Knowledge Workers' Interpersonal Skills and Innovation Performance: An Empirical Study of Taiwanese High-Tech Industrial Workers
Tsai, Ming-Tien, Chen, Cheng-Chung, Chin, Chao-Wei, Social Behavior and Personality: an international journal
While interpersonal skills have always been considered to be critical to the performances of managers and supervisors, recent trends in the workplace have extended the importance of these skills to virtually everyone in an organization. Flattening organizational structures and the movement toward working in terms and self-directed work groups mean that every worker needs to improve his/ her skills for influencing others. People now tend to fill broader roles in their organizations, rather than following specific, narrow job functions. These new roles require them to perform a wider variety of tasks and interact with more people in diverse contexts. This need for people to communicate with each other in the workplace, especially cross-culturally, is magnified by the increasing globalization of business interests and by the growing ethnic, cultural, and gender diversity represented in today's workforce.
Interpersonal skills are especially important in technical environments where employers want more than just technically qualified employees, and increasingly, soft skills, or the ability to deal with people, are required. For example, to meet the demands of increased competition, managers now expect their highly paid, highly educated technical employees to interact with each other, with members of the marketing department, and with customers.
Moreover, both for-profit and not-for-profit businesses in today's interconnected world need to look outside the organization and find partners who can help achieve greater results and build the communities required to meet the growing complexity of the challenges they face (Hesselbein & Whitehead, 2000). Most international, interorganizational, or interpersonal relationship collaborations articulate the collaborative effort as the primary method for achieving ideal goals that would not otherwise be attainable by entities working on their own.
While there has been a great deal of research into how firms seek to improve performance via collaborations, little empirical work has been carried out to investigate the influences of the collaborators' interpersonal skills during relationships. Moreover, although there are many human resources (HR) studies on interpersonal skill issues, most of them have been focused on organizations operating in a Western context, while relatively few have been focused on the Chinese community. To examine these issues in an Asia-Pacific context, the researchers consider the lessons learned from knowledge-intensive industries in Taiwan. Therefore, the researchers focused on whether or not knowledge workers in high-tech industries need more interpersonal skills to achieve tasks that have traditionally been viewed as dependent only on their technical skills. At the same time, the analytical results of this study can provide academics and practitioners with an empirical reference for further study in this field. The structure of this paper is as follows: in the next section the literature review and research hypotheses are presented and then the methodology is developed. Next, the results of the empirical study are presented and discussed, and finally conclusions are drawn in the last section.
Collaboration is an effort by heterogeneous teams to achieve goals that they cannot achieve by working in isolation. Collaboration has been widely discussed in a variety of disciplines, such as transaction cost economics (Williamson, 1975), relationship marketing (Hkansson, 1982; Jap, 2001), interorganizational systems (Alstyne, 1997; Browning, Beyer, & Shetler, 1995; Kuman & van Dissel, 1996), strategic management (Gajda, 2004; Gulati, Nohria, & Zaheer, 2000), supply chain management (Cousins, 2002), sociology (Winer & Ray, 1994), behavioral science (Logsdon, 1991; Sharfman, Gray, & Yan, 1991), negotiation (Eden & Huxham, 2001), and with regard to the factors that contribute to a spirit of collaborative alliance (Heath & Sias, 1999).
The great variety of the works mentioned reflects that the knowledge required for a modern corporation now touches on so many different disciplines that it is almost impossible for a specific R&D unit to keep up to date with the complex information being produced in every relevant field. However, collaboration can be used to obtain knowledge effectively from outside the organization (McAdam, 2000), and thus collaboration and its equivalents, such as networks and long-term relationships, have become central parts of doing business (Harrison & Heumer, 2005). Specifically, more firms now focus on the benefits of collaboration, such as sharing risks, resources, and knowledge, thus enabling them to overcome existing organizational barriers to achieve common goals (Carayannis, Alexander, & Ioannidis, 2000; Hagedoorn, 1993; Parker, 2000; Tidd, Bessant, & Pavitt, 1997). Hence, Rogers (1996) noted that the fifth generation of R&D management applied by leading corporate innovators is collaboration, not competition. Nonaka and Takeuchi (1995) also suggested that an organization should disseminate and embody new knowledge beyond its own boundaries, thus increasing its innovative partnerships and alliances. Such a collaborative system will enable the flow of knowledge across a firm's business network with suppliers, corporate partners, customers, and others. Therefore, modern organizations can effectively extend their potential to create knowledge by focusing on interaction with others (Preez-Bustamante, 1999).
Prior researchers have attempted to categorize and characterize collaboration, but there is still no consensus on the topic. Katz and Martin (1997) suggested that intra- or interorganization categories seem inadequate to classify collaboration. Notably, they observed that when the process involves many internal departments and external partners, it may be considered to be a mixture of inter- and intraorganization collaboration. In addition, Wang and Archer (2007) classified collaboration using horizontal and vertical categories, depending on the buyer/ seller purchasing relationship between members, while Klitgaard and Treverton (2004) created a typology of collaboration based on the degree of partnership between public and private constituents, all of which are confined within certain limits that cannot be clearly classified. Thus, in this study we have avoided using equivocal categories, and instead consider the orientation of various kinds of collaboration. We thus determine that collaboration may include two types of orientation simultaneously or individually, one of which is technology orientated in which the collaboration goal is to achieve forward technology development and the other is customer orientated in which the collaboration goal is to satisfy backward demand. The technology orientated collaborations often have a strong focus on knowledge generation, basic research, and acquiring resources, and are marked by greater levels of egoism and subjectivism (de Solla Price & Beaver, 1966; Katz & Martin, 1997). In contrast, the customer orientated collaborations usually focus on knowledge application, solving of customer problems, and extension of product lines, and are marked by greater levels of altruism and objectivity (Tuominen & Anttila, 2006). However, independent of the kind of orientation the collaboration has, following inferences from prior studies we proposed the following two hypotheses:
H1: Joining in collaboration will increase an engineer's innovation performance.
H1a: Joining in technology orientated collaboration will increase an engineer's innovation performance.
H1b: Joining in customer orientated collaboration will increase an engineer's innovation performance.
While collaboration represents a quick and effective opportunity to enhance firm and employee growth, researchers have found that certain costs can offset the benefits (Dyer, 1997; Hagedoorn & Schakenraad, 1994; Landry & Amara, 1998; Singh, 1997; Singh & Mitchell, 2005; Stuart, 2000). Firstly, in financial terms, for interinstitutional, intersectoral, and international collaborations, travel and subsistence costs are incurred as collaborators, equipment, and material move from one location to another. Secondly, collaboration brings costs in terms of time. Since different parts of the project may be carried out at different locations, time must be spent on coordinating actions. In addition, differences in opinion are almost inevitable, and time is also required to address these, and there is also likely to be a time cost incurred by the necessity of staff working in unfamiliar environments and developing new personal relationships. Thirdly, collaboration brings costs in terms of increased administration, as the more people and institutions involved, the greater the effort required to manage the collaboration. Furthermore, where two or more institutions are collaborating, problems often emerge from different management cultures, financial and reward systems, and possibly even a more general clash of values over what is the most important goal in the relationship. All of these potential differences need to be addressed by more management procedures before they develop into serious problems that can disrupt the collaboration. In short, collaboration may bring significant costs as well as benefits.
When such costs and problems occur, collaborators' interpersonal skills will be a useful immediate buffer to deal with them, smoothing differences among heterogeneous units. Interpersonal skill relates to one's ability to develop one's social network in the form of speaking skills, decoding skills, and role-playing skills. Hence, in this study we focused on the role of engineers' interpersonal skills playing in collaboration, and defined such skills in three ways (Medsker & Fry, 1997; Riggio, 1986; Riggio & Carney, 2003; Sandwith, 1993): (1) Social expressivity: verbal speaking skill and ability to engage others in conversation; (2) Social sensitivity: verbal decoding skill (listening) and knowledge of social rules and conventions; (3) Social control: sophisticated social role-playing skill and what is commonly known as savoir-faire. Such skills will be useful in face-to-face collaboration situations as well at a distance when using a technological interface to communicate. Common situations that necessitate the use of such skills include: negotiating, giving feedback (both positive and negative), conducting an interview, facilitating a meeting, giving a presentation, eliciting knowledge from an expert, giving or receiving work assignments, and soliciting customer requirements. Based on such considerations, the following hypotheses were formulated:
H2: A collaborator's interpersonal skill will strengthen the relationship between the orientations of collaboration he/she is involved in and his/her innovation performance.
H2a: A collaborator's interpersonal skill will strengthen the relationship between the technology orientated collaboration he/she is involved in and his/her innovation performance.
H2b: A collaborator's interpersonal skills will strengthen the relationship between the customer orientated collaboration he/she is involved in and his/her innovation performance.
These hypotheses are summarized in the research concept in Figure 1.
[FIGURE 1 OMITTED]
Sample and Procedure
The empirical survey data used in this study to test the hypotheses were collected in 2008 by means of dyadic questionnaires to avoid common method variance (CMV). All high-tech firms in Taiwan based on the Top 5,000 Business Organizations of 2007, published by the China Credit Information Service Ltd comprised the sample population. A total of 500 firms were selected using random sampling, and the engineers and managers of R&D departments, who play important roles in technology innovation and are likely to be significant players in collaboration, were chosen to be the respondents. After a total of 500 pairs of questionnaires were mailed, follow-up telephone calls were made two weeks later to increase participation. Of the 152 responses received, 13 were from respondents who lacked the relevant experience and 9 were incomplete. The remaining 130 valid and complete pairs of questionnaires were used in the quantitative analysis, representing a response rate of 26%. Of these respondents, 110 (84.62%) were male and 20 (15.38%) were female. To check whether the data collection was subject to the potential for common method bias, we checked the data by following the guidelines of Podsakoff, MacKenzie, Lee, and Podsakoff, (2003). The results indicated that common method bias was not a problem. In addition, non-response bias was assessed on a number of variables by comparing early and late respondents, following the suggestions of Armstrong and Overton (1977) with the results showing that this also was not a problem.
In this empirical study the researchers employed a dyadic questionnaire designed to collect data for testing the validity of the research hypotheses. In the engineer part of the questionnaires, the demographic data, 6 items were taken from Tuominen and Anttila's (2006) study of the orientations of collaboration, and 8 items from Sandwith's (1993) study of interpersonal skills. In the manager part of the questionnaires 10 items from Verona's (1999) study of engineers' innovation performance skills were used. All of the independent and dependent variables, except background information, were measured on a 5-point Likert-type scale (ranging from 1 = strongly disagree to 5 = strongly agree).
Cronbach's alpha analysis was used to test the reliability of scales, with values of 0.75, 0.85, 0.87, and 0.92 for technology orientation of collaboration, customer orientation of collaboration, interpersonal skill, and collaborator's innovation performance, respectively. All of the reliability scores, ranging from 0.75 to 0.92, exceeded the recommended minimum level of 0.70. We also ran an exploratory factor analysis on both antecedent and consequence measures on the basis of a baseline eigenvalue of 1.0, and arrived at factor solutions that supported our hypothesized factor structure. Means, standard deviations, and correlations among the latent constructs are given in Table 1.
Hierarchical regression was chosen as the technique for hypothesis testing due to its explanatory power, and is especially appropriate for this study because it allows for the evaluation of incremental changes in [R.sup.2], as the moderating effects are entered while controlling for the main effects (Hair, Anderson, Tatham, & Black, 1998). The results of the analysis are reported in Table 2.
As outlined by Baron and Kenny (1986), the hierarchical regression analysis entailed three models. First, the control variables were introduced into the model. In total, engineer age ([beta] = -0.380,p < 0.01), work experience ([beta] = 0.255,p < 0.01), and engineer sex ([beta] = 0.026, p = 0.758) explained about 14.3% of the variation in engineers' innovation performances (F = 8.171, p < 0.01). In the second model, the main effects of technology orientation of collaboration, customer orientation of collaboration, and engineers' interpersonal skills were entered, and the model proved to be significant (F = 10.625, p < 0.01) with an adjusted [R.sup.2] of 0.309--a considerable improvement on Model 1 ([DELTA] [R.sup.2] = 0.179, p < 0.01). In the final model, two interaction terms were entered. Once again, the change in [R.sup.2] from Model 2 to Model 3 is significant ([DELTA][R.sup.2] = 0.038, p < 0.05), implying that adding interaction terms significantly improves the predictive ability of the model.
The results of Model 2 provide support for Hypotheses 1a and 1b, which suggest that participating in collaboration can increase an engineer's performance. The positive sign ([beta] = 0.231, p < 0.01) in the relationship between technology orientation of collaboration and engineer's innovation performance offers support for Hypothesis 1a. At the same time, the positive sign ([beta] = 0.180, p < 0.05) of the relationship between customer orientation of collaboration and engineer's innovation performance supports Hypothesis 1b.
Two-way interaction terms are calculated by multiplying the mean-centered variables to avoid collinearity (Aiken & West, 1991; Jaccard, Turrisi, & Choi, 1990). The results of Model 3 provide support for Hypothesis 2b only, which suggests that when the engineers have high levels of interpersonal skills, the relationship between customer orientation of the collaboration they are involved in and their innovation performances is significantly stronger than the other proposed relationships. The interaction term (customer orientation of collaboration x interpersonal skill) is significant in the expected direction (ft = 0.203, p < 0.01), implying that to join in customer orientated collaboration is an important determinant of engineers' innovation performances--especially for those with a high level of interpersonal skills. Hence, Hypothesis 2b is supported. Meanwhile, Hypothesis 2a specifies interpersonal skills as a moderator variable that also influences the relationship between technology-orientation of collaboration engineers joining and their innovation performances. This hypothesis is not supported, as the interaction term (technology orientation of collaboration x interpersonal skill) is not significant in the predicted direction ([beta] = -0.002, p = 0.980), indicating that interpersonal skills have a negligible impact on the relationship between technology orientated collaboration engineers and innovation performance.
This study was aimed at integrating two constructs, collaboration and interpersonal skills, to understand their direct and interaction effects on engineers' innovation performance. The results of hierarchical regression analyses support the existence of certain significant relationships among these research variables. Firstly, these findings reveal why collaborative efforts have become the primary method for corporations to achieve their goals. No matter what kind of orientation the collaboration has, it will significantly contribute to an engineer's innovation performance by looking beyond organizational boundaries and building communities.
Secondly, managers tend to choose knowledge workers with better interpersonal skills (such as social expressivity, social sensitivity, and social control) to be part of a highly customer-orientated collaboration. This is because, while a technical employee possesses good interpersonal skills to interact with other technical employees, members of the marketing department have been found to be more effective for improving the outcomes of the customer orientated collaboration, such as applying knowledge, solving customer problems and extending product lines. Additionally, if managers cannot choose at least one collaborator with good interpersonal skills to participate in customer-orientated collaboration, it is likely that the results will not be optimal.
Thirdly, the moderating effects of interpersonal skills do not appear in the relationship between the technology orientated collaborations engineers participate in and their innovation performances. This indicates that there are certain ideal combinations that can be obtained with regard to the orientation of collaboration and the collaborators' characters. A cooperative team that emphasizes technical skills may have a positive impact on performance in highly technology orientated collaboration, but such an outcome is unlikely to be gained in highly customer-orientated collaboration if the team lacks interpersonal skills.
The importance of interpersonal skills has long been recognized, and it is very likely that successful people in many industries are so effective in part because they have such skills. However, it is also likely that much of the development of interpersonal skills does not take place in the context of HR development programs in workplaces, but rather is obtained in a much broader physical and temporal context. This is not to say that this line of research and practice does not have merit, but rather that more systematic research is required to determine the specific interpersonal skills needed for workplace success, and how to best teach them to knowledge workers. Hence, our study is the first step to developing a more complete understanding of the orientation of collaboration and interpersonal skills that affect a knowledge worker's innovation performance.
While this study provides an empirical basis towards a more complete understanding of the influences of knowledge workers' interpersonal skills during collaboration in the high-tech industry, in order to reduce the risk of producing an idiosyncratic framework, further longitudinal analyses of other industries could be useful.
In addition, future researchers could make use of the contingency theory to examine if certain internal factors fit with homogenous or heterogeneous networks in a way that leads to superior performance in collaborations. For instance, a study of how the combination of personal friendships and attitudes toward knowledge affects the barriers of formality would be valuable, further enhancing our understanding of the complicated nature of the knowledge workers' interaction and behavior in cross-cultural management.
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Ming-Tien Tsai, Cheng-Chung Chen, and Chao-Wei Chin
National Cheng Kung University, Tainan, Taiwan, ROC
Ming-Tien Tsai, Professor, Cheng-Chung Chen, PhD, and Chao-Wei Chin, PhD, Department of Business Administration, National Cheng Kung University, Tainan, Taiwan, ROC. Appreciation is due to reviewers including: Wen-Ko Liang, Associate Professor, Department of International Business, Southern Taiwan University of Technology, No. 1 Nantai Street, Yung-Kang City, Tainan 710, Taiwan, ROC. Email: email@example.com
Please address correspondence and reprint requests to: Cheng-Chung Chen, No 70, Lane 51, Sec. 2, Jinhua Road, South District, Tainan City 70260, Taiwan, ROC. Phone: +886-918-548547; Fax: +88606-255-3085; Email: firstname.lastname@example.org or email@example.com
TABLE 1 Means, Standard Deviations, and Correlations (N = 130) Variable M SD 1 1. Engineer age 3.18 1.90 2. Work experience 3.63 2.15 0.26 ** 3. Engineer sex 0.15 0.35 -0.14 4. T-orientation of collaboration 3.88 0.57 -0.20 * 5. C-orientation of collaboration 3.63 0.63 -0.17 6. Interpersonal skills 3.73 0.56 -0.33 ** 7. Innovation performance 3.81 0.55 -0.32 ** Variable 2 3 4 1. Engineer age 2. Work experience 3. Engineer sex 0.06 4. T-orientation of collaboration 0.00 -0.06 5. C-orientation of collaboration 0.05 0.11 0.36 ** 6. Interpersonal skills 0.04 0.07 0.45 ** 7. Innovation performance 0.16 0.10 0.42 ** Variable 5 6 1. Engineer age 2. Work experience 3. Engineer sex 4. T-orientation of collaboration 5. C-orientation of collaboration 6. Interpersonal skills 0.35 ** 7. Innovation performance 0.38 ** 0.42 ** * p < 0.05, ** p < 0.01 TABLE 2 Effect of Orientations of Collaboration and Interpersonal Skills on Engineers' Innovation Performance Model 1 Model 2 Model 3 Control variables Engineer age -0.380 ** -0.232 ** -0.236 ** Work experience 0.255 ** 0.200 * 0.179 * Sex 0.026 0.035 0.059 Independent variables Technology orientation of collaboration (Tco) 0.231 ** 0.233 ** Customer orientation of collaboration (Cco) 0.180 * 0.155 + Moderator variables Interpersonal skills (IS) 0.167 + 0.211 * Interaction effects T-co x IS -0.002 C-co x IS 0.203 * [R.sup.2] 0.163 ** 0.341 ** 0.379 ** Adjusted [R.sup.2] 0.143 ** 0.309 ** 0.338 ** [DELTA][R.sup.2] 0.179 ** 0.038 * F 8.171 10.625 9.240 The dependent variable is performance. Standardized coefficients are presented. N = 130 (two- tailed test: + p < 0.10, ** p < 0.05, ** p < 0.01).…
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Publication information: Article title: Knowledge Workers' Interpersonal Skills and Innovation Performance: An Empirical Study of Taiwanese High-Tech Industrial Workers. Contributors: Tsai, Ming-Tien - Author, Chen, Cheng-Chung - Author, Chin, Chao-Wei - Author. Journal title: Social Behavior and Personality: an international journal. Volume: 38. Issue: 1 Publication date: February 2010. Page number: 115+. © 2009 Scientific Journal Publishers, Ltd. COPYRIGHT 2010 Gale Group.
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