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SECTION 2.4 DISCUSSION

I have replicated Deephouse’s (1999) study by examining the relationship between strategic similarity and performance across 155 manufacturing industries in China. This study has four major findings as follows. First, strategic balance perspective has limitations with regarding to contexts and exploring the boundary conditions is critical for knowledge accumulation and theory development. Second, the presence of multiple reference points in an industry potentially causes the divergent patterns of strategic similarity-performance relationship. Third, some industry characteristics may trigger specific pattern of strategic similarity-performance relationship. Forth, for investigating strategic balance perspective, Driscoll-Kraay estimation is a more reliable analysis than WLS estimation while standard deviation is a proper measure for strategic similarity.

Although 69 of the 155 manufacturing industries provided supportive evidence for strategic balance perspective, 86 of them do not show consistent results and 45 of them suggest that being similar or being different is irrelevant to firm performance.

Given that balance is not always the best method for addressing competing demands from social norms and competition, it should be careful about the contexts to apply strategic balance perspective to a variety of contexts, especially not in strategy fields.

Understanding the boundary of a theory is important (Tsang and Kwan, 1999). This study suggests that higher level of competition is one of the potential boundary conditions. Low degrees of industry concentration and large number of competitors reflect fierce competition and the necessity for firms to balance the tension between competitive and institutional forces. Future studies on strategic balance perspective should pay more attention on research contexts, especially for competition condition.

Deephouse (1999, p. 153) has emphasized the importance of contexts and notes that

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‘the ultimate relationship between strategic similarity and performance then depends on the relative strength of the differentiation and conformity propositions over the range of strategic similarity,’ competitive pressures and institutional pressures are two potential determinants of strategic balance. The influences of competitive pressures and institutional pressures on the scope and limits of strategic balance perspective are worth for future research.

In reality, most industries consist of multiple reference points and the findings of this study implicitly support this idea. I found that strategic balance is verified in industries with lower ratio of foreign firms, whereas diverse patterns are presented in industries with large proportion of foreign firms. Foreign firms are distinguished from local firms in international business literature due to their competitive advantage and lack of local legitimacy. If foreign firms develop their own social norms different from local firms, another reference point is emerged. In this situation, operationalization of strategic similarity based on average strategies of the whole industry is not accurate unless the two reference points for foreign and local firms are extremely close. Otherwise, incorrect operationalization only leads biased and unreliable results. Similarly, industries with larger market size may compete nationwide and develop industry norms prevailing in the whole country, while industries with smaller market size probably engage in regionalized competition and develop reference points regionally. Moreover, firms with different size are often categorized in different strategic groups. One possible reason why the patterns of strategic similarity-performance relationship are similar across SMEs and non-SMEs is that SMEs and non-SMEs belong to different strategic groups and have different reference points. How firms facing multiple reference points decide to be similar or to be different is still unexplored and it is important for theory development of strategic

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balance perspective.

Some industry characteristics may trigger specific pattern of strategic similarity-performance. For example, Cennamo and Santalo (2013), studying the U.S.

video game industry, found that the relationship between distinctive positioning (the extent to which a platform’s game portfolio is dissimilar to rivals’ portfolios) and performance (i.e., market share) is U-shaped. Platform markets are usually described as ‘winner takes all’ (triggered by network externalities) where firms are encouraged to embrace aggressive strategies and compete head-to-head with conformity strategies to win the leading position of the mass market. Platform differentiation and dominance in a new market niche is only possible with a high level of dissimilarity.

They found that both low and high levels of dissimilarity increase performance and moderate dissimilarity decreases performance. In this study, I found that being similar is beneficial for firms in industries with high ratio of export. One possible explanation is that firms in these industries face strong expectations, requirements, and regulations from worldwide customers and being able to meet their expectations and requirements is the ticket for firms to export and obtain higher performance.

Driscoll-Kraay estimation is a more reliable analysis than WLS estimation for investigating strategic balance perspective, since strategic balance perspective predicts that too similar and too different strategic positions, relative to peers, damages firm performance. Social learning or herd behavior across firms affects the strategic position of the focal firm and the strategies of other firms, causing substantial spatial correlations in the disturbances of the measures of relatively strategic positions. Most studies applied the strategic balance perspective employ a relative measures of the key independent variables explaining organizational outcomes. For example, Oerlemans and Meeus (2005) adopted the absolute distance

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between the focal firm’s innovating strategies to competitors’ average innovating strategies as a proxy of innovation strategy deviation. Similarly, Norman et al., (2007) employed Euclidean distance between the focal firm’s non-conformity actions to competitors’ average non-conformity actions as a proxy of competitive non-conformity. The relative measures of key independent variables may lead to substantial spatial dependence in the analysis and bias the results. To obtain reliable results, researchers investigating strategic balance should conduct Driscoll-Kraay estimation to eliminate the threat of spatial dependence.

Although absolute deviation and Euclidean distance are both frequently employed as the measurement of strategic similarity, no research has attempted to investigate which operationalization fits the mental model of stakeholders to evaluate firms’ behaviors better. A preliminary comparison in this study suggests that absolute deviation is a proper measurement, but the results are sensitive to different operationalizations. Future studies may systemically investigate how stakeholders judge firms behaviors and what operationalization of strategic similarity fits theoretical arguments.

Several limitations and challenges in this study reveal the need for further replications. First, I used a broader definition of competitors than that used by Deephouse. Although I demonstrated that significant relationship between strategic similarity and firm performance in a majority of manufacturing industries in China, the broader definition of competitors may have resulted in an underestimation of the effect sizes of strategic deviation and its squared term. This introduces the risk of a type II error. Second, the dataset only contained manufacturing firms in China, so I was unable to compare the results with those for commercial banks in China or manufacturing firms in the U.S. to examine industry and country differences. These

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two limitations demonstrate the need to systematically conduct replication studies (Hubbard et al., 1998) to test the internal and external validity of the relationships between strategic similarity and performance.

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Table 2-1 Descriptive Statistics and Correlations

Means S.D. 1. 2. 3. 4. 5. 6 7 8 9 10

1.Relative ROA 0.000 1.00

2. Relative ROAt-1 0.000 1.00 0.63***

3.Strategic

Deviation² 13.086 23.738 -0.08*** -0.07***

4. Strategic

Deviation 3.071 1.912 -0.05*** -0.04*** 0.93***

5.Market Share 0.002 0.008 0.05*** 0.05*** -0.02*** -0.01***

6.Total Expense

Ratio 0.941 0.094 -0.35*** -0.26*** 0.08*** 0.08*** -0.04***

7.Market Growth 0.209 0.086 0.00 -0.00 -0.00 0.00 0.03*** -0.02***

8. Financial Slack^a 0.005 0.106 0.04*** 0.05*** -0.02*** -0.01*** 0.12*** -0.05*** -0.00*

9.Firm Size 5.037 1.054 -0.01*** -0.01*** 0.00 -0.02*** 0.30*** 0.01** -0.09*** 0.04***

10. Firm Age 9.631 7.793 -0.05*** -0.05*** 0.04*** 0.04*** 0.05*** -0.00* -0.01*** 0.00* 0.14***

n = 226,946 * p < 0.05, ** p < 0.01, *** p < 0.001 a. Financial slack = working capital/10⁶

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Table 2-2 Results of Exact Replication Regression Analysis (WLS Estimation)

Deephouse

Ind16 Ind17 Ind18 Ind19 Ind20 Ind21 Ind22 Ind23 Ind24 Ind25 Ind26 Ind27 Ind28 Ind29 Ind30 Ind31 Ind32

Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-2-continued Results of Exact Replication Regression Analysis (WLS Estimation)

Ind33 Ind34 Ind35 Ind36 Ind37 Ind38 Ind39 Ind40 Ind41 Ind42 Ind43 Ind44 Ind45 Ind46 Ind47 Ind48 Ind49

Strategic

Ind50 Ind51 Ind52 Ind53 Ind54 Ind55 Ind56 Ind57 Ind58 Ind59 Ind60 Ind61 Ind62 Ind63 Ind64 Ind65 Ind66

Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-2-continued Results of Exact Replication Regression Analysis (WLS Estimation)

Ind67 Ind68 Ind69 Ind70 Ind71 Ind72 Ind73 Ind74 Ind75 Ind76 Ind77 Ind78 Ind79 Ind80 Ind81 Ind82 Ind83

Strategic

Ind84 Ind85 Ind86 Ind87 Ind88 Ind89 Ind90 Ind91 Ind92 Ind93 Ind94 Ind95 Ind96 Ind97 Ind98 Ind99 Ind100

Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-2-continued Results of Exact Replication Regression Analysis (WLS Estimation)

Ind101 Ind102 Ind103 Ind104 Ind105 Ind106 Ind107 Ind108 Ind109 Ind110 Ind111 Ind112 Ind113 Ind114 Ind115 Ind116 Ind117 Strategic

Ind118 Ind119 Ind120 Ind121 Ind122 Ind123 Ind124 Ind125 Ind126 Ind127 Ind128 Ind129 Ind130 Ind131 Ind132 Ind133 Ind134 Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-2-continued Results of Exact Replication Regression Analysis (WLS Estimation)

Ind135 Ind136 Ind137 Ind138 Ind139 Ind140 Ind141 Ind142 Ind143 Ind144 Ind145 Ind146 Ind147 Ind148 Ind149 Ind150 Ind151 Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-3 Results of Improved Replication Regression Analysis (D&K Estimation)

Deephouse

Ind16 Ind17 Ind18 Ind19 Ind20 Ind21 Ind22 Ind23 Ind24 Ind25 Ind26 Ind27 Ind28 Ind29 Ind30 Ind31 Ind32

Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-3-continued Results of Improved Replication Regression Analysis (D&K Estimation)

Ind33 Ind34 Ind35 Ind36 Ind37 Ind38 Ind39 Ind40 Ind41 Ind42 Ind43 Ind44 Ind45 Ind46 Ind47 Ind48 Ind49

Strategic

Deviation² -0.008* 0.005 -0.014*** -0.007*** -0.008** 0.019 -0.008** -0.011*** -0.015*** -0.13*** 0.008 -0.015* 0.009 -0.005 0.021 0.025*** -0.008*

Strategic

Ind50 Ind51 Ind52 Ind53 Ind54 Ind55 Ind56 Ind57 Ind58 Ind59 Ind60 Ind61 Ind62 Ind63 Ind64 Ind65 Ind66

Strategic

Deviation² -0.019*** -0.015*** -0.021* -0.007*** -0.009** 0.000 -0.006 -0.027*** -0.012* -0.040*** -0.023** -0.085*** -0.006 -0.024* 0.058* -0.023*** -0.009 Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-3-continued Results of Improved Replication Regression Analysis (D&K Estimation)

Ind67 Ind68 Ind69 Ind70 Ind71 Ind72 Ind73 Ind74 Ind75 Ind76 Ind77 Ind78 Ind79 Ind80 Ind81 Ind82 Ind83

Strategic

Ind84 Ind85 Ind86 Ind87 Ind88 Ind89 Ind90 Ind91 Ind92 Ind93 Ind94 Ind95 Ind96 Ind97 Ind98 Ind99 Ind100

Strategic

Deviation² -0.010*** -0.019** -0.009 -0.012*** -0.019*** -0.058*** -0.038* -0.004 0.002 -0.010 -0.048 -0.005 0.004 -0.005*** -0.013*** -0.004 -0.031***

Strategic

Deviation 0.029*** 0.087** 0.028 0.057*** 0.097*** 0.237*** 0.194 0.029* 0.019 -0.015 -0.250** 0.006 -0.050 -0.010 0.066*** -0.046* 0.111***

R² 0.082 0.111 0.125 0.159 0.165 0.211 0.172 0.054 0.194 0.101 0.204 0.206 0.170 0.060 0.087 0.077 0.099

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-3-continued Results of Improved Replication Regression Analysis (D&K Estimation)

Ind101 Ind102 Ind103 Ind104 Ind105 Ind106 Ind107 Ind108 Ind109 Ind110 Ind111 Ind112 Ind113 Ind114 Ind115 Ind116 Ind117 Strategic

Deviation² -0.034* 0.007 0.003 -0.059 -0.001 -0.027** -0.011 0.012** -0.007 -0.015*** -0.011* -0.007* -0.003 -0.007*** -0.019*** -0.012*** -0.012 Strategic

Ind118 Ind119 Ind120 Ind121 Ind122 Ind123 Ind124 Ind125 Ind126 Ind127 Ind128 Ind129 Ind130 Ind131 Ind132 Ind133 Ind134 Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-3-continued Results of Improved Replication Regression Analysis (D&K Estimation)

Ind135 Ind136 Ind137 Ind138 Ind139 Ind140 Ind141 Ind142 Ind143 Ind144 Ind145 Ind146 Ind147 Ind148 Ind149 Ind150 Ind151 Strategic

N/A means that the turning point is not in the range of strategic deviation.

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Table 2-4 The Comparison of the Patterns of the Strategic

Deviation-Performance Relationship Between WLS and D&K Estimations (With

and Without Additional Controls)

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Table 2-5 The Comparison of the Patterns of the Strategic

Deviation-Performance Relationship between SD3 and ESD (with WLS and D&K Estimations), and between SMEs and Non-SMEs

Pattern of the

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Table 2-6 The Comparison of Industry Characteristics between Different Patterns of Strategic sSimilarity-Performance Relationship Pattern of the relationship

Comparison Industry-level

Variables

1. No relationship

2. Negative linearity

3. Positive linearity

4. Inverted-U shape

5. U shape 6. Increasing concave-down Industry

Concentration 0.016 0.015 0.014 0.010 0.018 0.012 1>4

Market Size

^a

18.94 22.94 22.82 33.74 14.08 17.54 4>1, 5<4, 6<4

Average ROA 0.08 0.08 0.08 0.08 0.09 0.08

Market Growth 0.22 0.21 0.22 0.22 0.22 0.20

Average Firm

Age 9.97 8.76 10.90 9.72 10.44 9.02 3>2

Number of Firms 314.04 306.63 407.64 444.34 185.11 313.50 4>1, 5<4

Ratio of Foreign

Firms 0.31 0.39 0.26 0.26 0.34 0.34 3<2. 4<1

Market

Uncertainty

^b

5.64 11.16 11.80 12.46 3.43 3.93 3>1, 4>1, 5<3,

5<4, 6<3, 6<4

Ratio of Export 0.25 0.42 0.25 0.20 0.20 0.33 2>1, 3<2, 4<2,

5<2, 6>4 Ratio of New

Product Sales 0.09 0.12 0.12 0.08 0.10 0.10

N 192 32 44 272 36 40

a

Market size = market size/10

9 b

Market uncertainty = standard variance of market size/10

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Table 2-7 Descriptive Statistics and Correlations between Industry-Level Variables and Main Variables

Means S.D. 1. 2. 3. 4 5 6 7 8 9 10 11

1.Relative ROA 0.000 0.981 2. Strategic

Deviation 3.071 1.912 -0.05***

3. Industry

Concentration 0.007 0.009 0.00 0.02***

4. Market Size^a 30.675 28.781 0.00 0.01** -0.07***

5. Average ROA 0.082 0.024 0.01*** -0.01*** -0.07*** 0.10***

6. Market Growth 0.209 0.086 0.00 0.00 0.09*** 0.06*** 0.26***

7. Average Firm Age 9.678 1.786 -0.01*** 0.03*** 0.04*** 0.04*** 0.15*** -0.03***

8. Number of Firms 905 820 0.00 -0.03*** -0.36*** -0.28*** -0.16*** -0.17*** -0.13***

9. Ratio of Foreign

Firms 0.286 0.150 -0.00 -0.00 0.07*** -0.22*** -0.22*** -0.31*** -0.25*** 0.11***

10. Market

Uncertainty^b 14.792 16.050 0.00 -0.01*** -0.13*** 0.29*** 0.11*** 0.14*** 0.08*** 0.02*** -0.22***

11. Ratio of Export 0.242 0.184 -0.00 -0.01*** -0.00 -0.20*** -0.20*** -0.30*** -0.25*** 0.14*** 0.85*** -0.17***

12. Ratio of New

Product Sales 0.086 0.081 -0.01* 0.02* 0.40*** -0.16*** -0.19*** 0.05*** 0.25*** -0.09*** 0.06*** -0.14*** 0.03***

a Market size = market size/109 b Market uncertainty = standard variance of market size/10⁹ * p < 0.05, ** p < 0.01, *** p < 0.001

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APPENDIX A: LIST OF INDUSTRIES

1. Construction sand and gravel mining 2. Grain mills

3. Prepared feeds 4. Vegetable oil mills 5. Sugar

6. Slaughtering and meat processing 7. Aquatic products processing

8. Vegetables, fruits, and nuts processing 9. Other grain mill products

10. Bakery Products 11. Confectionery Products

12. Instant food and related products 13. Dairy Products

14. Canned products

15. Seasoning and Dressing Manufacturing 16. Other food manufacturing

17. Breweries and Wineries 18. Soft Drinks

19. Tea refining and processing 20. Broadwoven Fabric Mills, Cotton 21. Broadwoven Fabric Mills, Wool

22. Broadwoven fabrics: linen, jute, hemp, and ramie

23. Broadwoven Fabric Mills, Silk 24. Textile Furnishings Mills

28. Miscellaneous Apparel and Accessories 29. Leather Tanning and processing 30. Leather and Leather Products 31. Fur Tanning and processing 32. Fur goods

33. Millwork, Veneer, Plywood, and Structural Wood

34. Lumber and Wood Products

35. Bamboo, rattan, palm, grass products 36. Furniture, wood

37. Furniture, metal 38. Other furniture 39. Paper Mills

40. Paper and Allied Products 41. Printing

42. Support Activities for Printing

43. Pens, Pencils, and other Artists Materials 44. Sporting and Athletic Goods

45. Musical Instruments 46. Dolls and Toys 47. Games

48. Petroleum Refining 49. Coal Coking

50. Basic Chemicals and Allied Products 51. Agricultural Chemicals, Fertilizers 52. Agricultural Chemicals, Pesticides

53. Paints, Varnishes, Lacquers, Enamels, and Allied Products

54. Plastics Materials and Synthetic Resins, Synthetic Rubber

55. Specialty Chemicals

56. Soap, Detergents, and Cleaning Preparations;

Perfumes, Cosmetics, and Other Toilet Preparations

57. Medicinal chemicals, bulk

58. Medicinal chemicals manufacturing 59. Botanical Products processing 60. Botanical Products manufacturing 61. Animal medicine Manufacturing 62. Biological Products

63. Pharmaceutical Preparations 64. Cellulosic Manmade Fibers 65. Synthetic fiber

66. Tire Manufacturing

‧ 67. Rubber plates, tubes, and tapes

68. Gaskets, Packing, and Sealing Devices and Rubber

69. Recycling rubbers

70. Necessary and medical rubbers 71. Rubber footwear

72. Miscellaneous rubber Products 73. Plastics Film and Sheet

74. Plastics Plate, Pipe, and Profile Shapes 75. Plastics string and Fabrics

76. Plastics Foam Products 77. Plastics leathers 78. Plastics containers 79. Plastics Components 80. Plastics Necessaries

81. Miscellaneous Plastics Products

82. Concrete, Gypsum, and Plaster manufacturing

83. Concrete, Gypsum, and Plaster Products 84. Brick and Structural Clay Tile

85. Glass and Glassware 86. Pottery and Related Products 87. Refractories

88. Graphite and Miscellaneous Nonmetallic Mineral Products

94. Gold, Silver, and other Platinum refining 95. Rare Earth Metals refining

96. Nonferrous alloys 97. Nonferrous Foundries 98. Fabricated Structural Metal 99. Metal tools

100. Metal Cans and Shipping Containers

101. Metal string and allied products

102. Architectural and Structural Metals Manufacturing

103. Plating and Polishing 104. Enameling

105. Stainless necessaries and allied products 106. Miscellaneous Fabricated Metal Products 107. Engines and Turbines

108. Metalworking Machinery and Equipment 109. Hoists, Cranes, and Monorails

110. Pumps and Pumping Equipment 111. Bearings, Drives, and Gears 112. Blowers and Fans

113. Machine Tools

114. Metal casting and forging 115. Mining Machinery 116. Woodworking Machinery 117. Food Products Machinery

118. Printing Trades Machinery and Equipment 119. Textile Machinery

120. Electronic and engineer machinery 121. Farm Machinery and Equipment 122. Medical machinery and Equipment

123. Environmental protection and Miscellaneous Special Industry Machinery

128. Ship and Boat Building and Repairing 129. Aircraft and Parts

130. Motors and Generators

131. Power, Distribution and Specialty Transformers

132. Wiring Devices 133. Battery

134. Electrical Household Appliances

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136. Lighting equipment

137. Miscellaneous Electrical Machinery, Equipment, and Supplies

138. Communications Equipment

139. Radio and Television Broadcasting and Communications Equipment

140. Computers manufacturing 141. Electronic devices 142. Electronic Components

143. Radio and Television. Communications Equipment

144. Miscellaneous Communications Equipment 145. Laboratory Apparatus and Analytical,

Optical, Measuring, and Controlling Instruments

146. Special industry Apparatus and Instruments 147. Watches, Clocks, Clockwork Operated

Devices, and Parts

148. Optical and Ophthalmic Goods 149. Office equipment and supplies

150. Artwork, Jewelry, Silverware, and Plated 151. Necessaries

152. Miscellaneous Manufacturing 153. Nonmetallic scrap processing 154. Electric generation

155. Gas generation and distribution 156. Salt mining*

157. Furniture, plastic*

158. Pulp Mills*

159. Industrial Furnaces and Ovens*

* These four industries were not included in individual analysis due to the number of observations was

too few to conduct individual analysis

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CHAPTER 3 THE TENSION BETWEEN LEGITIMACY AND

在文檔中 策略相似性與廠商績效：正當性與競爭之權衡 - 政大學術集成 (頁 46-69)