refers to a voluntary agreement between independent firms to develop and commercialize new products, technologies or services [7]. Alliances have become an extraordinarily popular way for firms to extend their organizational resources by combining them with those of other firms. Recent studies in the alliance literature have found that alliances can be a source of learning for firms since they can develop capabilities to help them internalize resources [8]. Studies have noted that alliance learning is a preferable means to effective organizational capability development. Alliance learning is becoming an essential feature for sustaining advantage in today's intensely competitive marketplace. However, some researchers have moved beyond alliance learning and shown that alliance experience improve the performance between these

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two partners [9]. Thus, except alliance learning, understanding how alliance experience affects firm performance is another important issue. Power asymmetry has become increasingly accepted in the strategic alliance literature as a pivotal construct that affects the effect of performance [10]. Power asymmetry refers to the degree to which one firm holds substantially more or substantially less power than another in an alliance relationship [10,11]. Strategic management scholars have dedicated considerable effect to understand the power asymmetry effect [12]. Although a few pioneering studies have examined how power asymmetry may affect the relationship between alliance and firm-level performance, the existing literature offers conflicting empirical findings about the power asymmetry of exchange relationships. These results rang from positive (e.g., [13,14]) to negative (e.g., [15,16]). As a result, little is known about how power asymmetry influences the learning and experience of alliance and thereby differentiates high-tech firm's innovative performance. Because of continuing competition between alliances, an investigation of the role of power asymmetry offers substantial value and importance to practitioners. Moreover, an investigation of the role of power asymmetry may refine our conceptual understanding of the alliance–innovative performance link. Powell, Kogut, and Smith-Doerr [17] suggested that a diversity of alliance experience should enhance firm alliance learning. Hoang and Rothaermel [18] also proposed that alliance experience facilitates the way firms learn how to manage alliance. Conversely, Kale, Dyer, and Singh [19] emphasized that, “the implicit assumption behind the relationship between alliance experience and success is that there are learning effects that enable firms to develop a ‘relational capability’”. In another word, alliance learning efforts explain how previous alliance experience facilitates alliance success. The initial inclusive results in strategic alliance research led to a fragmentation of research programs, split between those studying the effect on alliance learning (e.g., [20]) and those examining the impact of alliance experience (e.g., [9,21]). Thus, the purpose of this research is to bridge the gaps. Two calls to deliberate the mixed findings have attracted serious attention in the past decade. First, the simultaneous consideration of alliance learning and alliance experience to analyze the potential effects of alliances on innovative performance. The roots of this model can be traced to work of Emden et al. [22]. Second, even though the relationship of alliance–innovative performance theoretically recognizes the potential moderating influence of the situation on the effectiveness of alliances, few studies have done empirical testing for such moderating effects. We argue that the existing research examining the alliance– innovative performance link has overlooked potential moderating variables. We seek to fill in this gap by examining the effect of a high-tech firm's alliance activities on innovative performance and subsequently by assessing the moderating influence of power asymmetry on the relationship between alliance and innovative performance. Drawing from arguments of alliance, power asymmetry, and innovative performance, through this richer explanation and empirical assessment, we contribute to greater clarity of how alliance may contribute to successfully developing innovative performance. In the next section, we develop the theoretical model and hypotheses, drawing on prior literature from several theoretical disciplines that have studied strategic alliances. The details of data collection and analysis are then presented, and the results are discussed. The paper concludes with implications and suggestions for further research. The moderating effect of power asymmetry on the relationship between alliance and innovative performance is depicted in Fig. 1, and represents the model that is tested in the present study. 2. Literature and hypotheses To pursue innovation diversity should be an important goal for firms' alliance learning. Alliance learning is defined as a means to absorb critical skills, information, know-how, or capabilities from alliance partners [23,24]. The knowledge accumulation enables its differentiation of a high-tech firm's innovative performance through the learning it achieves by its interactions with partner knowledge bases. Interaction between the alliance partners facilitates innovation capabilities [25,26]. For example, early in an alliance startup, alliances can aid innovation expectations by coordinating knowledge sharing and learning. Over time, alliance learning can be used to leverage innovative performance generation and send signals that enable continuous coordination to achieve objectives [27]. Recent studies also emphasize the vital role of external knowledge sources in innovation activities [28]. As modern literature on innovation emphasizes the importance of utilizing external knowledge sources from alliance partners [29,30], the innovation process has been described as a sequence of activities by which an idea is transferred into a commercial product, and it lies at the heart of firm strategy [31]. Huber [32] suggests that innovative knowledge acquisition may be carried out through several processes, learning by interacting with other organizations. Thus, learning in alliances has been examined by many researchers [20,33], and it can positively enhance a high-tech firm's innovation because learning occurs through the sharing of knowledge

Alliance learning

H1

Innovative performance Alliance experience

H2

H3

H4

Power asymmetry Fig. 1. Hypothesized model.

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differences between two or more organizations [34,35]. The suggested relationship between the firm's alliance learning and its innovative performance is thus clear and we posit the following: H1. Alliance learning will be positively related to innovative performance. Alliance experience is a result of the individual alliance partners' relationships through a firm's former alliances [36]. Alliance experience is defined as an accumulation of resources and the knowledge of how to exchange them that a firm has obtained from its alliance relationships over time [37]. Previous research suggests that the alliance experience enables firms to better understand the critical processes and issues in alliance management [21,38]. The alliance experience may also affect organizational routines because a high-tech firm developing an important new product with its partners will need to change the way they develop the product if the alliance partner develops a similar product. Shared experience engenders the development of common perspectives, enabling a firm to absorb new knowledge more effectively [39]. Stressing the need to thoroughly experience an interorganization's routines and practices in order to be optimally leveraged, various scholars have also suggested that prior experience shape future improvements in performance [40,41]. Since alliance experience is an important source of innovative idea generation, experience can be seen as a key concept in innovative capability development. Alliance experience can make innovation more valuable because they give firms access to a partner's core competences and resource-exchange experience [33,42]. Effective alliance experience is cumulative in nature because it can accumulate the partner's know-how, and is a unique driver of innovation [43]. Firms with alliance partners have experienced the benefits of alliance relationships and may be less likely to feel threatened by accumulating information with partners over time [44]. We argue that, if an alliance experience indeed exists, it must have tangible benefits to the basis for a firm-level innovative performance. On such tangible benefit of a firm's alliance experience is the firm's accumulated knowledge to productively manage its alliances, which in turn should impact its innovative performance [45]. Therefore, alliance experience may also be a valuable resource of innovative performance, and so we posit the following: H2. Prior alliance experience will be positively related to innovative performance. Gulati, Nohria, and Zaheer [46] further delineate the rationale of alliance learning and suggest that performance is created by enhancing partner learning. In addition, the organizational learning perspective attempts to understand how the partners contribute to performance creation through learning processes [47]. However, most studies of strategic alliances have failed to consider the potential moderating role of this relationship. Without consideration of its moderating effects, it is not surprising that shortcomings in many results of empirical studies might be partially due to neglect of asymmetrical relationships [48]. Thus, we test the moderating role of power asymmetry on the relationship between alliance learning and innovative performance. Firms always depend, to varying extents, on their exchange partners. Dependence and power are closely related reciprocal concepts. The possession and control of critical assets generate power [49]. The sign of the dependence between the two parties indicates the relative power of one organization over the other. Most plausibly, power asymmetry arises because, by engaging in alliance activities, differences in resource dependence, competencies, financial strength between partners [50]. Power asymmetry can have an important effect on the relationship between alliance learning and innovative performance. One of the parties dominates the other and forces its views onto the other alliance partner; innovative performance could be negatively affected. In such situations the sharing of data and information which is critical to alliance learning will be difficult or even impossible. Furthermore, Ford and Thomas [51] showed that in asymmetric relationships communication will predominantly go from the dominating party to the dependent party. The lack of balance in power in turn hampers the dependent party's responses to the dominant party's initiatives. According to Rota, Thierry, and Bel [52] and Senge [53] there can hardly be alliance learning without the sharing of information on an equal basis. Hence, symmetry in the power situation of two alliance partners is expected to facilitate mutual learning and equivalently, power asymmetry will lead to less alliance learning. From the relationship marketing perspective, business relationships affect a firm's innovativeness and competence and even its performance potential. The learning capabilities of an alliance partner reflect how successful it has been in combining relationships and its own features. Power symmetry therefore indicates to other firms that a company has the potential to be a strong contributor to alliance learning within the relationships [54]. Without the types of symmetry relationships that are considered to make important contributions in a strategic alliance and are seen as valuable and distinctive by the other party, learning by the alliance partners may be hollow. Interaction with another party in a symmetrical relationship will determine the usefulness of alliance learning and will enhance the performance in which these symmetrical relationships are developed. In so doing, we expect to find innovative performance to be most effective when dyadic power relationship is symmetry. Thus, the third hypothesis states: H3. Power asymmetry will negatively moderate the relationship between alliance learning and innovative performance. Although the alliance process can be costly, time-consuming, and dilute a high-tech firm's resources, alliances provide experience with exchange relationships and they can reduce the time for the alliance partners to mutually benefit from combined resources. This proposition is congruent with Arino and de la Torre's [55] view that alliance partners with experience may have more efficient and effective integration processes when involved in an alliance, and this can further benefit innovative performance. Hitt, Hoskisson, Johnson, and Moesel [56] shed light on the potential importance of alliance experience by providing distinct advantages for partners. Through their alliance experiences, partners can access information and experience with resource

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exchanges that could otherwise be unavailable in strategic factor markets [57]. In this regard, experience with alliance partners can benefit the accumulating partner's new knowledge, and possibly even the development of innovative knowledge. Despite the abundance of findings in regard to the effectiveness of alliance experience (e.g., [18,58]), there are numerous reasons to question whether alliance experience is positively related to innovative performance in all situations. Das and Teng [59] argue that from social exchange theory, alliances can be viewed as interorganizational arrangements that closely integrate partners through resource exchanges. In the same vein, Anand and Khanna [8] argue that the relational capital accumulated through continuing experiences may enhance cooperative behavior, thus resulting in greater competence in managing future alliances. However, given the shift in treatment of alliance relationships, perhaps in the enthusiasm for what should make for ideal business exchange conditions, the role of power asymmetry in alliance relationships has been either overlooked or dealt with as a side issue. Lenox and King [60] propose that merely referring to experience as the explanatory variable in a firm's performance seems to be an overly simplistic representation of reality. As such, we seek to add the power perspective to the experience– innovative performance link by testing the effect of power symmetry/asymmetry on this relationship. Accordingly, we anticipate that alliance experience will be most effective when an alliance partner's power is symmetrical and least effective when power is asymmetrical. H4. Power asymmetry will negatively moderate the relationship between alliance experience and innovative performance. In summary, although we expect that the main effects of alliance learning and experience are positive, we anticipate that alliance learning and experience will be less effective in a situation of power asymmetry than power symmetry. 3. Method 3.1. Pretest, sample, data collection The high-tech industry is particularly suitable to test the notions of alliance and innovation since it has the greatest number of alliances of any industry. Moreover, high-tech firms frequently collaborate with R&D, production, and supply activities due to rapidly changing technology and increased development cost [61]. We chose 800 Taiwanese high-tech firms as the research setting, selecting firms that had engaged in alliances and that operated in industries where alliances are a critical means of competing. Table 1 summarizes the classified results of alliance characteristics. With respect to length of time of alliance, the alliance sample consists of all alliance activities for high-tech firms commencing in the years 2002–2009 inclusive. This time period provides more comprehensive alliance samples than earlier time periods but still allows sufficient time to track the effect of learning and experience. The vast majority of firms were based in Taiwan, with only 16.3% high-tech firms having their headquarters in other areas. For pretesting, a random selection of 30 CEOs or senior executives was contacted to increase content validity. After obtaining their consent to participate, the 30 CEOs or senior executives were asked to evaluate the draft questionnaire. Based on their feedback, we modified some of the survey questions accordingly. Cover letters sent with the revised questionnaires confirmed the respondents' involvement with the alliance in question, stressed the importance of this research, and offered an incentive (a copy of the finished report summarizing our research findings). To maximize the response rate, we used the mail survey methods suggested by Dillman [62], including a follow-up letter with an additional copy of the questionnaire sent to non-respondents 1 month after our initial mailings. We also identified appropriate respondents in each of these firms. Although most survey-based studies on alliances have relied on sending surveys to the CEOs, there may be other appropriate people in companies who could respond to the questionnaire.

Table 1 Alliance characteristics of sample. Alliance characteristics Alliance functional scope Manufacturing/marketing Joint marketing Strategic purchasing Joint product development Joint R&D Exchange of know-how/technology Others Length of time of alliance 1–3 years 4–6 years 7–9 years 10–12 years N12 years Alliance border Taiwan domestic Cross-border

Percentage 18.3% 14.8% 23.8% 19.7% 4.1% 10.3% 9% 21.4.% 37.8% 15.9% 14.3% 10.6% 83.7% 16.3%

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Table 2 Industry-sector distribution of the sample. Industry-sector

SIC code

Number of firms involved in this study

Number of response

Machinery equipment Electronic equipment Communication equipment Computer industry Semi-conductor and related Device industry Total Response rate

3541 3641 3663 3571 3674

160 168 88 160 72 800

68 88 36 86 38 316 39.5%

For example, companies often have executives responsible for strategic alliances, who would be more knowledgeable. We also assessed the key informant quality in our survey to make sure that respondents were knowledgeable about the alliance on which they reported [63], and then eliminating respondents who were not knowledgeable about the alliance on which they were reporting. A total of 800 questionnaires were distributed and 316 responses were received, for a response rate of 39.5%. The sample used in this study includes 800 Taiwanese high-tech firms in five high-tech industry sectors, as described in Table 2. Table 3 provides an overview of the relative distribution of the respondents in terms of the following three relevant variables: number of employees, respondents' professional title, and company age. The largest group of respondents work for a parent firm having between 1 and 100 employees (46.1%), and 19.6% of the respondents worked in a firm that employs over 1000 employees. The highest position of respondents is the category of CEO, with 21%, and 136 respondents (43%) were alliance managers. Of 114 business development staff, 92 of the respondents considered themselves to be the person responsible for the alliance, and the remaining 22 were part of the alliance team. The largest group of respondents work in firms established less than 15 years (33.5%), and 28.5% for companies of over 32 years, and 23.4% for companies between 16 and 23 years of age. 3.2. Measurement and validity 3.2.1. Unit of analysis An obvious obstacle to using the alliance as level of analysis is that each alliance is treated as a single and independent transaction [64]. For example, Olk [65] measures the alliance performance using the individual alliance. Recently, researchers have begun to analyze knowledge transfer within firms, and doubts have arisen whether an alliance level or partner level of analysis is appropriate [66]. How can the measure be designed to examine perception of two-firm alliances? Researchers widely recognize the value of gathering data from both sides of the firm because the confirmation of perceptions and the validity testing of such data is required, although the difficulties associated with gathering and using such data are great. Indeed, the questionnaire cover letter made it clear that the alliances of concern in this study use the innovative performance of a firm's average innovative performance as a level of analysis that is more likely to be a reliable representation of a firm's alliance portfolio. 3.2.2. Explanatory variables We used Churchill's [67] approach to questionnaire development, combining scales from several previous relevant empirical studies with new items to develop an initial list of questions. We eliminated several redundant items through face-to-face interviews with academic and practitioners, and we tested a first draft of the questionnaire across 20 dyadic alliances. All items were recorded on a seven-point Likert scale (ranged from 1 = strongly disagree to 7 = strongly agree).

Table 3 Distribution of respondents. Variables Number of employees 1–100 101–300 301–500 501–1000 N1000 Respondent title CEO Alliance manager Business development staff Company age 1–7 years 8–15 years 16–23 years 24–31 years N31 years

Number of respondents

Percentage

146 34 28 46 62

46.1% 10.8% 8.9% 14.6% 19.6%

66 136 114

21% 43% 36%

20 86 74 46 90

6.3% 27.2% 23.4% 14.6% 28.5%

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3.2.3. Alliance learning In spite of the difficulty of measuring alliance learning, alliance learning is realized and potentially converted into innovative performance, often being indirectly inferred rather than directly observed. This study analyzes a set of alliance learning data critical to a firm's innovative performance. Moreover, the results of the expert interviews clearly underlined the fact that alliance learning is an important determinant of innovative performance. In this study we verified the face validity of the operationalization chosen. Hence, we use eight items that refer to the extent to which the firm, in the course of its alliance operations, gained new knowledge or new skills in developing new product designs, improving product development, improving manufacturing processes, and identifying emerging technologies from its alliance partners. The eight items (see Appendix) used to measure alliance learning were adapted from Kale et al. [33], Ramus and Steger [68], and Hsu and Pereira [47]. 3.2.4. Alliance experience The firm's alliance experience was measured using two indicators: (1) alliance years, as in Kale and Singh [69] and Lee and Hong [70]; and (2) alliance count, as in Heimeriks and Duysters [71] and Rothaermel and Deeds [21]. Alliance years are the cumulative sum of the alliance duration for each of the firm's alliances. In the literature, there is growing consensus that 5 years is the correct period to examine [19,72,73]. It is considered to be the average period during which an alliance can still contribute to the experience level of companies. A seven-point scale defined different categories representing a firm's count of alliance as follows: (1) under 5 alliances; (2) 6–10 alliances; (3) 11–15 alliances; (4) 16–20 alliances; (5) 21–25 alliances; (6) 26–30 alliances and (7) over 31 alliances. As the average alliance portfolio of firms in our dataset consisted of over 11 alliances, the total dataset refers to approximately 3476 alliances. We arrived at a total of 3496 alliances by multiplying the number of respondents within each category by the average of each category. Overall, the average alliance portfolio of our respondents consisted of 11.06 alliances. This alliance experience measure corresponds to the experience construct underlying the experience curve effect since a firm-level alliance experience is accumulated through learning-by-doing over time. These two indicators were standardized and summed to construct a global measure of alliance experience. 3.2.5. Power asymmetry The idea of power asymmetry was to see if dependence levels differ in asymmetric relationships when one alliance party is less/ more dependent than the other alliance party. Therefore, operationally, there is difference in the dependence levels. Assuming dependence to be the obverse of power, a positive value indicates a power advantage, and a negative value a disadvantage. The four items (see Appendix) to measure power asymmetry were drawn from Lusch and Brown [74] and Gelderman [75]. Although the measures used were designed to examine perceptions of the dyad, the data were collected from only one partner's viewpoint. This technique is referred to as proxy reports [76]. Empirical and theoretical support exists for the use of proxy reports when there is joint participation in an event [77]. The reliability of each unidimensional scale was examined by computing the reliability coefficient. In cases where the other coefficient alpha was smaller than 0.7, the item with the lowest corrected item-to-total ratio was removed. Convergent validity was investigated by performing a series of confirmatory factor analyses (CFA) at the first-order level. The criterion of all factor loadings being significant at the 0.05 level was used as an indicator of convergent validity. Discriminant validity was assessed by estimating a two-factor first-order model for each possible pair of scales. Thus, alliance and power asymmetry included in the study, and their unidimensionality was asserted using CFA. The CFA fit statistics (χ2 = 334.487, df = 109, goodness of fit indices (GFI = 0.901; AGFI = 0.912; NFI = 0.962; CFI = 0.931; and RMSEA = 0.031) indicate an acceptable level convergent and discriminant validity [78]. After purification, the scales indicated a sufficient degree of unidimensionality, reliability, and validity. 3.2.6. Dependent variable This study integrated and modified Tsai [79] and Tuominen and Hyvönen's [80] conceptualization to develop a performance conceptualization of innovation. Innovation performance was measured as the natural logarithm of percentage of sales from products which have been launched or substantially improved within 3 years. This variable was used earlier by Caloghirou et al. [4] and Jantunen [81], for example, and it follows the Oslo Manual (OECD, 1997) guidelines that have been adopted in European Union Community Innovation Surveys (CIS). 3.2.7. Control variables There are some variables not considered in the hypotheses that may still influence innovative performance. We thus control for some variables that are likely to affect performance, including firm size, firm age, and industry diversity. Firm size, a commonly used control variable often related to diversity levels, is measured by the logarithmic function of number of total employees [82]. This is significant because smaller firms may contribute a disproportionate share of major innovation. Although large companies have more resources invested in R&D, marketing campaigns, and production equipment than smaller firm, they often choose safer projects that generate fewer radical innovations [83]. Firm age, an important control variable, is measured by the natural logarithm of the number of years a firm has been in existence since younger firms often pursue more radical innovations than older companies [84,85]. Industries differ widely in the degrees to which they engage in alliance activity. These differences can be explained by technological opportunity, meaning the ability to capture the results of technical innovations. Industry diversity is very difficult to operationalize, and even the data necessary for empirical work are often unavailable or unreliable. Hence if it is not possible to include these factors in an empirical study, it is especially important to control for innovative performance differences [86]. The

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industries were divided into 16 different categories representing different industrial classifications. There were categorized as: (1)–(3) are SIC code 3541, (4)–(6) are SIC code 3641, (7)–(9) are SIC code 3663, (10)–(12) are SIC code 3571; (13)–(15) are SIC code 3674, and (16) are others. 4. Results Table 4 provides descriptive statistics and a correlation matrix, while scale reliability estimates are provided along the diagonal within Table 4. The hypotheses were tested by estimating the following equation using ordinary least squares regression: IP = β0 + β1 FS + β2 FA + β3 IC + β4 AL + β5 AE + β6 PA + β7 AL × PA + β8 AE × PA + ε In order to test this study's hypotheses, we analyzed different models (see Table 5). Using the three models shown in Table 5 helps us to examine the increases the variance explained, which is reflected by an increase in the adjusted-R squared. Moreover, following these steps, we can test for a moderator [87]. First, the control variables were regressed in the dependent variable. Our findings are listed in Table 5. From the results presented in Model 1, it follows that firm size does not yield any significant result. Therefore, we do not find any support for differences that pertain to firm size. In testing the hypotheses relating to the moderating effects, moderated regression analysis, as recommended by Irwin and McClellan [88], was undertaken hierarchically to test for significant interaction effects over and above the simple effects of independent variables. These tests allow us to expand on both the importance and the significance of the interaction. The resultant models are shown in Table 5. Model 2, containing the simple additive model, show that respective alliance learning (β = 0.398, t = 8.038, p b 0.01) and alliance experience (β = 0.314, t = 6.234, p b 0.01) positively impact innovative performance. Thus, H1 and H2 are supported, respectively. At the next stage the two interactive terms were added to Model 3 resulting in a statistically significant increase in R2. This result reveals that the effects of alliance learning and alliance experience on innovative performance are influenced by the level of power asymmetry. Although the main effect of alliance learning was positive, the interaction of alliance learning and power asymmetry on innovative performance (β = −0.83, t = − 1.671, p b 0.1) was negative and significant. H3 is supported. To better interpret our findings, we conducted a simple slope test, as described by Aiken and West [89]. This revealed that, for the slope significance test at low level (t = 1.926, p = 0.055) of power asymmetry, the relationship between alliance learning and innovative performance is statistically significant. We used the ModGraph software [90] and graphed the interaction effects following procedures set forth by Cohen and Cohen [91], as shown in Fig. 2. Finally, in contrast to the hypothesized relationship, the interaction between alliance experience and power asymmetry is significant but positive for innovative performance (β = 0.126, t = 2.517, p b 0.05). H4 is not supported. A simple slope analysis showed that at both high (t = 8.4331, p = 0.000) and low (t = 3.582, p = 0.000) levels of power asymmetry the relationship between alliance experience and innovative performance is positive and statistically significant. Fig. 3 shows the moderating role of power asymmetry (high and low levels) on the alliance experience–innovative performance relationships. 5. Discussion This research examined the effects of alliances on innovative performance. We have further theorized that power asymmetry may moderate the individual effect of alliance learning and alliance experience on innovative performance. Our results demonstrate the need to make a distinction between the two moderating effects. Specifically, we found that the main effects for alliance learning and alliance experience were positive, which largely extends previous research [92]. We argue that, in order to sustain innovativeness, firms must learn partner's knowledge and accumulate experience from their alliance partners. This finding also holds interesting managerial implications. First, when a high-tech firm learns from its alliance partner, the innovative knowledge generated can be used to enhance strategy and operations in areas related to the alliance activities [93]. This learning effect can constitute the benefits that a high-tech firm can earn unilaterally by acquiring innovative knowledge from its alliance partners [94]. The knowledge can be internalized by the firm and applied to new product development and innovative activities [38]. Transaction cost theory (TCT) supports the rationale of this finding [95]. The basic premise of TCT is that if in-house organization of innovative cost and sunk cost can be reduced through an alliance partners' knowledge, the firm will favor the alliance strategy. Conversely, if these costs exceed the benefit of the external market Table 4 Descriptive statistics and correlations.

1. 2. 3. 4. 5. 6. 7.

Innovative performance Alliance learning Alliance experience Power asymmetry Firm age Firm size Industry classification

Mean

S.D.

1

2

3

2.495 4.892 4.084 3.520 2.820 3.430 8.650

0.8770 0.9719 1.2034 1.4760 1.4020 2.2900 5.5860

0.87 0.518⁎⁎ 0.463⁎⁎ 0.078 0.244⁎⁎ 0.171⁎⁎ 0.151⁎⁎

0.81 0.366⁎⁎ 0.166⁎⁎ 0.259⁎⁎ 0.111⁎

0.94 0.230⁎⁎ 0.330⁎⁎ 0.363⁎⁎

0.001

0.083

The alpha reliabilities are on the diagonal. ⁎p b .05; ⁎⁎p b .01.

4

0.91 0.259⁎⁎ 0.284⁎⁎ −0.011

5

6

7

0.79 0.643⁎⁎ 0.181⁎⁎

0.83 0.092

0.75

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Table 5 Results of hierarchical regression. Model 1

Control variable Firm age (FA) Firm size (FS) Industry classification (IC) Direct effect Alliance learning (AL) Alliance experience (AE) Power asymmetry (PA) Moderating effect Alliance learning × power asymmetry Alliance experience × power asymmetry Adjusted R-square R-square change F

Model 2

β

t-value

VIF

0.206 ⁎⁎ 0.029 0.111 ⁎

2.850 0.406 2.007

1.748 1.705 1.035

0.063 0.072 8.068

β

Model 3 t-value

VIF

β

t-value

VIF

0.033 0.015 0.149 ⁎⁎⁎

0.535 0.244 3.262

1.873 1.878 1.040

0.023 0.007 0.153 ⁎⁎⁎

0.376 0.115 3.378

1.894 1.884 1.042

0.398 ⁎⁎⁎ 0.314 ⁎⁎⁎ − 0.33

8.038 6.234 − 0.653

1.222 1.265 1.208

0.383 ⁎⁎⁎ 0.344 ⁎⁎⁎ −0.37

7.735 6.701 − 0.747

1.243 1.332 1.231

−0.83 + 0.126 ⁎ 0.378 0.394 24.922

− 1.671 2.517

1.241 1.259

0.368 0.38 31.595

+

p b .10. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .1001.

mechanism, the firm will favor keeping activities in-house [96]. In other words, TCT explains how alliance partners use the learning mechanism as a means to safeguard their specific investments by adapting to the uncertainty of relationships. Second, the richer the prior experience of the high-tech firm, the greater its exposure to various possible integrations with different partners. Adaption should follow, as firms reduce inappropriate alliance partners and find good ones. Thus, more extensive alliance experience allows firms to identify effective processes for exchanging information and technology with their partners and for managing complex innovative activities with uncertain outcomes. More specifically, as firms accumulate alliance experience, they are better able to make adaptations in alliance processes to attribute innovative performance [8]. With this alliance experience, high-tech firms can adopt better innovative processes to reduce the complicated time-consuming processes. This finding is in agreement with previous studies [97]. Most articulations of alliance theory implicitly suggest that alliance learning is differentially effective, depending on whether the power situation is symmetrical or asymmetrical [98]. Our results certainly do not detract from this view. We expand upon this notion that alliance learning is likely to have a negative effect on the innovative performance under lower power asymmetry relationships. The potential explanation for this is that learning may have a negative effect when alliance partners have imbalanced resources and capabilities in lower power asymmetrical relationships. This is typified in alliances where one or more partners have more power or resources than the others. Since all parties in alliance relationships may face varying degrees of conflict in their interaction with other parties, conflicts may arise from partners' differing expectations in asymmetrical relationships so that goals and cultural norms of one party can eventually clash with those of another [99]. For example, there are instances where a weaker alliance partner requires a particular kind of product as an important component in the production of their final products. These weaker alliance partners may be willing to enter into alliances with other stronger alliance partners who are the dominant suppliers of that product simply because of necessity. In this kind of asymmetrical relationship, even though both alliance partners maintain the alliance relationship, both parties may not create a win–win situation, especially since weaker partners are unlikely to develop new products because the stronger partners are unwilling to provide new knowledge to weaker partners [58].

Innovative performance

3.5 3 2.5 2 1.5 1 Low power asymmetry

0.5

High power asymmetry

0 Low alliance learning

High alliance learning

Fig. 2. Moderating role of power asymmetry on the alliance learning–innovative performance link.

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Innovative performance

5.5 5 4.5 4 Low power asymmetry

3.5

High power asymmetry

3 Low alliance experience

High alliance experience

Fig. 3. Moderating role of power asymmetry on the alliance experience–innovative performance link.

The implications are straightforward. First, new product technology, R&D processes, and new market developments may have been underdeveloped in asymmetrical power relationships, or even have lain dormant over a period of time since the focus of innovation development has been for the stronger partners' benefit. Conversely, weaker parties may lack learning capabilities in resource deployment and innovative development to assist the creation of new products. Therefore, it may be necessary for the stronger party to provide to weaker parties more new knowledge, specialized skills, resources, and activities [100]. The second implication is the development of new symmetrical relationships by alliance learning to improve innovative performance. Both parties need to redress the balance of their asymmetry relationship characteristics. Changing the nature of an asymmetrical relationship is not something that partners can do alone. Rather they must learn how to work in collaboration with their partners, as both parties have influence over the direction of the relationship. Having learned to live with relationships where no mechanism for collaboration has existed, weaker parties who are more used to coping with asymmetrical relationships with stronger parties may have gained little learning experience in collaborative ways of working. Therefore, the ability of learning to change from asymmetrical to symmetrical relationships between alliance parties will be critical for creating better performance. The wave of alliance experience has been associated with an increased interest in the rationale of performance. In the literature, the relationship between alliance experience and performance is mostly positive [8]. However, Baum and Ingram [101] posited that the value of experience decays over time, suggesting that the benefits from such experience are likely not cumulative over time. Little empirical research has examined what moderators cause this contradictory finding. As such, perhaps the most important contribution of this research is the results of our examination of power asymmetry as a moderator of the link between alliance experience and innovative performance. Unlike the results for alliance learning, a different picture emerged for alliance experience. For alliance experience, one of the main effects was positive on innovative performance. Further, as the level of power asymmetry increases, the effect of alliance experience on innovative performance was positive and significant. This suggests that the alliance experience led to innovative performance under the level of high and low power asymmetry situations, respectively. A possible explanation for this unexpected finding is that power asymmetry could not be a polar opposite of cooperation [102]. Power can be seen as a mechanism for achieving coordination among alliance partners [103]. Despite some critics' view of power as the antithesis of trust, Kumar [104] contends that trusting partnerships can be built between unequal, but only that the onus is on the powerful party to treat the weaker, vulnerable party fairly. Relationships are seldom fair in power, nor are all parties equally active in commitment to a relationship. A general view is that such partnership arrangements tend to offer the most to the more powerful business partner [105]. Therefore, this does not mean that such power asymmetry relationships are not workable or enduring. Moreover, power asymmetry may also be viewed as positive effect, which brings together different alliance partners and staff within them with varied views, cultures, strategies and competitiveness.

6. Limitations and future research In spite of these important contributions, several research limitations should be recognized to provide a balanced discussion of our findings. First, innovativeness, our dependent variable, might also be considered a type of strategic orientation (orientation toward new product innovation), and therefore, the use of the new product development as a proxy of for innovative performance might provide a limitation to this study. Future researches should examine other types of strategic orientations because innovation is not only dependent on the new product, but might also be dependent on other issues such as managerial innovativeness [106], technology innovation [107,108], and manufacturing process innovation [109], etc. Second, due to the dynamic nature of the variables in study, learning, experience, and power are seldom static and likely to change over time. This is because alliance partners during the interactions may react differently depending on the different phases in the alliance relationships. Furthermore, another recent research has called for future studies' need to empirically assess the learning dynamics in strategic alliance [20]. In similar vein, this study employs of a cross-sectional design. In any model in which causality is suggested, longitudinal studies will provide for stronger inferences. Therefore, the proposed model developed in this study could benefit from being tested in a longitudinal design, so that actual situation of variables can be taken into account. Future research should also consider the need for longitudinal research, as longitudinal research designs may be needed to explore how comparison standards change over time,

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as learning and experience along various phases of alliance developments. Though there is difficulty in this line of research, but this appears to be a critical area for future research. Third, although the heterogeneous nature of our sample lends support to the generalizability of our results, there may be significant differences that might have attenuated our results. Had we used a sample that was more homogeneous, perhaps we would have reduced the variance noise, which might have resulted in more explained variance. One significant potential cause of attenuation involves the variables in a study: alliances may have different measures of success. Thus, some of the alliances in this sample may have had objectives in addition to innovation. Future research should take into account various measures of alliance performance. The findings of this study support this theoretical view. To sustain innovativeness and alliance success, firms need assets, processes and structures that enable strategic flexibility and support entrepreneurial opportunity for sensing and allying. Company structures, processes, strategic orientations and their interaction are potential relevant subjects for future research. To conclude, this article has conceptualized and tested a model to explain how alliance learning and alliance experience influence innovative performance. The study of power asymmetry is still relatively new to alliance research. We hope that this article has contributed to both researchers' and managers' understanding of these complex phenomena. Ackowledgements I would like to thank the anonymous referees for their constructive suggestions and comments. I also wish to acknowledge the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract NSC 99-2410-H275-007. Appendix A A.1. Alliance learning [33,68] 1. 2. 3. 4. 5. 6. 7. 8.

Your company learnt or acquired some new or important information from the partner. Your company learnt or acquired some critical capability or skill from the partner. This alliance has helped your company to enhance its existing capabilities/skills. Thanks to your partners you are able to improve your manufacturing processes. Thanks to your partners you are able to shorten the timeline of our product introduction. You feel that your partners are a valuable source of information and new ideas. Due to the help of your partners, you are able to get a sustainable competitive advantage. Due to the help of your partners, you are able to identify emerging technologies.

Power asymmetry [74,75] 1. 2. 3. 4.

This partner is important for us in terms of volume of trade. We need the technological expertise of this partner. We will experience high switching costs if another partner will replace the current partner. We depend on this partner.

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