科技多角化、組織剩餘對績效之影響:以智慧型手機產業為例

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(1)國立高雄大學亞太工商管理學系碩士論文. 科技多角化、組織剩餘對績效之影響: 以智慧型手機產業為例 Technological Diversification, Organizational Slack and Firm Performance: the case of smart phone industry in Taiwan. 研究生：楊德馨撰指導教授：陳一民博士. 中華民國一百年七月.

(2) 謝誌兩年前從台北來到高雄啟程的深造之旅，如今告一段落；雖然短暫的旅程結束了，這兩年我所得到的無形資產會伴隨著我一輩子。學生資質駑鈍，從小的學習歷程即是挫折連連、如屢荊棘之路。但很慶幸在人生的每一個階段都遇到貴人相助，不管在物質、心靈、生活等層面上，在這方面我真的非常的幸運。需要感謝的人實在太多，但單純「謝天」又太過籠統含糊。這兩年順利學習成長的功勞，首推我的指導老師：陳一民教授。總是給我。老師曾對我說過：策略性的思維，讓我及早對未來的方向有明確的「策略雄心」「心誠求之，雖不中不遠矣」，這句話學生將永遠謹記在心。此外，過程中也受到高蘭芬、黃怡芬、黃明新、黃豪臣、劉信賢、盧昆宏教授給予的寶貴意見及幫助。曾有教授說過：生命是一種長期而持續的累積過程。學生對於這句話也有很深的體會，倘若沒有父母、師長、朋友的幫助，也沒有今日的我，我對各位朋友的感謝實在難以言喻。今天，結束了現階段的工作，下一階段將是我追逐夢想的時刻，還有好一段時日，得努力奮鬥！也期望往後的日子，我還能與現在一般，熱愛學習、熱愛閱讀，持續充實學識，以期能對我的朋友及社會有所貢獻。楊德馨謹誌於國立高雄大學中華民國民國一百年七月. I.

(3) 中文摘要科技多角化、組織剩餘對績效之影響: 以智慧型手機產業為例指導教授：陳一民博士國立高雄大學亞太工商管理學系學生：楊德馨國立高雄大學亞太工商管理學系. 在 21 世紀的第一個十年裡，在科技策略領域有許多發現支持，創新能力與科技多角化可作為企業維持競爭優勢的關鍵資源。也因為擁有創新對於企業競爭優勢的重要性日與俱增，學術界也對於公司科技多角化與績效間之關聯性抱持著極大的興趣。然而，先前的研究大多探討對於創新績效的影響，較少探討對於公司績效之影響。為了探討科技多角化與公司績效之間的關聯，本文以台灣智慧型手機產業作為實證之對象，並以權變之角度來探索科技多角化對於台灣智慧型手機廠商績效之影響，在不同策略定位—組織剩餘的情況下，而本研究依照學者先前之研究將組織剩餘分類為可吸收剩餘、不可吸收剩餘兩種種類。假說的實證結果也確認了科技多角化對於智慧型手機產業製造商之績效影響是顯著負向關聯的，在 Tobin's q 和 MVA 兩績效指標上為顯著，在 ROA 及 EVA 指標上則不顯著。此外，可吸收與不可吸收剩餘對於科技多角化及公司績效的調節作用上，在 EVA 和 MVA 指標上有顯著影響，但對於 ROA 和 Tobin's q 指標上為不顯著。 II.

(4) 這些發現指出台灣智慧型手機廠商在不同的科技多角化程度上，可以效用化其所擁有的不同類型之剩餘資源來強化其績效。根據不同種類的組織剩餘，本研究之結果也表示了不同組織剩餘對於績效之調節作用，也與本研究在組織理論及代理理論上之預測一致。. 關鍵字: 科技多角化、組織剩餘、公司績效、智慧型手機產業、台灣. III.

(5) Abstract Technological Diversification, Organizational Slack and Firm Performance: the case of smart phone industry in Taiwan Advisor: Dr. Yi-Min Chen Department of Asia-Pacific Industrial and Business Management National University of Kaohsiung Student: De-Hsin Yang Department of Asia-Pacific Industrial and Business Management National University of Kaohsiung. The first decade of the twenty-first century witnessed a significant increase in technology strategy research relating to innovation capacity and technological diversity recognized as the critical sources of competitive advantages among the firms. Owing to the increasing importance of innovations to the competitive advantages of the firms, scholars recently have paid attention to the relationship between corporate technological diversification and performance. However, many previous studies have been made on innovation performance rather than firm performance. Acknowledging the increasingly important role of Taiwanese smart phone sectors, this study follow the contingency approach to explore the impact of a strategic orientation toward corporate technological diversity on Taiwanese smart phone firms‟ performance under the context of different organizational slack conditions, absorbed and unabsorbed IV.

(6) slack resources. The results of hypothesis testing confirm the existence of a significant and negative relationship between technological diversification and firm performance in terms of Tobin‟s q and MVA, but not of ROA and EVA. In addition, organizational slack of a company moderates the relationship between technological diversification and firm performance in terms of EVA and MVA, but not of ROA and Tobin‟s q. The current findings indicate that Taiwanese smart phone firms with different technological diversity approach could utilize different slack resources to improve their firm performance. Based on different types of slack resources, the current results of different moderating role of organizational slacks in affecting firm performance support the predictions of organizational theory and agency theory.. KEY WORDS: Technological diversification, Organizational slack, firm performance, smart phone industry, Taiwan. V.

(7) 目錄謝誌................................................................................................................................ I 中文摘要 ...................................................................................................................... II Abstract...................................................................................................................... IV List of figures .......................................................................................................... VIII List of Tables ............................................................................................................. IX Chapter1: Introduction ............................................................................................... 1 1.1Research background........................................................................................................ 1 1.2 Research motivation and purposes .................................................................................. 3. Chapter 2: Literature review and hypothesis development ..................................... 7 2.1 Technological diversification .......................................................................................... 7 2.2 Organizational slack and Performance .......................................................................... 14 2.2.1 The moderating role of absorbed slack................................................................... 17 2.2.2 The moderating role of unabsorbed slack............................................................... 19. Chpater3: Research method ..................................................................................... 21 3.1 Research framework ...................................................................................................... 21 3.2 Survey procedures and sample ...................................................................................... 22 3.3 Methodology ................................................................................................................. 25 3.3.1 Hierarchical regression analysis ............................................................................. 25 3.4 Measure of variable ....................................................................................................... 27 3.4.1 Organizational slack ............................................................................................... 27 3.4.2 Technological diversification ................................................................................. 28 3.4.3 Return on total assets (ROA) .................................................................................. 30 3.4.4 Tobin‟s q................................................................................................................. 30 3.4.5 MVA ....................................................................................................................... 31 3.4.6 EVA ........................................................................................................................ 32 3.5 Control variable ............................................................................................................. 35 VI.

(8) 3.5.1 Firm size ................................................................................................................. 35 3.5.2 R&D intensity ......................................................................................................... 35. Chapter 4: Empirical Results ................................................................................... 36 4.1 Descriptive statistics ...................................................................................................... 36 4.2 Pearson correlation analysis .......................................................................................... 38 4.3 Measurement model ...................................................................................................... 39 4.4 Discussion and implication............................................................................................ 48. Chapter 5: Conclusion and Limitation .................................................................... 52 5.1 Conclusion ..................................................................................................................... 52 5.2 Limitations and future research ..................................................................................... 55 Reference： ......................................................................................................................... 56. VII.

(9) List of figures Figure 3-1: The hypothesized model ........................................................................ 21. VIII.

(10) List of Tables Table 3-1 Taiwanese smart phone industry classification and the number of sample firms ......................................................................................................................... 23 Table 3-2: The calculation of technological diversification .................................... 29 Table 4-1: The number of patent of Taiwanese smart phone industry from 2003-2008: ............................................................................................................... 36 Table 4-2: Notice the technological diversification index of Taiwanese smart phone industry in from 2003-2008: .................................................................................... 36 Table 4-3: Descriptive statistics: ............................................................................. 37 Table 4-4 Pearson correlation coefficient analysis: ................................................. 38 Table 4-5 Results of regression analysis (Technological diversification on dependent variable: ROA, Tobin‟s q, MVA, and EVA) ........................................... 39 Table 4-6: Results of regression analysis (Dependent variable: ROA) ................... 41 Table 4-7: Results of regression analysis (Dependent variable: Tobin‟s q) ............ 42 Table 4-8: Results of regression analysis (Dependent variable: MVA)................... 43 Table 4-9: Results of regression analysis (Dependent variable: EVA) .................... 44 Table 4-10: Result of hypothesis testing: ................................................................ 45 Table 4-11 Results of regression analysis (Technological diversification squared term on dependent variable: ROA, Tobin‟s q, MVA, and EVA) ............................. 47. IX.

(11) Chapter1: Introduction. 1.1Research background After Apple Inc. launching the iPhone in June 29, 2007, the smart phone industry started to upsurge. Even during the world‟s financial crisis in 2008-2009, the sales of smart phones continued to grow up and totaled 40,962,800 representing a growth of 27%, while other consumer electronics products had a serious decline in that period (Market Intelligence & Consulting Institute, 2008). Beginning with Blackberry launched by RIM, smart phones not only include the features of mobile phone, music player, and digital camera, but also integrate the functions of software applications with Internet-based. These features not only satisfy the needs of businessmen in the world, but also create the demands from the public. With the good user experience of iPhone and RIM, Google, the dominant Internet search engine player, has also entered the smart phone market by launching the Android platform with 33 related Open Handset alliances. While Apple, Nokia and RIM use the closed system to position themselves in the smart phone market, Google uses open source way to create the possibility of innovations for the smart phone industry. Thus, the smart phone industry is a representative of consumer electronics‟ innovations, and has become very competitive and demonstrated outstanding performance since the late 1.

(12) 2000s. Along with highly competitive dynamics in the industry, the smart phone players have taken many competitive actions/reactions to get more competitive advantages in the market. For example, the patent lawsuit is an important strategy for a smart phone company to attack or respond others‟ business behavior. The Finnish mobile phone giant Nokia sued the U.S. smart phone company Apple in the U.S. Delaware federal court in October 2009 because of iPhone infringements of Nokia‟s mobile phones as many as ten. These infringements cover the patents of wireless communications, speech coding, security, and encryption programs. In addition, Apple Inc. sued its smart phone competitive rivalry HTC, the emerging Taiwanese smart phone brand, against HTC‟s illegal practice of 20 related to iPhone patent ownership of user interface, infrastructure, and hardware. After this patent lawsuit, HTC also immediately make a counter lawsuit against Apple. Thus, these competitive actions/reactions have made the smart phone industry much fierce. Meanwhile, to respond these highly competitive dynamics among the firms, smart phone players including designers and manufacturers have put more investments to develop new technologies and patents, and to establish a patent portfolio within the firm. Since their success in the global production networks, Taiwanese smart phone manufacturers have emerged as formidable global market players and 2.

(13) demonstrated a powerful mechanism of industrial clustering that illustrates geographic concentrations of interconnected firms and associated institutions in a similar field. For example, most of Taiwanese smart phone manufacturers of IC, photoelectric, passive, peripheral and mechanical components, and assembling process. From the perspective of conducting patents innovation, Chen (2010) finds that corporate technological diversification strategy is matter to the development of Taiwanese IT sectors. Thus in this study, we following Chen‟s approach use patents information from the United States Patent Office to estimate technological diversification of patent portfolio for Taiwanese smart phone industry.. 1.2 Research motivation and purposes The first decade of the twenty-first century witnessed a significant increase in technology strategy research relating to innovation capacity and technological diversity recognized as the critical sources of competitive advantages among the firms (Chen, 2010; Huang and Chen, 2010; Subramanian and Youndt, 2005). In terms of innovation capacity issue, prior studies in innovation literature have paid much attention to the relationship between organizational slack resources and innovations in different approaches (e.g., Damanpour, 1991; Greve, 2007; Judge et al., 1997; Nohria and Gulati, 1996; Tan, 2003; Voss et al., 2008; Yang et al., 2009; Huang and Chen, 3.

(14) 2010). On the other side, owing to the increasing importance of innovations to the competitive advantages of the firms, scholars recently have also paid attention to the relationship between corporate technological diversification and performance (Lin et al., 2006; Leten et al., 2007; Garcia-Vega, 2006; Breschi et al., 2003; Huang and Chen, 2010; Suzuki and Kodama, 2004). The conventional measure of firm performance relies on raw accounting values of returns on assets (ROA). However, a famous debate, which includes comments by Horowitz (1984), Long and Ravenscraft (1984), Van Breda (1984), and a reply by Fisher (1984), raise questions about whether or not ROA reflects monopoly rents and whether or not booked assets are fairly depreciated. As McGahan and Porter (1997) and Hawawini et al. (2003) indicated, there are shortcomings to the accounting measures of profit. First, because accounting conventions such as Generally Accepted Accounting Principles (GAAP) exclude intangible assets from the balance sheet, accounting measures of assets may be understated for some firms. Second, ROA using operating income (i.e. earnings before interest and taxes) divided by total assets excludes the effects of differences in financing, such as the cost of capital. Since ROA conveys neither the cost of capital nor adjusts for accounting policies that may distort the true values of the measure, such as asset values (Hawawini et al., 2003), the adoption of value-based measures, such as the 4.

(15) consultancy Stern Stewart & Co.‟s economic value added (EVA), has coincided with increasing pressure from capital markets and corporate control markets for managers to focus their strategies on value creation, i.e. economic performance (Haspeslagh et al. 2001, Hawawini et al. 2003). Value creation occurs only when firms earn returns greater than the cost of capital, which implies that value creation is a reasonable proxy for economic performance. Stewart (1991) introduced value-based economic measures of performance: EVA and market value added (MVA). EVA, indicating economic profit, reflects operating performance in a given year, while MVA, indicating market-to-book value, reflects the market‟s expectations of the firm‟s future operating performance. Except accounting measure ROA and economic profitability measures EVA and MVA, Tobin‟s q reflects investor expectations about firm value relative to asset replacement cost. By incorporating a capital market measure of firm rents, Tobin‟s q implicitly uses the correct risk-adjusted discount rate, imputes equilibrium returns, and minimizes distortions due to tax laws and accounting conventions. Thus, Tobin‟s q is theoretically a much more appealing measure than accounting returns. Since these four measures of firm performance incorporate different information and in general are imperfectly correlated, this study uses these four types of returns to measure firm performance. Acknowledging the increasingly important role of Taiwanese smart phone 5.

(16) sectors, this study follow the contingency approach to explore the impact of a strategic orientation toward corporate technological diversity on Taiwanese smart phone firms‟ performance under the context of different organizational slack conditions, absorbed and unabsorbed slack resources.. The main purpose of this study are as follows: 1.Does firms have more available slack can moderate the technological diversification and each type slack had different utility? 2. Technological diversification would affect the firm performance? Keywords: Smart phone, Organizational slack, Patent, Technological diversification, Firm Performance. 6.

(17) Chapter 2: Literature review and hypothesis development. 2.1 Technological diversification Patent can protective benefits of R&D results, so firms tend to patent in the form of published research results. Patent provides a wealth of R&D and technical information, patent information can reduce the use of research funding and research time (Narin, 1995; Narin et al., 1987; Porter and Detampel, 1995). Therefore, the information provided by the patent can find the technical information of competitors. To identify and help firms effectively manage the allocation of R&D resources (Ernst, 1998). That is, the patent on behalf of a firm that hold the type of technical capabilities and advantages, through patent analysis to understand the different technical expertise of enterprises, while exploring the development of industrial technology-specific trajectories and corporate layout at the same time. Product diversification has been a highly popular strategy (Rumelt, 1974), and plays a key role in the strategic behavior of large and growing industrial firms in the United States, Europe, Asia, and other parts of the industrialized world (Berry, 1975; Dyas and Thanheiser, 1976; Suzuki, 1980; McDougall and Round, 1984; Chang 7.

(18) and Choi, 1988; Hitt, Hoskisson, and Ireland, 1994; Hitt, Hoskisson and Kim, 1997; Wan and Hoskisson, 2003). Product diversification strategy can yield technological diversity, e.g., technological diversification. Given the substantial research on product diversification and its assumed effects on firm outcomes (Hoskisson and Hitt, 1990). Owing to the increasing importance of technologies to be the competitive advantage of the firms, scholars recently have paid much attention to the technological diversification issues of firm (e.g. Dibiaggio, 2004; Suzuki and Kodama, 2004; Lin et al., 2006; Garcia-Vega, 2006; Grandstrand, 1998). Nelson (1959) considered that firms that diversify their technological base are likely to benefit from new technological possibilities. Since many innovations are designed to solve unrelated problems, companies that are more diversified profit more from their own research activities, because they capture more of the social benefits of their innovations. Investments in R&D are used as competitive “weapons” (Baumol, 2002). And technological diversification can prevent a negative lock-in effect in one particular technology, and it can sustain the evolution and business renovation of the firm. Technology diversification is suggested to be beneficial to the innovation performance in terms of economy of scope and knowledge-base view (e.g., Granstrand, 1998; Suzuki and Kodama, 2004; Turner and Fauconnier,1997; Almeida and Phene, 2004; Lin etal.,2006). Granstrand (1998) argued that the central role 8.

(19) played by technology diversification in the evolution of a technology-based firm from the view points of economies of scope, speed, and space. Similarly, Suzuki and Kodama (2004) suggest that taking advantage of economies of scope in technology through persistent diversification is necessary for a technology-based firm if it is to survive and to grow for a prolonged period of time. To overcome lack of meaningful measures of innovative inputs and outputs argued by Kuznets (1962), new data sources measuring patented inventions develop from the computerization by the U.S. Patent Office (Hall et al., 1986; Jaffe, 1986; Pakes and Griliches, 1980), better measures of R&D (Bound et al., 1984; Scherer, 1982), and stock market values of inventive output (Pakes, 1985). In addition, several researchers (e.g., Acs and Audretsch, 1988; Pakes and Griliches, 1980) have used the number of patents a firm holds as a measure of inventive activity. The entropy measure of product diversification (Jacquemin and Berry, 1979; Palepu, 1985) was employed to measure technological diversification. This index has become increasingly popular in strategic management research (e.g., Baysinger and Hoskisson, 1989; Hill, Hitt and Hoskisson, 1992; Hitt el al., 1996; Palepu, 1985). Also, it has been reported to generate estimates of product diversification similar to those based on Rumelt‟s (1974) subjective categorization methods and to evidence construct validity (Hoskisson, Hitt, Johnson and Moesel, 9.

(20) 1993). The entropy measure of diversification (TD) has two components, related diversification (RD) and unrelated diversification (UD), so that TD=RD+UD. Related diversification (RD) is defined as the diversification arising from operating in four-digit segments within a two-digit industry group (arising out of operating in several segments within an industry group). Unrelated diversification is defined as diversification arising from operating between two-digit industry groups. Researchers use SIC codes to define the industry segments and groups, treating two-digit SIC industries as the industry groups and fourdigit SIC industries as the industry segments (Baysinger and Hoskisson, 1989; Palepu, 1985). Combining the entropy measure of diversification and technological diversity proposed by Miller (2006), argued that firm‟s knowledge base interacts with its product market activity, can concern by creating a measure of technological diversity based on citation-weighted patents. The measure indicates a firm‟s opportunity for corporate diversification based on economies of scope in valuable knowledge assets. The result shown that a large sample of firms shows the positive relationship. between. diversification. based. on. technological. diversity. and. market-based measures of performance, controlling for R&D intensity and capital intensity as further indicators of the type of assets underlying diversification. The role of innovation, e.g., patents, in creating firm value has long been 10.

(21) recognized. Firms undertake investment in research and development in hopes of developing patents that lead to increased performance. Prior research has found a positive correlation between innovation and firm value (Griliches, 1981; Pakes, 1985). For example, Griliches (1981) reported that investments in innovation can yield returns of 200 percent over the long run. The role of technological diversification on the firm performance, however, is not as clear. Based on the studies of product diversification, firms would find it particularly beneficial to pursue low levels of product diversification to stay focused and attain more specialized product-market expertise (Wan and Hoskisson, 2003). Because the sources of competitive advantage in these environments rest on continuous improvements in the value chain, specialized capabilities in certain transformational activities, leading to patents or consumer loyalty, constitute significant barriers to entry (Wan and Hoskisson, 2003). Low product diversification, which places great emphasis on developing unique, critical capabilities, is likely to enhance firm performance (Wan and Hoskisson, 2003). From opponent of perspective, building on the work of Rumelt (1974, 1977) investigated the relationships among diversification strategy, organizational structure and economic performance. Rumelt tied diversification strategy to financial performance. The related diversification strategies were found to outperform the other diversification strategies on the average. By contrast the unrelated diversification 11.

(22) strategy was found to be one of the lowest performing diversification strategies. Rumelt (1977) suggested that firms in industries is characterized by an extensible core skill and many opportunities for product differentiation and segmentation. They are excellent fields for using a difficult-to-replicate competence to create unique and defensible product-market position. Montgomery (1979) argued that performance differences between related-constrained and unrelated firms, the essence of which is that related-constrained firms tend to be in industries whose market structure leads to above average profitability. Montgomery also founded that an individual firm‟s profitability depended on the weighted average industry concentration, weighted average industry profitability, and weighted average firm market share across all of the industries the firm participates in. Bettis (1981) have founded the same result as Rumelt that science-based relatedness would appear to be particularly potent in generating excess returns. This suggests that for research and development related (i.e. science-based) firms, management should focus particular emphasis on maintaining a strong degree of relatedness along the research and development dimension as the firm continues to grow and diversify (Ehrbar, 1980). As the extent of technological diversification increases, it is unavoidable that a firm not only pays more coordination costs (Granstrand and Oskarsson, 1994), but it also enable a firm to uncertainty due to unrelated fields or unfamiliar activities 12.

(23) (Brown, 1992). With a narrow technological base can enhance knowledge accumulation. When the technological scope is narrow, a firm can accumulate its technological competence in similar fields, producing a higher learning effect and accumulation of knowledge (Stuart and Podolny, 1996). As Breschi et al. (2003) pointed out, focusing their R&D in a small number of technological fields can allow firms to gain more profit from the specialization of their research activities. Technological specialization can also enhance the economic of scale associated with the learning process, speeding the transfer of knowledge between the core technologies of the firm, and benefit from the technological „„comparative advantages‟‟ of the firm (Garcia-Vega 2006). The knowledge accumulation has path dependency effects through which a firm‟s core competence can be established and enhenced (Rheem 1995). Thus, this study expected if the higher technological diversification would lead lower performance. The relationship between technological diversification and firm performance may be negative, then form the third hypothesis:. Hypothesis 1: The negative effect of technological diversification on firm performance in such a way that high levels of technological diversification decrease the firm performance 13.

(24) 2.2 Organizational slack and Performance The first decade of the twenty-first century witnessed a significant increase in technology strategy research relating to innovation capacity and technological diversity recognized as the critical sources of competitive advantages among the firms (Subramanian and Youndt, 2005; Chen, 2010; Huang and Chen, 2010). In terms of innovation capacity issue, prior studies in innovation literature have paid much attention to the relationship between organizational slack resources and innovations in different approaches (e.g., Damanpour, 1991; Nohria and Gulati, 1996; Judge et al., 1997; Tan, 2003; Greve, 2007; Voss et al., 2008; Yang et al., 2009; Huang and Chen, 2010). On the other side, owing to the increasing importance of innovations to the competitive advantages of the firms, scholars recently have also paid attention to the relationship between corporate technological diversification and performance (Suzuki and Kodama, 2004; Lin et al., 2006; Garcia-Vega, 2006; Breschi et al., 2003; Leten et al., 2007; Huang and Chen, 2010). In this study, we follow the contingency approach to explore the impact of a strategic orientation toward corporate technological diversity on firm performance under the context of different organizational slack conditions, absorbed and unabsorbed slack resources. Since the 1980s, many researchers treat organizational slack from a 14.

(25) theoretical perspective (e.g., Bourgeois, 1981; Nohria and Gulati, 1996), and often define organizational slack as the cushion of actual or potential resources that allow an organization to adapt successfully to internal pressures for adjustment or to external pressures for change in technologies or markets (Bourgeois, 1981; Lawson, 2001). Meanwhile, the resource-based view of the firm (RBV) has recognized that the strategic resources, like organizational slack, can enhance the ability of external adaptation to the environment and the creation of competitive advantage, and improve enterprise strategy execution to create better performance (Barney, 1991; Peteraf, 1983; Prahalad and Hamel, 1990; Wernerfelt, 1984). Thus, based on the RBV theory, organizational slack resources play a crucial role in allowing the firms to experiment with new strategies and innovative projects that might not be approved in a more resource-constrained to environment (Cyert and March, 1963). On the other hand, from competitive dynamics perspective have identified the awareness, motivation and capabilities of a firm (Chen, 1996; Chen, Su et al., 2007). Lamberg et al. (2009) found that a focused and resourceful administration enhances the awareness to act and accepted strategic focus motivates the firm to promote the strategic objectives. Thus, sufficient slack resources enable the capability to respone for competitive actions. Insufficient slack resources may result in a lower excutiton, paving the way for firms to pretermit growth (Hambrick and D‟Aveni, 15.

(26) 1988, 1992). Since the 1960s, the definition of slack resources can include excess input such as redundant employees, unused capacity, and unnecessary capital expenditures, and also include unexploited opportunities to increase outputs, such as increases in the margins and revenues that might be derived from customers and innovations that might push a firm closer to the technology frontier. Through buildup and cultivation of organizational slack resources, previous studies have used organizational slack in different forms as a predictor of risk-taking (Wiseman and Bromiley, 1996), innovation (Nohria and Gulati, 1996) and performance (Bromiley, 1991; Tan and Peng, 2003). In addition, organizational slack can be deployed in various ways. A firm can use slack resources to respond to uneven performance (Kamin and Ronen, 1978) or to such contingencies as budget cuts or environmental jolts (Meyer, 1982), as well as to engage in slack search, or experimentation (Levinthal and March, 1981). Since the 1960s, scholars such as Cyert and March (1963) and Thompson (1967) have argued that slack may be useful to organizations because it provides an essential buffer to their activities. For example, without organizational slack, any reductions in cash flow will result in immediate shortages of funds. Such shortages will result in dysfunctional organizational changes such as layoffs and cancellation of capital investments. Firms use organizational slack to smooth investment, staffing, and so forth and to 16.

(27) buffer their technological cores from short-term random fluctuations in the environment. Furthermore, firms with additional resources have more strategic options available than firms without resources (Bromiley, 1991). Thus, available resources in the form of slack provide a strategic advantage and recent researchers have found that slack can enhance operating performance of manufactures and can be invested to create and generate new resources or strengthen and extend existing resources (Miller and Leiblein, 1996; Daniel, Lohrke, Fornaciari, and Turner, 2004). Although many organizational slack studies focus on the relationship with performance, little research has been done to investigate the effect of organizational slack resources could explain the variation in technological diversification on firm performance.. 2.2.1 The moderating role of absorbed slack Recently, some studies identify that different types of slack resources, such as absorbed and unabsorbed and unabsorbed slacks, may have different effects on innovations (e.g., Greve, 2007). Absorbed slack is organizational slack in the form of administrative resources beyond what is necessary for the short-term operation and maintenance of the organization (Singh, 1986; Greve, 2007) and is directly and useful for developing innovations. For example, organizations with more absorbed slack would provide extra facilities for R&D, staff specialized for development activities 17.

(28) among other staff that are needed for stimulating innovations (Greve, 2003). Thus, absorbed slack in the evolution of a technology-based firm commonly acts as internal shock absorber to provide the needed resources to make the planned innovation projects progress over time and therefore achieve the benefits of economy of scope across different technology fields (Huang and Chen, 2010). In addition, absorbed slack not only includes large administrations, costly facilities, and high wage levels that are directly for innovation development, but also provides a cushion or buffer to integrate different technological knowledge across disciplinary frontier by resolving latent goal conflicts and reducing resource competition among the innovation projects and accordingly result in a higher level of cross-fertilization of heterogenous knowledge that are useful for developing innovations (Greve, 2003). Since Greenhalgh (1983) hypothesized that organization slack would have a positive influence on innovation, further empirical studies have found the positive effects of slack on innovation and performance (Singh, 1986; Damanpour, 1987; Bromiley, 1991; Zajac, Golden and Shortell, 1991; Majumdar and Venkataraman, 1993; Greve, 2003). Chen and Hambrick (1995) has found that organizational slack tends to suppress initiative actions and promotes responsive actions. Furthermore, organizational slack allows firms to respond in more creative ways (Smith et al., 1991). Therefore, based on the important and positive relationship among 18.

(29) technological diversity, absorbed slack, and innovation performance found by Huang and Chen (2010), this study extends Huang and Chen‟s research and hypothesized that absorbed slack positively moderates the relationship between technological diversification and firm performance. Thus, for the technological diversification, this study propose absorbed slack positively moderates the relationship between technological diversity and innovation performance. The above reasoning leads to the following hypothesis:. Hypothesis 2: Absorbed slack will positively moderate the strength between the technological diversification and firm performance.. 2.2.2 The moderating role of unabsorbed slack Unabsorbed slack is organizational slack in the form of financial reserves (Singh, 1986), which is more easily redeployed everywhere and allowing for greater managerial discretion (Tang and Peng, 2003). While absorbed slack is directly useful for developing innovations, unabsorbed slack is not directly helpful in the development of innovations. However, unabsorbed slack affects managerial decisions to continue innovation projects because great financial resources lead to laxer 19.

(30) performance monitoring of uncertain projects (Greve, 2007). While unabsorbed slack can stimulate the management to support new innovative activities (Thompson, 1967; Nohria and Gulati, 1996; Huang and Chen, 2010), a higher level of unabsorbed slack may cause premature termination of innovation projects due to its strict performance monitoring (Greve, 2007). In other words, before the management in a technology-based firm has accumulated enough experience to know whether they will eventually improve its performance (Lounamaa and March, 1987), a higher level of unabsorbed slack may cause inefficient problems in an over-diversified technology base (Tang and Peng, 2003; George, 2005; Huang and Chen, 2010). Therefore, this study extends Huang and Chen‟s (2010) research and hypothesize in absorbed slack negatively moderates the relationship between technological diversification and firm performance. Hypothesis 3: Unabsorbed slack will negatively moderate the strength between the technological diversification and firm performance. 20.

(31) Chpater3: Research method. 3.1 Research framework Our three hypotheses are summarized in Figure 1. In the following section we shall confront this model with the empirical data.. Figure 3-1: The hypothesized model. 21.

(32) 3.2 Survey procedures and sample This study is to investigate the relationship between the slack resource of firm, technological diversification, and performance. Thus, this research focus on there has more R&D capabilities and science-based of the industry in Taiwan. The total industry in Taiwan over the years have the most investment amounts is in the electronic industry. Especially in communication equipment industry in Taiwan, the design and production of world famous. In 2009, the communication industry of Taiwan had revenue about 28.5 billion U.S. dollars, ranked the 7th highest in the world. The output value of Taiwan's communications equipment for the 20.4 billion U.S. dollars (Industrial Development Bureau, 2010). Therefore, this study chooses smart phone industry-related firms as of the object, use the TEJ database, selected the study period from 2004 to 2009, total of 7 years. This study selected 55 smart phone industry-related firms from listed firms in Taiwan. According to the classification of Taiwan Stock Exchange Corporation (TSE), including the 5 firms in semiconductor industry, 17 in photovoltaic industry, 3 in other electronics, 3 in communication network industry, 1 in electronic channel industry, 18 in electronics components industry, and 8 in Computer and peripheral equipment manufacturing.. 22.

(33) Table 3-1 Taiwanese smart phone industry classification and the number of. sample firms. IT industry classification. No. of firms. Semiconductor. 5. Photovoltaic. 17. Other electronics. 3. Communication network. 3. Electronic channel. 1. Electronics component. 18. Computer and peripheral equipment. 8. This study collected the patent data of 55 sample firms from the database provided by the U.S. Patent and Trademark Office (USPTO). Data collected period from 2003 to 2008, total of 3,635 patents to calculate technological diversification. Exclude the missing data, this study totally collect 294 samples. The USPTO oversees the process of granting property rights to inventors for inventions that are „useful‟ and „novel‟. Patent data are systematically compiled, have detailed information, and are 23.

(34) available continuously across time. A number of previous scholars have used patent data as a proxy for innovation (Argyres and Silverman, 2004; Fleming and Sorenson, 2004; Gittelman and Kogut, 2003; Henderson and Cockburn, 1994; Rosenkopf and Nerkar, 2001). Patent file lists the corporation and business unit that applied for each patent, the technological class to which each patent belongs. In addition, a patent document contains a list of citations to other patents which represent the technological antecedents to the particular innovation. Thus, we use U.S. patent data to compare the performance of patent diversification of Taiwanese smart phone industry.. 24.

(35) 3.3 Methodology. 3.3.1 Hierarchical regression analysis Hierarchical regression analysis method is take the single effect of important predict variable successively put in regression model. In order to understand the various class of the regression model to total explained variance capacity of dependent variable and individual prediction of variables and its explanation of variability. This study used hierarchical regression analysis to examine firms from 2003-2008. Try to find the relationship between technological diversification, organization slack, and firm performance. Consider the resources, structure, and strategy of firms may impact on the performance of a deferred nature, so the strategy of the company during the performance of the next variable t, used t +1 period. This study try to examine Hypothesis 1, 2, and 3 using following regression:. 25.

(36) E(8). E(12). : Firm size (number of employee) of firms during t period. : R&D intensity of firms during t period. : Absorbed slack of firms during t period. :. slack of firms during t period. 26.

(37) : Technological diversification of firms during t period. : ROA of firms during t+1 period. : Tobin‟s q of firms during t+1 period. : Market Value Added of firms during t+1 period. : Economic Value Added of firms during t+1 period.. 3.4 Measure of variable. 3.4.1 Organizational slack Organizational slack has been widely conceptualized into two primary dimensions: absorbed slack and unabsorbed slack (Sharfman etal.,1988; Tan and Peng, 2003). Absorbed slack is due to the hard-to-redeploy nature whereas unabsorbed slack reflects the easy-to-redeploy elements of the organizational resources (Singh 1986; Tan and Peng, 2003). This study following Tan and Peng (2003) and Huang and Chen (2010), this study use three items: major repair fund, inventory fund, and accounts payables, to measure absorbed slack. The absorbed slack dimension is measured as the sum of the standardized estimations of these three items. Likewise, unabsorbed slack is measured as the sum of the standardized estimations of five items: depreciation fund, reserve fund, loans, sales expenses, and retained earnings. 27.

(38) 3.4.2 Technological diversification The entropy measure of technological diversification is derived from product diversification (Jacquemin and Berry, 1979; Palepu, 1985). Combining the entropy measure of diversification and technological diversity proposed by Miller (2006) and Chen (2009) followed to calculate the index of technological diversification. This study collect data from the United States Patent and Trademark Office, and to take patent data of smart phone industry manufacturers in Taiwan from 2003-2008. The classification of data is according to the U.S. Patent Office Patent classification code (US_Class) provided, taking patent classification codes in the first four digits of UPC codes as the class number to calculated value patent diversification. Each cited patent is identified by U.S. Patent Class (UPC) and related to the distribution. Adding up within 4-digit patent codes gives a citation-weighted count of „patent equivalents.‟ Made most in number of (core patents) to and for each value of the difference, if any patent equivalent in the same 4-digit SIC as the core patent is assigned a zero, the counts sharing the same 3-digit SIC code are assigned a one, the same 2-digit code a two, the same 1-digit code a three, and in different 1-digit codes the patent equivalents are assigned a four. To summarize, the index is a measure of the 28.

(39) dispersion of patent applicability across firms, with those patents weighted by adjusted citation counts and depreciated over time. The variable may be higher because the firm has many different classes of patents. The variable as. , i is the. core patent and j to measure patent dispersed in the enterprise level. If the business is more focused on the development of the patent category, then the patent. values. will be lower, if the company adopted a strategy to technology diversification, the development of many types of patents, the. values will be higher.. For example, a firm have 5 patents in 2008: Table 3-2: The calculation of technological diversification. Code. Difference. Sum. T.D. index. 2211. 3. 11. 2.2. 2143. 0. 2143. 0. 3706. 4. 3051. 4. The most frequent U.S. patent code is 2143, so used it as core patent of firm.. 29.

(40) 3.4.3 Return on total assets (ROA) ROA is to measure the efficiency of the use of asset management companies. It‟s also called DuPont Analysis. Measure of the study variables using the following formula:. ROA = [Net income + Interest expense (1-25%)] ÷ Average total assets. 3.4.4 Tobin’s q Lang and Stulz (1994) are using Tobin's q as a measure of corporate performance variables. In addition, the evidence also shows that the Tobin's q measure of market value, not only stable but also fit in response to the revenue of R&D activities (Ayadi, Dufrene, and Obi, 1996). Tobin's q used the company's market value as the numerator, the company's replacement cost of tangible assets are calculated as the denominator of the ratio. The higher the value of intangible assets of the company, Tobin's q values are more higher. That is, when firms have the stronger monopoly power, value of goodwill, and skilled manager more, the company's Tobin's q values are also larger. However, due to the complexity estimates of the replacement cost of assets must be calculated separately 30.

(41) inventory, land, plant, and other equipment, then must be price inflation. Other factor need to be concerned, like the real rate of depreciation, the value of such capital expenditures and investment into consideration. Thus, this study refer to Chung and Pruitt (1994), developed a relatively simple approximate Tobin's q (approximate q) of the formula, simply through the basic financial and accounting information can be calculated. Approximate q =. 3.4.5 MVA Mentioned in Fortune magazine: MVA can show some of the increase in equity, so the MVA is an external measure of financial performance. It‟s also a good measure of overall performance to firms. MVA reflects the company's overall operating performance and make business aggregated its limited resources distribution and adequate treatment management. To create the greatest value of the enterprise, thereby enabling the shareholder wealth maximization, and often applied by scholars at the attention and use. Lehn and Makhija (1996) point out that the traditional performance evaluation assessment indicators have neglected the cost of surplus investment funds, while the MVA but took into account the investment cost of funds, and the whole enterprise is the best measure performance indicators. The 31.

(42) premium of shareholder value is the measure of the market value of equity less the book value of equity, while the MVA is the market value of equity and debt net of equity and debt book value of the economy. The company's MVA and can be expressed as equity MVA (market value of equity less book value of equity of the economy) and liabilities of the MVA (market value of debt less liabilities of the economic book value of debt). The market value increases when the company implemented a NPV> 0 the project case, will increase its market value, NPV calculation is the company's future cash amount of the discount period to reduce the sum derived from the original capital invested. MVA calculation, the capital's economic book value is equivalent to the investment company's past and present invested capital combined. NPV method of the future cash flows discounted value of the aggregation, that is the market value of capital of the firm by the MVA method. In other words, the MVA of firm increase, that means the company has invested NPV> 0 of the plan.. MVA＝Market Value of equity－Book Value of equity. 3.4.6 EVA Economic Value Added (EVA) was developed by the New York Stern Stewart & Co. financial consultants for the purpose to firm's financial performance 32.

(43) measures. The measure was derived from the concept of residual income. EVA emphasizes the firm should earn more than the return of the cost of capital to create shareholder value (Stewart and Bennett, 1990; Stern and Joel, 1993). In general, the increase in shareholder value is the value from the enterprise economy value creation, and economic value of the enterprise can through increase the profitability, improved working capital management, or effective project investment to be created. Thus, the EVA is used to measure a certain period in the economic value created is higher than the cost of capital assets that use as a specialized technique (Bennett and Linda, 1995; Fisher, 1995; Grant, 1996). EVA has moved from academic jargon evolved into a financial phenomenon. EVA is a performance management as an indicator, can use in various departments, project managers and the performance evaluation of each firm, and it is also has the effect of incentives. As long as the firm adopted to EVA, the various departments in order to achieve better performance that will try to reduce capital costs, improve return on invested capital (ROIC) and weighted average cost of capital (WACC) the difference between make more efficient use of capital, and economic value added increased. Therefore, value-based as the management foundation of the firm, will use EVA as a measure of corporate value creation assessment system to determine the management of funds, long-term financial planning, management 33.

(44) objectives, performance measurement, shareholder communication and incentive pay and other issues. According to scholars of American and British studies that the stock market, EVA is the stock price changes and stock prices and the relationship between the performance measures most closely.. EVA= (Return on Invested Capital – Cost of Capital) × Beginning Total Invested Capital = EBIT(1－Tax)－WACC × (Total assets － Current liabilities). Weighted Average Cost of Capital ＋. ＝. ＋－. ＝. ＋＋. Cost of equity Capital： This study used capital asset pricing model to approach cost of equity. =. ＋ β × (. －. ). Risk-free interest rate is using rate of Taiwan Treasury bill for one year period. Risk interest rate is using Taiwan market rate weighted index from TEJ database.. 34.

(45) β: With the firms since listing to 2004 years until the rate of return on stocks of the Taiwan Stock Exchange on the weighted stock index return of the regression coefficients.. 3.5 Control variable. 3.5.1 Firm size Firm size has long been found to be an important factor affecting firm survival and performance (Porter, 1980). (Hitt et al., 1997) indicated that firm size is associated with economies of scale and, hence, is expected to have a positive association with firm performance. Follow Hitt et al. (1991), this study using number of employees to measure firm size.. 3.5.2 R&D intensity Total R&D expenditures divided by total sales is the most commonly used measure in the studies of R&D intensity (Hambrick and MacMillan, 1985; Baysinger and Hoskisson, 1989).. 35.

(46) Chapter 4: Empirical Results. 4.1 Descriptive statistics. Table 4-1: The number of patent of Taiwanese smart phone industry from. 2003-2008:. Annual. 2003. Number 255 of patent. 2004. 2005. 2006. 2007. 2008. 388. 388. 640. 834. 1,130. Table 4-1 presents the number of patents and its trend from the sample firms of this study during the year 2003 to 2008. The trend of annual patent numbers can be understood from table 4-1, the number increased year by year in Taiwan, such as a patents in 2008 had a 35.49% growth in the number.. Table 4-2: Notice the technological diversification index of Taiwanese smart. phone industry in from 2003-2008:. Annual Patent diversification index. 2003. 2004. 2005. 2006. 2007. 2008. 1.03. 1.09. 1.16. 1.22. 1.27. 1.49. 36.

(47) Table 4-2 and figure 4-2 show the technological diversification index and its trend during the year 2003 to 2008.. Table 4-3: Descriptive statistics: Minimum. Maximum. Mean. Standard deviation. 49. 550,000. 18,180.87. 53,019.71. 0.00. 37.24. 3.79. 4.92. -12.22. 25.90. 0.06. 2.88. -2.31. 35.62. -0.13. 4.46. 0. 4. 1.15. 1.31. -33.31. 47.34. 7.71. 10.72. 0.20. 1.81. 0.71. 0.22. -388,921,660. 2,827,978. -21,462,402.89. 49,073,483.49. -384,515,753.47. 288,678,497.12. -1,143,911.62. 40,254,256.35. The total sum of sample is 282. Table 4-3 presents descriptive statistics including means, standard deviations, maximum, and minimum for all measured variables in this study.. 37.

(48) 4.2 Pearson correlation analysis. Table 4-4 Pearson correlation coefficient analysis: 1. 2. 3. 4. 5. 6. 7. 8. 9. 1 -0.14*. 1. 3.. 0.84**. -0.12*. 1. 4.. 0.85**. -0.12. 0.83**. 1. 5.. 0.01. -0.06. 0.14*. 0.16**. 1. 6.. 0.00. -0.05. -0.02. 0.03. 0.00. 1. 7.. -0.08. -0.14*. -0.04. -0.11. -0.09. -0.63**. 1. 8.. -0.84**. 0.11. -0.81**. -0.94**. -0.13*. -0.11. 0.18**. 1. 9.. -0.03. -0.00. -0.14*. -0.10. -0.01. 0.10. -0.03. 0.11. 1. * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed) N=282 Table 4-4 show all variables in this study using Pearson correlation coefficient matrix. Through the correlation coefficient can understood the correlation and direction with individual variables.. 38.

(49) 4.3 Measurement model Table 4-5 Results of regression analysis (Technological diversification on dependent variable: ROA, Tobin‟s q, MVA, and EVA) Independent. Dependent. Dependent variable:. Dependent variable:. Dependent variable:. variable. variable:. Tobin‟s q. MVA. EVA. ROA Predict sign. Model 1. Model 2. Model 1. Model 2. Model 1. Model 2. Model 1. Model. E(1). E(2). E(5). E(6). E(9). E(10). E(13). 2 E(14). -0.00. -0.00. -0.01 *. -0.01**. -0.84***. -0.84***. -0.03. -0.03. -0.05. -0.05. -0.16***. -0.16**. -0.01. -0.02. -0.01. -0.01. －. Adjust F-value. -0.00. -0.10*. -0.12***. -0.01. 0.00. 0.00. 0.03. 0.04. 0.70. 0.71. 0.00. 0.00. -0.01. -0.01. 0.02. 0.03. 0.70. 0.71. -0.01. -0.01. 0.32. 0.21. 4.31**. 3.83***. 323.01***. 230.81***. 0.09. 0.07. N = 282. *p<0.1; **p<0.05; ***P<0.01 Table 4-5 displays the results of regression analyses regarding the effects of technological diversification on firm performance that using four type of indicator: ROA, Tobin‟s q, MVA, and EVA. Each of Performance indicators has four model: Respectively, Model 1 is the base model that includes two control variables: Firm size and R&D intensity. Models 2 try to capture the direct effect of technological diversification on the dependent variable. Table 4-5 displays the results of the effects of technological diversification and on four types of performance indicator. For ROA, E(1) indicates that the 39.

(50) combination of control variables does not have significant impact on the dependent variable (F =0.32,. =0.00). It show that the control variable don‟t have directly. effect on ROA. E(2) does not have significant and can explain an additional 0.2% of variance over what the control variables alone explain. The coefficient of technological diversification is negative and not significant. For Tobin‟s q term, E(5) shows that the combination of control variables has significant impact on the dependent variable (F =4.31, P<0.05,. =0.03). It show that the control variable. have directly effect on Tobin‟s q. E(6) and have significant and can explain an additional 1% (F =3.83, P<0.01) of variance over what the control variables alone explain. The coefficient of technological diversification is negative (0.1, P<0.1) and significant. The finding indicate that technological diversification has an negative relationship with firm's performance, the results in Tobin‟s q terms support Hypothesis 1. For MVA terms, E(9) indicates that the combination of control variables have significant impact on the dependent variable (F =323.01, P<0.01. =0.70). It show. that the control variable have directly effect on MVA. E(10) have significant and can explain an additional 1.5% (F =230.81, P<0.01) of variance over what the control variables alone explain. The coefficient of technological diversification is negative (-0.12, P<0.01) and significant. The finding indicate that technological diversification has an negative relationship with firm performance, the results was support 40.

(51) Hypothesis 1. For EVA term, E(13) indicates that the combination of control variables does not have significant impact on the dependent variable (F =0.09,. =0.001). It. show that the control variable don‟t have directly effect on EVA. E(14) does not have significant and can explain equal to what the control variables alone explain. The coefficient of technological diversification is negative and not significant. Followed Table 4-5, the second part, this study examines the contingent role of organizational slack between technological diversification and firm performance. Model 4 adds the two dimensions of organizational slack: absorbed slack and unabsorbed slack and their two interaction terms with the technological diversification dimension. All the results was shown from table 4-6 to 4-9. Table 4-6: Results of regression analysis (Dependent variable: ROA) Independent variable Dependent variable: ROA Predict sign. Model 1 E(1). Model 2 E(2). -0.00 -0.05. -0.00 -0.05 -0.00. -0.00 -0.04 -0.15 0.16. -0.22 -0.05 -0.25 0.24 -0.05 0.34 -0.07 -0.09. 0.00. 0.00. 0.00. 0.02. -0.01 0.32. -0.01 0.21. -0.01 0.07. -0.01 0.64. － + + － * *. Model 3 Model 4 E(3) E(4). + －. Adjust F-value N = 282 *p<0.1; **p<0.05; ***P<0.01. 41.

(52) In the term with the dependent variable of ROA, table 4-6 displays the results of the effects of technological diversification and organizational slack on ROA. The E(4) is not significant (F =0.64,. =0.02) and explains an additional 1.6 percent. of variance over what the control variables alone explain. The coefficient of the interaction term between technological diversification and absorbed slack in Model 4 is negatively signed and not significant. The absorbed interaction term was inconsistent with the predict positive signed. The coefficient of the interaction term between technological diversification and unabsorbed slack is negatively signed and not significant.. Table 4-7: Results of regression analysis (Dependent variable: Tobin’s q) Independent variable Dependent variable: Tobin‟s q Predict sign. Model 1 E(5). Model 2 E(6). Model 3 Model 4 E(7) E(8). -0.01 * -0.16 ***. -0.01** -0.16** -0.10*. -0.10* -0.15** -0.41 0.32. -0.20 -0.15** -0.37 0.26 0.08 0.04 0.16 -0.20. 0.03 0.02. 0.04 0.03. 0.05 0.03. 0.06 0.03. 4.31 **. 3.83***. 3.26**. 2.24**. － + + － * *. + －. Adjust F-value N = 282 *p<0.1; **p<0.05; ***P<0.01. 42.

(53) In the term with the dependent variable of Tobin‟s q, table 4-7 displays the results of the effects of technological diversification and organizational slack on Tobin‟s q. The E(8) is significant (F =2.24, P<0.05,. =0.06) and explains an. additional 3.2 percent of variance over what the control variables alone explain. The coefficient of the interaction term between technological diversification and absorbed slack in E(8) is positively signed and not significant. The coefficient of the interaction term between technological diversification and unabsorbed slack is negatively signed and not significant. Although E(8) with two dimensions neither significant, but the sign are consistent with the original predict as Hypothesis 2 and Hypothesis3.. Table 4-8: Results of regression analysis (Dependent variable: MVA) Independent Variable. Dependent variable: MVA Predict sign. Model 1 E(9). Model 2 E(10). Model 3 E11). Model 4 E(12). -0.84*** -0.01. -0.84*** -0.02 -0.12***. -0.84*** -0.02 -0.11 -0.02. -0.28*** -0.00 -0.02 0.01 -0.11** -0.54*** 0.12*** -0.21***. 0.70 0.70. 0.71 0.71. 0.71 0.71. 0.90 0.89. 323.01***. 230.81***. 172.50** *. 279.52***. － + + － * *. + －. Adjust F-value N = 282 *p<0.1; **p<0.05; ***P<0.01. 43.

(54) In the term with the dependent variable of MVA, table 4-8 displays the results of the effects of technological diversification and organizational slack on MVA. The E(12) is significant (F =279.52,. =0.89) and explains an additional 19.3. percent of variance over what the control variables alone explain. The coefficient of the interaction term between technological diversification and absorbed slack in E(12) is positively signed (0.12, P<0.01) and significant. The coefficient of the interaction term between technological diversification and unabsorbed slack is negatively signed (-0.21, P<0.01) and significant. These findings support Hypothesis 2 and Hypothesis 3 that absorbed slack positively moderates while unabsorbed slack negatively moderates the effect of technological diversification on firm performance. Table 4-9: Results of regression analysis (Dependent variable: EVA) Independent Variable. Dependent variable: EVA Predict sign. Model 1 E(13). Model 2 E(14). Model 3 E(15). Model 4 E(16). -0.03 -0.01. -0.03 -0.01. -0.03 -0.00. 0.38* 0.03. -0.01. -0.11 0.10. -0.01 0.02 -0.80*** 0.27 0.59*** -0.46***. 0.00. 0.00. 0.00. 0.14. -0.01 0.09. -0.01 0.07. -0.01 0.09. 0.11 5.47***. － + + － * *. + －. Adjust F-value N = 282 *p<0.1; **p<0.05; ***P<0.01. 44.

(55) In the term with the dependent variable of EVA, table 4-9 displays the results of the effects of technological diversification and organizational slack on EVA. The E(16) is significant (F =5.47,. =0.14) and explains an additional 13.9 percent of. variance over what the control variables alone explain. The coefficient of the interaction term between technological diversification and absorbed slack in E(16)is positively signed (0.59, P<0.001) and significant. The coefficient of the interaction term between technological diversification and unabsorbed slack is negatively signed (-0.46, P<0.001) and significant. These findings support Hypothesis 2 and Hypothesis 3 that absorbed slack positively moderates while unabsorbed slack negatively moderates the effect of technological diversification on firm performance. Table 4-10: Result of hypothesis testing: Dependent Hypothesis Coefficient t-value variable. Conclusion. ROA. Tobin‟s q. MVA. EVA. H1. -0.00. -0.04. Insignificant. H2. -0.07. -0.49. Insignificant. H3. -0.09. -0.46. Insignificant. H1. -0.01*. -1.67. Significant. H2. 0.16. 1.25. Insignificant. H3. -0.20. -1.02. Insignificant. H1. -0.12***. -3.83. Significant. H2. 0.12***. 2.74. Significant. H3. -0.208***. -3.16. Significant. H1. -0.01. -0.14. Insignificant. H2. 0.12***. 4.70. Significant. H3. -0.21***. -2.46. Significant. *p<0.1; **p<0.05; ***P<0.01 45.

(56) All the results was organized into a simple form as table 4-10. Overall, four performance indicators including ROA, Tobin's q, MVA, and EVA. In addition to traditional accounting indicators ROA, the technological diversification and organization slack dimension to the new economy has a certain degree of reflection. The first step in testing Hypothesis 1: Technology Diversification has a negative impact on performance. Tobin's q and the MVA is negative and significant. Though other two performance indicator ROA and EVA was not significant, but also negative signed to the same direction, generally in support of hypothesis 1. The second step was testing the moderating role of organization slack between technological diversification and firm performance. This study using Hypothesis 2 and Hypothesis 3 that absorbed slack positively moderates while unabsorbed slack negatively moderates the effect of technological diversification on firm performance. Except to ROA and Tobin's q is not significant, the absorbed slack and unabsorbed slack of MVA and EVA were positive and negative as significant. Although the ROA and Tobin's q was not significant, but except to the unabsorbed ROA interaction is inconsistent with the predict negative signed, the other signed was consistent with previous predict. The result was general support of the Hypothesis 2 and 3.. 46.

(57) Table 4-11 Results of regression analysis (Technological diversification squared term on dependent variable: ROA, Tobin‟s q, MVA, and EVA). Independent variable Predict sign. －＋. Adjust F-value. Dependent variable: ROA. Dependent variable: Tobin‟s q. Dependent variable: MVA. Dependent variable: EVA. Model 3 E(3). Model 3 E(7). Model 3 E(11). Model 3 E(15). -0.00 -0.04 -0.15 0.16. -0.10* -0.15** -0.41 0.32. -0.84*** -0.02 -0.11 -0.02. -0.03 -0.00 -0.11 0.10. 0.00 -0.01 0.07. 0.05 0.03 3.26**. 0.71 0.71 172.50***. 0.00 -0.01 0.09. N = 282. The additional step in testing technological diversification and firm performance linear relationship, does there exist nonlinear relationship by including the linear term of technological diversification in Model 2 and adding the squared term in Model 3 to alternate the maximum of technological diversification. The result shows in table 4-11. Although the four performance indicators in the technological diversification squared term was not significant, but in addition to MVA was positive signed. ROA, Tobin's q , and EVA are respectively positive, with a U-shaped trend, may also be used as a research reference.. 47.

(58) 4.4 Discussion and implication First, results of hypothesis testing confirm the existence of a significant and negative relationship between technological diversification and firm performance in terms of Tobin‟s q and MVA, but not of ROA and EVA. This insignificant relationship implies ROA and EVA may not be an appropriate indicator to measure firm performance of smart phone industry for investigating the relationship between technological diversification and firm performance. This is a new finding in the literature. One possible explanation for this finding is that although the current evidence shows no effects of technological diversity on accounting profitability such as ROA and economic value added EVA, diversification in technology development strategy can‟t achieve better firm performance that has observed and recognized by the financial markets. In addition, highly engineered and technologically advanced machines and equipments upgraded by Taiwanese smart phone firms have built the image and impression of specialized manufacturing in investor‟s minds since the 2000s. The managerial implications of the findings are that managers should understand their technological diversity strategy should be carefully implemented due to no effects on accounting profitability and negative impacts on financial markets. Second, the results show and focused on their core business to enhance competitive (Chen, 2010). That organizational slack of a company moderates the 48.

(59) relationship between technological diversification and firm performance in terms of EVA and MVA, but not of ROA and Tobin‟s q. This insignificant relationship implies ROA and Tobin‟s q may not be an appropriate measure to investigate the moderating role of organizational slack on the relationship between technological diversity and firm performance. In terms of EVA and MVA, while absorbed slack has a positive moderating role between technological diversity and firm performance, unabsorbed slack has a negative moderating role between technological diversity and firm performance. Since the negative effect of technological diversity on firm performance exists in this study, absorbed slack is more beneficial to firm performance when Taiwanese smart phone OEM-oriented and specialized manufacturers choose a lower degree of technological diversity approach, and unabsorbed slack is more helpful to firm performance when Taiwanese firms adopt a higher degree of technological diversity approach. Traditionally, most of Taiwanese firms understand that it is difficult to invest more resources on the development of technological capacity for OEM manufacturers because of structural characteristics of IT industry such as fast product life cycle, dynamic competitive rivalry and specialized processes. However, the current findings indicate that Taiwanese smart phone firms with different technological diversity approach could utilize different slack resources to improve their firm performance. 49.

(60) Third, the findings of this study contribute to the theoretical development of a conceptual model. for. investigating the relationships. among. technological. diversification, organizational slack, and firm performance. Due to the structural characteristics of Taiwanese smart phone industry, the current results, consistent with the findings of previous studies (e.g., Argyres, 1996; Lin and Chen, 2005; Garcia-Vega, 2006; Lin et al., 2006; Huang and Chen, 2010), suggests the negative effect of technological diversity on firm performance, and proposes that excess technological diversification would have detriment impacts on firm performance from the perspective of core competence. Fourth, this study provides evidence for the application of organization theory and agency theory. While organization theory typically posits that organizational slack buffers a firm‟s technological core from environmental turbulence and thus has a positive impact on firm performance (Cyert and March, 1963; Thompson, 1967; Pfeffer and Salancick, 1978), agency theory argues that slack is a source of agency problems and thus breeds in efficiency and hurts performance (Jensen and Meckling, 1976; Fama, 1980). Based on different types of slack resources, the current results of different moderating role of organizational slacks in affecting firm performance support the predictions of organizational theory and agency theory. Empirically, by applying the contingency approach, the current finding is also consistent with the 50.

(61) suggestions of Tan and Peng (2003), Voss et al. (2008), and Huang and Chen (2010). Finally, based on the weakness of accounting profitability ROA, this study using firm-level return Tobin‟s q and economic profitability EVA and MVA as performance measures tests the validity of the framework and hypothesis in Taiwanese smart phone industry. The results confirm the argument of previous studies that examining what drives value creation, and ROA is not an appropriate measure to test the framework and the industry similar to this study.. 51.

(62) Chapter 5: Conclusion and Limitation. 5.1 Conclusion This study explores the effect of corporate technological diversification on firm performance with a particular interest in investigating the moderating roles of organizational slacks, absorbed slack and unabsorbed slack. In the technology management and corporate strategy fields, recent researchers have interested in understanding the relationship of technological diversification and performance. However, many previous studies have been made on innovation performance rather than firm performance (e.g., Huang and Chen, 2010). Thus, by using four types of firm performance measures, the present study examines the effect of technological diversification on firm performance with incorporating the moderating effects of absorbed and unabsorbed slacks for Taiwanese smart phone industry, providing new findings for the literature and offers support to some existing findings for technological management and corporate strategy in industrial manufacturing settings. This study is contributed to that Taiwanese smart phone industry firms with its OEM-oriented nature, thus don‟t invest in widely technological portfolio. In addition, the result also support our prediction that when firms being specialization would have better firm performance. This case maybe a consultation for 52.