Identifying new product successes in China

J PROD INNOV MANAG 1994;11:15-30

15

0000

Identifying New Product Successes in China Mark E. Parry and X. Michael Song

To examine the genera&ability of the work of Cooper regarding the correlates of new product success and failure, Mark Parry and Michael Song surveyed new product development managers at 129 state-owned enterprises in the People’s Republic of China. Their analysis of 2.58 reported product successes and failures indicated that relative product advantage and the acquisition of marketing information were highly correlated with new product success, just as in Canada. In addition, several factors not significantly correlated with success in CanadianJirms emerged as significant correlates of success in the PRC. These included the level of competitive activity, the timing of the product launch, and the level of projiciency in executing activities in the early stages of the product development process.

Address correspondence to Mark E. Parry, Ph.D., The Colgate Darden Graduate School of Business Administration, University of Virginia, Box 6550, Charlottesville, VA 22%.

0 1994 Elsevier Science Inc. 655 Avenue of the Americas. New York, NY 10010

Introduction More than ten years ago, Cooper [4] compared 102 successful and ninety-three unsuccessful industrial products introduced by 103 Canadian firms. He concluded the study by identifying the fifteen most important variables distinguishing successes and failures in his sample. These included (1) the proficiency of the new product launch, (2) the product-customer fit relative to competitive products, and (3) the relative quality of the product. Later studies by Cooper [5,6] and Cooper and Kleinschmidt [7-lo] have expanded on Cooper’s earlier work, and two recent studies have extended the work of Cooper and Kleinschmidt to Australia [l l] and Spain [24]. In this article, we extend Cooper’s analysis to the People’s Republic of China [hereafter denoted as the PRC]. This extension is important for two reasons. The first reason is “China’s formidable economic potential,” as evidenced by the southern province of Guangdong [30]. According to The Economist, Guangdong’s population exceeds that of every European country except the Federal Republic of Germany. From 1980-1990, Guangdong’s “gross value of industrial and agricultural output” (GDP) measured in constant dollars grew 12.5% per year. During this time, Guangdong’s annual growth rate exceeded that of Thailand by 66% percent [28].* Thus Fortune has described Guangdong’s growth as “a stunning accomplishment unmatched by Japan, South Korea, Taiwan, or Asia’s other tigers during similar stages of

’ This Chinese version of GDP omits services.

0737-6782/94$7.00

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M. E. PARRY AND X. M. SONG

J PROD INNOV MANAG 1994; I I: 15-30

BIOGRAPHICAL

SKETCHES

X. Michael Song is Assistant Professor of Marketing in the College of Business Administration at the University of Tennessee. He holds a B.S. in mathematics from Jinan University in the Peoples’s Republic of China, an M.S. in statistics from Cornell University, and an M.B.A. and a Ph.D in Business Administration from the University of Virginia. His articles have appeared in Journal oj Product Innovation Management, Journal of the Academy oj Ma&ring Science, and Research-Technology Management. His research interests focus on managing new product development processes in high- technology firms. In 1992 he received a Winner’s Award in the Marketing Science Institute’s Competition on Enhancing the New Product Development Process. His current projects include an investigation of cross-functional team management in Japanese and US firms. Mark E. Parry is Assistant Professor of Business Administration in the Colgate Darden Graduate School of Business Administration at the University of Virginia. He holds a B.A. in history from Metropolitan State College, an M.A. in economics from the University of Texas at Arlington, and a Ph.D. in management science from the University of Texas at Dallas. His articles have appeared in Marketing Science, Journal of’ Marketing, Journal of Producf

Innovation

Management,

Marketing

Letters, International

of Research in Marketing, and the Journal of’ the Academy of Marketing Science, among others. His current research interests focus on new product development processes in high-technology

Journal

fillllS.

development” [30, p. 711. More recently, York Times has observed the following:

the New

Based on comparisons of purchasing power, China may have the second largest economy in the world, ranking behind only the United States. Such statistics, while open to conflicting interpretation, suggest that China could overtake the United States as the biggest economy in another decade or so [16, p. 11.

The second reason for studying new product development in China involves the role of the central government in the Chinese economy. Jefferson, Rawski, and Zheng [15] distinguished five types of Chinese enterprises: 1. state-owned

2. 3. 4. 5.

factories; urban collectives; township-village collectives; small private enterprises; and joint ventures.

Schermerhom and Nyaw [25,13] identified two structural characteristics of state-owned factories that distinguished them from Western firms: simultaneous systems and parallel power structures. In addition to business and operations systems, the typical Chinese

factory also includes two support systems. The enterprise life support system provides services such as housing, health care, child care, and education. The enterprise sociopolitical support system, which includes the Worker’s Union, Women’s Federation, Communist Youth League, and Militia, “is designed to advance socialist ideology” and to “allow the party to exert a ‘political’ presence” [25, p. 111. The result is a parallel power structure consisting of administrative and party authority: Within any individual enterprise, it is not uncommon for the party cadre to be involved in appointing the factory director and other high-level officials at one decision-making extreme, and in making employee compensation and discipline decisions at the other. Furthermore, this party involvement stands in addition to its active role in such ancillary units as the enterprise workers union and workers congress. . . . This means that the party’s influence in each firm is very highly integrated. In many cases, it is the party structure that serves as final arbiter of disputes arising within or among the many disparate internal units of the enterprise [25, p. 13; see also 141.

Given this parallel power structure, it is not surprising that state-owned factories are heavily influenced by the central government’s decisions regarding labor allocation, investment, materials, prices, and performance targets [ 151. At the same time, Jefferson, Rawski, and Zheng noted that Most state-owned enterprises (SOEs) obtain resources and ship products through some combination of plan and market activity. At the margin, virtually all SOEs now have considerable choice in setting output levels, product mix, input combinations, and prices [ 15, p. 2401.

Unlike state-owned enterprises, urban collectives are supervised by local governments. Relative to state-owned enterprises, urban collectives are more autonomous and flexible. Township-village collectives are even less subject to the control of the central government. Given this mix of market forces and central control, what determines new product success in China? To answer this question, we surveyed new product development managers in 129 state-owned enterprises under the jurisdiction of four government ministries. Our analysis of 258 successes and failures indicated that, in the PRC as in Canada, relative product advantage and the acquisition of marketing informa-

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NEW PRODUCT SUCCESSES IN CHINA

tion were highly correlated with new product success. In addition, several factors that were not significantly correlated with success in Canada did emerge as significant correlates of success in the PRC. These included the level of competitive activity, the timing of the product launch (i.e., whether or not the firm was a pioneer), and the level of proficiency in executing activities in the early stages of the product development process. In the remainder of the article we provide additional methodological details and expand on our analysis of the data. Our discussion is organized as follows. The next section summarizes Cooper’s model of new product success. We then describe our data collection process. Succeeding sections present our analyses. We close with a discussion of the implications of our research for new product management in the PRC.

Cooper’s Model and Methodology Cooper [4] hypothesized that the success of new product ventures reflected six types of variables. These variables describe the following: 1. the market in which the new product competes; 2. the compatibility of the new product with the firm’s existing skills; 3. the characteristics of the new product venture; 4. the proficiency of new product development activities; 5. the characteristics of the commercialized product and its launch; and 6. the information acquired during the product development process. Of these, the first three types of variables “describe the setting in which a new product is developed” [4, p. 1261. Thus Cooper referred to the first three types of variables as environmental variables, and to the last three as controllable variables. To evaluate the relative importance of these variables in new product success, Cooper asked managers from Canadian firms to select two typical new product projects introduced by their firms: one a clear commercial success (as defined by the firm), and the other a clear commercial failure. He then presented each manager with seventyseven statements and asked the manager to indicate on an 11-point scale (0 = strongly disagree, 10 = strongly agree) how well each statement described the two products selected by the manager. For each of these seventy-seven statements, Cooper compared the mean

17

rating assigned successful products with the mean rating assigned unsuccessful products. He concluded that “Environmental variables do not play a critical role in deciding new product success” [4, p. 127; italics are Cooper’s].

Data Collection To determine whether Cooper’s results extended to Chinese firms, we used the seventy-seven statements developed by Cooper. Eight Chinese nationals assisted us in preparing the Chinese questionnaire: one was an executive from a major Chinese trading company, three had working experience in China, and four were Ph.D. students in prominent US universities. Four of these people prepared independent Chinese translations of the English-language questionnaire, and the other four translated the Chinese translations back into English. A comparison of the resulting questionnaires revealed considerable consistency across translators. One of the authors (also a Chinese national) met with the eight translators to resolve discrepancies in the translations of certain questions. When disagreements could not be resolved, the authors selected the phrasing favored by a majority of the translators. To assess the appropriateness of the measures, we pretested the resulting questionnaire among nineteen managers in Chinese firms. The final version of the questionnaire reflected several minor modifications suggested by participants in the pretest. It should be noted, however, that both the translators and the pretest participants indicated that the instrument was appropriate for the study of new product development in Chinese firms. The sample frame consisted of mailing lists obtained from four Chinese ministries: Aviation, Electric Machinery-Building, Chemicals, and Electronics. From these mailing lists we randomly selected 300 firms with eleven or more employees, and we mailed one copy of the questionnaire to the president of each firm. In an enclosed letter, we asked each president to forward the questionnaire to his firm’s new product development manager. Of the 300 questionnaires initially mailed, eleven were returned as undeliverable, yielding an adjusted sample size of 289. After one follow-up letter, we obtained 147 usable responses, for an effective response rate of 51 %.2

2After we sent the follow-up letter, twenty-one firms wrote to us, saying that they never received the original mailing and requesting a copy of the questionnaire.

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M. E. PARRY AND X. M. SONG

Table 1. Industry Representation:

The Sample Population

Sample population

Industry

Number of firms

Aero and Space Chemicals Consumer Electronics Machinery Total

65 60 102 62 289

Percentage of the sample 22.5% 20.8% 35.3% 21.4%

100%

1 contains a profile of respondent firms. Although 147 firms participated in the survey, eighteen did not provide information about a product that failed. For this reason, we confined our analysis to the 129 firms that provided information on a new product success and a failure. Of these, 126 were state-owned enterprises, and three were joint ventures. All of the firms participating in our survey produced industrial goods. Our analysis is based on information they provided about 258 products. Of these, 250 were industrial products. The remainder were consumer goods (four televisions, two cassette recorders, one washing machine, and one clock). Because each firm in our analysis provided information about a success and a failure, the success and failure samples were not independent, but related. Thus the F test for comparing two means from independent samples was not the appropriate statistical test for identifying significant differences between successful and unsuccessful products [3, p. 7911. For this reason, the following analyses use the paired-comparison t test to determine the significance of mean differences between successful and unsuccessful products. Table

Analysis Environmental

Variables

Market characteristics. Table 2 contains fifteen statements describing possible characteristics of the market into which a new product is introduced. For thirteen of these statements the mean rating assigned successful products differed significantly from the mean rating assigned unsuccessful products. The two insignificant statements addressed the degree of product homogeneity in the market and the extent of market competitiveness. Table 2 also reports the correlation of each of the fifteen ratings with a dichotomous success-failure

and the Response Sample Successful projects

Failure projects

Number of firms

Response rates

Number of firms

Response rates

29 30 52 36 147

44.6% 50.0% 51.0% 58.1% 50.8%

26 28 46 29 129

40.0% 46.7% 45.1% 46.8% 44.6%

variable (1 = success, 0 = failure). The six market characteristics having the largest negative correlations with new product success all involved competitor activity and the level of consumer satisfaction with competitor products. These characteristics were 1. frequent new product introductions by competitors (Y =-0.814); 2. the presence of a strong, dominant competitor-with a large market share-in the market (I = -0.728); 3. intense price competition (r = -0.7 I 6); 4. high levels of customer satisfaction with existing products (r = -0.707); 5. high levels of loyalty to existing (competitors’) products (r = -0.681); and 6. the presence of a large number of competitors (I- = -0.652). These results diverge sharply from those of Cooper [4], who found only three significant correlations between marketplace descriptors and project success. Of these, the two highest correlations involved (1) the degree of customer need for products in this product class (r = 0.329) and (2) the market growth rate (Y = 0.221). He concluded, “Variables describing the marketplace are notable for their lack of impact on new product outcomes” [4, p. 129; italics are Cooper’s]. Similarly, in a later study Cooper [5] reported that market competitiveness and competitive dominance were not related to three measures of performance, and Cooper and Kleinschmidt [8] found that market competitiveness was not significantly related to any of eleven measures of success. They concluded, “The overwhelming evidence from the research is that market competitiveness is not a decisive determinant of project outcomes” [8, p. 178].3

3Cooper and Kleinschmidt did find that “market potential is a positive wccess factor, certainly for some measures of success, and particularly for opportunity windows” [8, p. 1781.

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Table 2. Success-Failure Impact of Variables Describing the Marketplace Mean score for successes I. Frequency

2. 3. 4. 5. 6. 7. 8. 9. IO. Il. 12. 13. 14. 15.

of new product introductions in the market Existence of a dominant competitor Degree of price competition Degree of satisfaction with competitors’ products Degree of loyalty to competitors’ products Number of competitors Existence of a potential demand only (no actual market) Degree to which users’ needs change quickly in the market Degree of product homogeneity in the market Degree of competition Extent of role of government in the marketplace Market size Existence of a mass market (as opposed to one or a few customers) Degree of need for products in product class Market growth

2.93 2.99 3.36 3.06 3.16 3.38 2.19 5.07 5.94 7.12 7.96 7.62 8.36 8.04 7.58

Mean score for failures” 6.19”: 6.06** 6.16** 5.78** 5.74** 5.84** 4.29** 6.10** 6.55 7.15 6.34** 5.26** 5.62** 5.21** 4.3 I **

Correlation coefficient, r -0.814 -0.728 -0.716 -0.707 -0.68 I -0.652 -0.266 -0.209 -0.110 -0.088 0.322 0.409 0.536 0.592 0.604

‘IA paired-comparison t test was used to test the significance of differences in the responses of successful projects and failure projects. *P < 0.05. **P < 0.01,

Our results indicate that this conclusion does not generalize to the PRC, where measures of competitive activity were significantly correlated with new product success in domestic markets. To obtain further insight into this result, we examined the bivariate correlations between the competitive-activity variables and five measures of relative product advantage. As Table 3 reveals, all of these correlations are less than -0.5. Intuitively, these correlations indicate that Chinese manager perceptions of the competitive environment were closely tied to perceptions of the relative advantage of their own products. Thus the correlation between competitive activity and new product success may simply reflect the high correlations between relative advantage and new product success (see Table 8). Product-firm compatibility. Table 4 contains eight statements addressing the fit between various product requirements and existing firm skills. In every instance, the mean rating assigned successful products was significantly higher than the mean rating assigned unsuccessful products. The skills that had the highest correlations with new product success were the following: 1. 2. 3. 4. 5.

marketing research skills (r = 0.5 19); salesforce and distribution skills (r = 0.5 17); engineering skills (I = 0.460); management skills (r = 0.460); and R&D skills (r = 0.449).

Interestingly, the marketing research skills variable had strong negative correlations with measures of competitive activity and strong positive correlations with measures of market size and growth and with measures of relative product advantage. These correlations suggest that a firm’s marketing research skills affect both its choice of markets and its ability to develop a product with a significant relative advantage. In his Canadian sample, Cooper [4] found seven significant correlations between descriptors of firm skills and project success. Similarly, Cooper and Kleinschmidt [8] reported that 1. marketing synergy (a 5-item measure of the fit between marketing skills and product requirements) was significantly correlated with six measures of new product success; and 2. technological synergy (a 3-item measure of the fit between technical skills and product requirements) was significantly correlated with seven measures of new product success. At the same time, it should be noted that, in both Canadian studies, none of the significant correlations between skills and success exceeded 0.4. This suggests that the relationship between firm skills and product success is-stronger in the PRC than in Canada. With regard to the skills variables, the sole insignificant correlation reported by Cooper addressed the company’s financial resources. Cooper concluded

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Table 3. Correlations

M. E. PARRY AND X. M. SONG

Among Competitive

Activity Variables and Relative Product Advantage

Variables

Product advantages relative to competitors Competitive

Activity

Frequency of new product introductions Existence of a dominant competitor Degree of price competition Degree of satisfaction with competitors’ products Degree of loyalty to competitors’ products Number of competitors

Unique features”

Meeting customer needsh

-0.7 1 -0.63 -0.66 -0.56 -0.55 -0.58

-0.78 -0.70 -0.73 -0.66 -0.64 -0.64

Reduced customer

costs’

-0.69 -0.59 -0.55 -0.57 -0.55 -0.69

0 Compared tn competitive products, our product offered a number of unique features or attributes to the customer. b Our product was clearly superior to competing products in terms of meeting customers’ needs. c Our product permitted the customers to reduce their costs, when compared to what they were then using. dour product permitted the customers to do a job or do something they could not presently do with what was available. pOur product was of higher quality-tighter specifications or stronger or lasted longer or more reliable, etc.-than competing

that “financial strength alone is simply not that critical a determinant of new product success” [4, p. 1291. In the PRC sample, the financial strength variable had the lowest correlation of the eight descriptors in Table 3, suggesting that Cooper’s conclusion may generalize to industrial markets in China. The new product venture. Table 5 contains eighteen statements that describe (1) characteristics of the new product venture and (2) the firm’s experience with the production and marketing activities inherent in the venture. With four exceptions, the mean ratings assigned successful products were statistically greater than the mean ratings assigned unsuccessful products. The venture characteristics having the highest correlations with new product success were as follows: 1. the product was highly innovative (r = 0.523); 2. the product was a “big ticket” item (r = 0.504); 3. the product was high-technology product (r = 0.499); and Table 4. Success-Failure

Impact of Product-Firm

Compatibility of the following resources in the firm for this product project (0 = poor; 10 = high)

*P < 0.05. **P < 0.01.

r test was used tc test the significance

-0.81 -0.78 -0.77 -0.74 -0.70 -0.72

-0.77 -0.72 -0.7 1 -0.69 -0.67 -0.65

products.

It should be noted that the bivariate correlations among the first three variables were also high (above 0.47). Thus, highly innovative products tended to be expensive products that incorporated cutting-edge technology. This result is consistent with the second author’s working experience in China. The functional form of the relationship between innovativeness and success also merits a brief comment. We asked respondents to indicate the relative success of successful projects on an 1 l-point scale (0 = barely met our minimum profitability criteria, 10 = far exceeded our minimum profitability criteria). We also asked respondents to indicate the relative failure of unsuccessful projects on an 1 l-point scale (0 = barely met our minimum profitability criteria, 10 = was far below our minimum profitability criteria). We then multiplied the ratings of the failed projects by -1, Compatibilities Mean score for successes

6.41 6.96 7.80 6.71 7.36 7.49 4.93 7.30

research skills and people Sales force and/or distribution resources and skills Engineering skills and people Management skills R&D skills and people Production resources and skills Advertising and promotion skills and resources Financial-resources

“A paired-comparison

Higher qualitye

7. the product idea came from the market-place (r = 0.725).

1. Marketing

2. 3. 4. 5. 6. 7. 8.

Performed a new task”

of differences

Mean score for failures”

4.10** 4.70** 6.04** 5.36** 5.52** 6.05** 3.70** 6.30**

Correlation coefficient, r 0.519 0.517 0.460 0.460 0.449 0.363 0.339 0.178

in the responses of successful projects and failure projects.

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IDENTIFYING NEW PRODUCT SUCCESSES IN CHINA

Table 5. Success-Failure

Impact of the Characteristics

21

of the New Product Venture Mean score for successes

Mean score for failures”

Correlation coefficient, r

8.18 7.14 7.16 5.77 6.68

5.58’* 4.62** 4.89** 4.94** 5.55**

0.523 0.504 0.499 0.241 0.213

3.29

3.43

0.038

9.19 8.42

5.60** 6.93**

0.725 0.365

9.18

8.07**

0.345

3.03

2.88

0.083

6.60 5.59 5.20 7.97 6.86 7.17 8.29 3.13

5.23’* 4.05%’ 3.66** 7.02% 6.11* 6.88 7.80 6.19**

General

1. Innovativeness

of product to the market Per unit price (whether product was a big-ticket item) Technology level of product Mechanical-technical complexity of product Relative magnitude of investment (vis-ci-vis other new products) 6. Whether product was a custom product or not

2. 3. 4. 5.

Source 7. Whether product idea was market-derived 8. Technical determinateness (whether the technical solution was clear at the beginning) 9. Market determinateness (whether product specifications were clearly defined by the marketplace) 10. Whether the product was a defensive (as opposed to an offensive) introduction Newness to the Firm I I. 12. 13. 14. 15. 16. 17. 18. 0A

Newness Newness Newness Newness Newness Newness Newness Newness

of of of of of of of of

technology distribution/sales force advertising/promotion product class competitors customers for product customer need production process

naked-comparison

*p;o.o5.

1

0.326 0.305 0.284 0.136 0.085 0.060 0.057 -0.725

t test was used to test the significance of differences in the responses of successful projects and failure projects.

**p
thereby obtaining a 2 1-point relative success variable that ranged from -10 to 10. We defined a highly innovative product as a product that received an innovativeness rating of 9 or 10. Similarly, we defined a moderately innovative product as a product that received an innovativeness rating between 3 and 9, and a noninnovative product as one that received an innovativeness rating of 3 or less. We then computed the mean success rating for each type of product. The mean success rating for the 111 highly innovative products was 4.47. The ninety-two moderately innovative products had a mean success rating of -0.43, and the fifty-five noninnovative products had a mean success rating of -4.09. Unlike Kleinschmidt and Cooper [16], we found no evidence of a u-shaped relationship between product innovativeness and success. On the contrary, these results suggest that in Chinese firms the relationship between innovativeness and success is linear.

Cooper [4] found only four significant correlations between venture descriptors and project success. Of these, the highest correlations involved (1) the innovativeness of the product to the market (r = 0.199), (11) the newness of the product class to the firm (r = -0.163), and (17) the newness of the type of customer need served by the firm (r = -0.169). Cooper suggested that these results explained “the new product dilemma the firm often faces-specifically, the choice of the optimal level of product innovativeness” (p. 130). Our results suggest that, for firms in the PRC, this explanation must be qualified. Although product innovativeness was an important determinant of success (r = O/523), newness of customer needs was not significantly related to product success (r = 0.057), and newness of product class was positively related to product success (r = 0.136). Nevertheless, our data indicate that Chinese firms faced a dilemma in choosing the optimal level of innovativeness, because

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“newness of the production process” was negatively correlated with new product success (r = -0.725). Although innovative products had relatively higher probabilities of success, these probabilities were diminished when the products entailed production processes that were unfamiliar to the firm. The strong negative correlation between “newness of the production process” and new product success is consistent with descriptions of structural obstacles that impede the performance of Chinese electronics firms. According to Simon, . . . the weak technical foundation of most enterprises makes it difficult for them to embark upon even the most modest research and development efforts. This problem has also been evident with respect to imported technologies; though their application has increased production, there has been very little indigenization of the imported technologies, let along new technical innovations [26, pp. 23-241.

In the semiconductor industry, Simon attributes quality problems in the semiconductor industry to “obsolete manufacturing facilities and [the] inability to utilize at the factory level technological advances made in the lab” [26, p. 241. Similar problems afflict the textile industry [21, p. 371. To the extent the new product processes imply greater demands for electricity and water, the negative correlation between “newness of the production process” and new product success may also reflect basic infrastructure problems. A recent discussion of the textile industry in the PRC illustrates some of these problems: infrastructure bottlenecks will remain a critical obstacle to more comprehensive textile industry development. Electricity and water shortages will continue throughout the country, while transportation bottlenecks will force the industry to concentrate in increasingly wealthy coastal areas, where labor costs tend to be higher than in the interior [21, p. 381.

In addition to these infrastructure deficiencies, the ability of state-owned enterprises to adapt to new production processes is undoubtedly affected by the role of central planning in China’s economy. In reviewing his analysis, Cooper [4] reported surprise at the finding t.hat only two newness-to-thefirm variables were significantly (and negatively) correlated with product success.4 A similar result emerged from the PRC data. Although six of the eight

M. E. PARRY AND X. M. SONG

newness variables were significantly correlated with success, five of these correlations were positive. More specifically, relative to unsuccessful products, successful products were more likely to involve 11. a technology that was totally new to the company; 12. a distribution system and/or type of salesforce that was totally new to the company; and 13. a type of advertising and/or promotion that was totally new to the company; and 14-15. a product class that was new to the company and that embraced competitors who were new to the company. At the same time, it should be noted that the correlations between these newness-to-the-firm variables and product success ranged from 0.085 to 0.326. Thus, the positive correlations between these descriptors and new product success were far weaker than the negative correlation between newness of the production process and product success. The positive correlation between product success and the salesforce/distribution-newness deserves further comment. Earlier we noted the significant correlation (r = 0.5 17) between new product success and the product’s fit with the firm’s existing level of salesforce and distribution skills and resources. The contingency table in Table 6 sheds some light on the apparent contradiction between these results. To create this table, we classified a product as having a good fit with the firm’s salesforce and distribution skills and resource if the product’s rating on the salesforce/distribution-skills/ resources fit variable exceeded the mean rating for all products on that variable. Similarly, we classified a product as imposing high levels of neti salesforce and distribution requirements on a firm if the product’s rating on the salesforce/distribution-newness variable exceeded the mean rating for all products on that variable. We then calculated mean success ratings contingent upon these two dummy variables. The resulting contingency table indicates that when a product’s fit with the firm’s salesforce and distribution resources and skills is low, variations in the salesforce/ distribution-newness variable have no relationship with relative product success rates, which are poor. However,

41n a later study, Cooper [5] reported that market newness (as measured by newness of advertising/promotion methods, newness of channels and salesforce, newness of customers and newness of competitors) was negatively correlated with new product “track records” (success rates and kill rates).

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Table 6. Salesforce/Distribution Skills and Resources, SalesforceAXstribution Newness, and Relative Product Success Rates Newness Product salesforce

fit with existing

LOW

of salesforce/ requirements

and distribution

skills and resources Highh

distribution High”

6.29’

LOW

cessful than ideas that originated from technical work or from in-house labs. Finally, “newness of the production process” had a strong negative correlation the remaining with product success. Surprisingly, newness variables were positively correlated with success, although some of these correlations were statistically insignificant.

0.87

(85) -3.19

(61) -3.19

(43)

(69)

a A product was defined to impose high levels of new salesforce and distribution requirements on a firm if the product’s rating on the salesforce/distribution-newness variable exceeded the mean rating for all products on that variable. h A product’s tit with the firm’s existing salesforce and distribution skills and resources was defined to be high if the product’s rating on the salesforce/distribution-skills/resources fit variable exceeded the mean rating for all products on that variable. ‘Table entries are mean relative success ratings. Numbers in parentheses are the number of products in each cell.

when a product’s fit with these skills and resources is high, higher ratings on the salesforce/distributionnewness variable are associated with higher relative product success rates. One possible explanation for the latter result is that products requiring new distribution channels or new salesforce skills represent a conscious effort by Chinese firms to diversify into markets that are relatively more attractive than the markets the firms have historically served.5 Cooper found no evidence market-derived ideas had a higher probability of success: “Technology-push ideas are as likely to succeed as market-pull ideas” [4, p. 130; italics are Cooper’s]. This conclusion also did not extend to Chinese firms. As Table 5 reveals, product ideas derived from the market place were much more likely to be successful than were ideas that originated from technical work or from in-house labs. Summary. In this section we have examined the relationship between new product success and environmental variables in the PRC. Our results indicate that variables measuring competitive intensity had a strong negative correlation with new product success. The firm skills and resources having the highest correlations with success involved marketing research, sales and distribution, engineering, management, and R&D. We also found that product ideas derived from the marketplace were much more likely to -be suc-

5 The authors thank Thomas Hustad for this observation.

23

Controllable

Variables

of process activities. Table 7 contains statements describing twelve new product development activities. We asked each respondent to indicate how well these activities were undertaken for each product selected by the respondent. In every case, the mean rating assigned successful products were statistically higher than the mean rating assigned unsuccessful products. Moreover, with one exception (the proficiency of in-house product testing), the activity ratings had correlations with new product success that exceed 0.5. Of these, the highest correlations involved the following activities: Proficiencies

1. 2. 3. 4. 5.

product development (r = 0.760); market research (r = 0.7 19); preliminary market assessment (r = 0.7 11); initial screening (r = 0.692); and financial analysis (r = 0.690).

These results are consistent with those reported by Cooper. With one exception, however, the correlations that Cooper reported were weaker than those recorded in Table 7. Moreover, in Canadian firms the proficiency ratings having the strongest correlations with success involved the market launch (r = 0.5 17), prototype testing with customers (r = 0.415), and test marketing (r = 0.407). All three of these activities occur in the later stages of product development and commercialization. In contrast, the correlation coefficients reported in Table 7 suggest that, in the PRC, proficiency in the early stages of the product development process was relatively more important in discriminating between success and failure. Interestingly, all five of the development activities listed above were highly correlated (0.5 or more) with the degree to which the product idea was market derived. This may indicate that market-derived ideas were more difficult to sell internally than ideas derived from in-house labs or technical work. Thus marketderived ideas required more thoughtful screening and more careful market research, financial analysis, and product development.

24

J PROD INNOV MANAG 1994;11:15-30

M. E. PARRY AND X. M. SONG

Table 7. Success-Failure

Impact of Proficiencies

Extent to which the following new product activities were proficiently undertaken (0 = poor; IO = excellent)

of New Product Activities Mean score for successes

Mean score for failures”

Correlation coefficient, r

7.83 7.61 7.80 7.94 8.10 7.01 8.09 7.48 6.95 7.73 7.45 8.44

4.24** 4.00** 4.45** 4.54:: 4.60** 3.79** 4.56** 4.35** 4.23*” 4.91** 5.28” 7.30**

0.760 0.719 0.711 0.692 0.690 0.656 0.637 0.553 0.543 0.543 0.506 0.324

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Product development Detailed market study or market research Preliminary market assessment Initial screening Financial analysis Market launch Trial production Start-up of full production Test marketing/trial selling Prototype testing with customer 11. Preliminary technical assessment 12. Prototype testing in-house “A

paired-comparison t test

was used to test the

significance of differences

in the responses of successful projects and failure projects.

* P < 0.05. ** P < 0.01.

It should be noted that the emphasis on predevelopment activities is consistent with the later findings of Cooper [6] and Cooper and Kleinschmidt [7]. These authors identified all five proficiencies listed above as keys to new product success. Three of these proficiencies also emerged as significant discriminators between successful and unsuccessful products in Australia [ 1 11. Elements of the commercial entity. Table 8 contains thirteen statements describing the benefits of the product and the launch effort that accompanied the product’s introduction. With one exception, the mean ratings assigned successful products were statistically greater than the mean ratings assigned unsuccessful products. Excluding price, the correlations between product benefit ratings and new product success all exceeded 0.63. With regard to the launch effort, only one correlation (first into market with this type of product) exceeded OS. These results are consistent with those reported by Cooper, who also reported twelve significant differences in mean ratings between successful and unsuccessful products. The two ratings having the strongest correlations with new product success involved (1) the degree to which the product met customer needs better than competitors (r = 0.492) and (2) the quality (reliability) level of the product (r = 0.416). This result led Cooper to identify “the product as the core or critical strategy in industrial product innovation” [p. 132; italics are Cooper’s]. Similarly, Cooper and Kleinschmidt [8] reported that “product advantage” (a 6-item measure) was significantly correlated with 10 measures of product success. They concluded, “The

evidence overwhelmingly points to product advantage as a number one success factor in this study” [8, p. 1781. Cooper also reported that the rating having the third strongest correlation with new product success concerned the targeting of the salesforce-distribution effort (r = 0.410). Because the correlation between new product success and the strength of the salesforce effort was lower (r = 0.283), Cooper concluded that “It is the direction of effort rather than the magnitude of effort that impacts more strongly on new product results” [p. 132; italics are Cooper’s]. This result did not extend to new product successes in the PRC. As Table 8 indicates, the correlation between success and strength of the salesforce-distribution effort (r = 0.416) was stronger than the correlation between success and the effective targeting of salesforce effort (r = 0.374). Thus, the direction and the magnitude of the salesforce-distribution effort were both related to new product success in China, but both were clearly dominated by characteristics of the product itself. In Canadian firms the least important characteristics of the commercial entity were (1) relative price (r = 0.053) and (2) pioneer entry (r = 0.177). The first result also applied in the PRC: the relative price variable was not significantly correlated with product success.6 The insignificance of the pioneer entry variable, however, did not generalize to China. Of the seven variables describing the new product launch, the pioneer entry variable had the strongest correlation with new product success (r = 0.522).

hThis

may reflect governmental

constraints

on pricing

[see

1.51.

J PROD INNOV MANAG 1994; I I :15-30

IDENTIFYING NEW PRODUCT SUCCESSES IN CHINA

25

Table 8. Success-Failure Impact of the Elements of the Commercial Entity Characteristics of the commercial entity (0 = no or low; 10 = yes or high) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Il. 12. 13.

Product was of higher quality-lasted longer, more reliable, etc. Product met customer needs better than competitors Product permitted customer to perform a unique task Product had unique features or attributes Product reduced customers’ costs First into the market Production facilities were geared up and smooth Production volume was adequate Strong sales force/distribution effort Sales force/distribution effort well targeted Strong advertising/promotion effort Advertising/promotion effort targeted Product was priced lower

u A paired-comparison

‘P

t test was used to test the significance

of differences

Mean score for successes

Mean score for failures”

Correlation coefficient, r

8.22 8.24 7.58 8.01 7.16 7.05 7.52 7.90 5.37 6.95 4.34 6.95 6.09

4.41** 4.35** 4.02** 4.75** 4.16** 4.36** 5.06** 5.62** 3.95** 5.23** 3.02** 5.02** 5.63

0.788 0.780 0.768 0,683 0.635 0.522 0.47 1 0.442 0.416 0.374 0.372 0.362 0.105

in the responses of successful

projects and failure projects.

-20.05. -

**P
The second author’s working experience in China suggests that pioneer industrial products are often perceived to be superior, so that Chinese firms are reluctant to switch to products manufactured by later entrants. The significance of the pioneer entry variable may also reflect the importance of personal relationships in Chinese businesses [ 181. Relationships appear to assume particular importance in enterprises linked to the defense industry, which includes the three Ministries included in our survey. According to Cheung, analysts indicate that “most contracts [in the defense industry] are still awarded on the basis of bureaucratic connections and patronage” [2, p. 411. It should also be noted that the readiness-ofproduction-facilities variable and the capacity-ofproduction-facilities variable both had high correlations (0.50 or more) with a variable measuring the importance of government regulation on the design and testing of products. This suggests that one of the primary constraints facing state-owned enterprises arises from the impact of regulation on production. Information acquired. Table 9 contains eleven statements describing the types of information acquired by the firm during the development of the product. With one exception, the mean ratings for successful products were statistically greater than the mean ratings for unsuccessful products. The exception involved management’s perceptions of the risk inherent in the new product venture. The significant ratings all had correlations with new product success that exceeded 0.4. Of these, the highest correlations indicate that firms introducing successful products:

1. Were confident about the success of their products (r = 0.659); 2. Knew customer needs, wants, and specifications for product (r = 0.609); and 3. Knew the market size (r = 0.600). As might be expected, the confident-about-success variable was strongly correlated (0.50 or more) with measures of market size and growth and with measures of relative product advantage. Moreover, the customerknowledge variable and the market-size variable both had high correlations (0.50 or more) with the proficiency-of-marketing-research variable. According to Cooper [4], the three information variables having the strongest correlations with new product success involved (1) knowledge of customer price sensitivity (r = 0.394), (2) understanding of buyer behavior (r = 0.391, and (3) knowledge of the customer’s needs, wants, and specifications for the product (r = 0.362). These results lead Cooper to view “an understanding of the customer-his needs, wants, behavior, and price sensitivity-as being vital to success in industrial product innovation” [4, p. 1341. As Table 8 indicates, in the Chinese sample all three ratings had correlations with new product success exceeding 0.5. Thus Cooper’s conclusion appears to extend to Chinese as well as Canadian firms. In addition to marketing-information variables, Table 9 also contains four technical-production variables (see items 7-10). Of these, “knew production costs” had the highest correlation with new product success in Canada (r =0.291) [see 41. In contrast, of the

26

.JPROD INNOV MANAG 1994;l I:1530

Table 9. Success-Failure

M. E. PARRY AND X. M. SONG

Impact of Information

Information acquired; extent to which the company (0 = no-none; 10 = yes-high)

Acquired During the New Product Process

...

I. Were confident

2. 3. 4. 5. 6. 7. a. 9. 10. Il.

about product’s success Knew customer needs, wants, and specifications for product Knew market size Understood product design well-all design “bugs” were ironed out Knew customer price sensitivity Understood buyer behavior Understood product’s technology Knew competitorsproducts, strategies, etc. Knew production costs Knew production process/equipment Viewed product as high-risk one

” A paired-comparison *P < 0.05. **P
f test was used to test the significance

of differences

technical-production variables, “knew product process-equipment” had the minimum correlation with success in the PRC (r = 0.416). The difference in the magnitudes of these correlations (0.291 versus 0.416) indicates that technical-production variables were relatively more important in China than in Canada. Finally, Table 9 contains one variable that measured management’s perception of the risk associated with a new product. Our results indicate that this variable was not significantly correlated with new product success or failure. Summary. In this section we have examined the relationship between new product success and controllable variables. Our results indicate that proficiency ratings for the following activities were highly correlated with success: product development, market preliminary market assessment, initial research, screening, and financial analysis. Product benefit ratings were also strongly correlated with new product success, as was pioneer entry. Finally, the information variables having the strongest correlations with success addressed the size of the market and the firm’s knowledge of customer needs, wants, and specifications.

Discussion In this article we have extended the work of Cooper by examining the determinants of new product success in the People’s Republic of China. We found several similarities between the correlates of success in Canada and the PRC. In both countries,

Mean score for successes

Mean score for failureP

Correlation coefficient, r

9.16 8.55 7.89

5.64** 5.71** 5.12** 6.09** 5.98** 5.44** 6.04** 5.87** 7.02** 6.85** 5.81

0.659 0.609 0.600 0.537 0.512 0.505 0.49 I 0.478 0.449 0.416 0.004

8.55

8.20 7.84 8.22 7.93 8.79 8.42 5.16

in the responses of successful

projects and failure projects.

1. relative product benefits (excluding a lower price) were highly correlated with new product success; and of marketing information was 2. the acquisition significantly correlated with new product success. In addition, we found that several significant relationships identified by Cooper assumed even greater importance in the PRC. These included the relationship between product success and productfirm compatibilities, as well as the relationship between success and technical-production variables. We also identified a number of important differences between Canadian firms and Chinese firms. Our data indicated that, in the PRC, 1. competitive activity was highly and negatively correlated with new product success; 2. pioneer entry was significantly correlated with new product success; 3. product ideas derived from the market place were much more likely to be successful that ideas that originated from technical work or from in-house labs; 4. relative to proficiencies in the later stages of the product development process, proficiencies in the early stages of the product development process were more important discriminators between success and failure; and 5. the strength as well as the targeting of the salesforce-distribution effort was correlated with new product success. Further, we found that, in China, “newness

of the

IDENTIFYING

J PROD INNOV MANAG 1994: I I :15-30

NEW PRODUCT SUCCESSES IN CHINA

was the newness-to-the-firm production process” variable most highly correlated with failure. This result contrasted with the experience of Canadian firms, where “newness of customers” and “newness of the product class” were most correlated with failure. We also noted that, with the exception of the “newness of the production process,” newness was not necessarily a negative in China. In fact, our analysis indicated that several of the newness-to-the-firm variables were positively and significantly correlated with success. One surprising result was the number of variables that were significantly correlated with success. By itself, this result might raise concerns that respondents were affected by hindsight bias. Two other factors lessen this concern. First, the signs of some of the significant correlations differed from the signs reported by Cooper. For example, four of the newnessto-the-firm variables were positively correlated with success. Second, a principal component analysis of the seventy-seven descriptor variables yielded sixteen components with eigenvalues greater than 1. If hindsight bias was a sufficient explanation for our results, we would expect to find fewer significant principal components7

Other Studies Although we have explicitly compared our results with those of Cooper [4,5,6] and Cooper and Kleinschmidt [7,8,9,10], a number of our results are consistent with those of other studies that have compared successful and unsuccessful projects. The first such study, Project SAPPHO, compared forty-three pairs of innovations and revealed forty-one significant differences between successes and failures [22,23]. The most important discriminators included firms’ understanding of users’ needs and the attention paid to marketing and publicity. In a comparison of projects from Europe and Japan, Utterback et al. reported that successful projects were more likely to (1) involve products having a great competitive advantage and (2) face no initial marketing difficulties [29]. From a study of West German projects, Gerstenfeld reported that demand-pull projects tended to be more successful than technologypush projects [ 121. More recently, Maidique and Zirger found that successful products from the U.S. electronics industry were based on a sounder understanding of

’ For a detailed description Song and Parry [27].

of this principal

component

analysis,

see

21

consumer needs and were marketed more actively than failures. In addition, the markets for successful products were forecast more accurately, and were expected to be more commercially successful. Successful products also were more likely to be pioneer products than were failed products [ 193. In a related study, Zirger and Maidique reported that “failures were more likely for products introduced into highly competitive markets” [30, p. 8781.

Managerial

Implications

Given the number of significant relationships identified in our analysis, where should Chinese managers focus their attention? To answer this question, we computed nine summary correlations by averaging the correlations of items grouped together by Cooper. Table 10 contains these averages, along with the correlations of two variables (“Product Idea was Market Derived” and “Newness of Production Process to the Firm”) that differed sharply from the correlations of conceptually-related variables.8 As this Table reveals, the four most important correlates of success were 1. relative product 2. a market-derived 3. proficiencies of and 4. market size and In addition, failure:

advantage (r = 0.73); product idea (r = 0.73); development activities (r = 0.61); potential (r = 0.54).

two factors were highly correlated

with

1. newness of the production process to the firm (r = -0.73); and 2. competitive intensity (r = -0.72). The importance of these factors was also supported by responses to an open-ended question included in our survey. We asked each respondent to consider the major reasons for the failure of the project that he had described to us. Of the ninety-five responses that we received, sixty-eight fell into one of the six categories

s Although Cooper grouped “Product Idea Was Market Derived” with three other “Source” variables (see items 7-10 in Table 4) the four items appear to be conceptually distinct. Moreover, “Product Idea Was Market Derived” had a correlation with success that was more than twice that of any of the remaining Source variables. “Newness of Production Process to Firm” was one of seven Newness variables (see items I 1-I 8 in Table 4), but it was the only one with a significant negative correlation with product success. For this reason, we separated it from the remaining newnessvariable correlations.

28

J PROD INNOV MANAG 1994;11:15-30

Table 10. Average Correlations New Product Success

M. E. PARRY AND X. M. SONG

of Sets of Variables with

Number of Items I. Relative product advantage

Product idea was market derived Newness of production process to firm Competitive intensity Proficiencies of development activities Market size and potential Information acquired during the new product process 8. Proficiency of launch 9. Fit with company skills (e.g., technoIO. Product characteristics 2. 3. 4. 5. 6. 7.

logical

5

Average Correlation 0.73u

I I

-0.73’

6

4.72“

12

0.73h

0.61e

4

0.54t

10

0.488

7

0.42h

8

0.41’

6

0.34i

7

0.18k

level)

Il. Newness to firm of activities required for developing and launching the product 0 Computed from statements l-5 (Table 8). b Computed c Computed n Computed e Computed fcomputed KComputed h Computed I Computed J Computed rtComputed

from from from from from from from from from from

statement statement statements statements statements statements statements statements statements statements

7 (Table 5). 18 (Table 5). IO-I5 (Table 2). l-l 2 (Table 7). l-4 (Table 2). l-10 (Table 9). 612 (Table 7). 1-8 (Table 4). l-6 (Table 5). I l-17 (Table 5).

listed in Table 11. Exhibit 1 contains representative comments from each of these six categories. By far the largest share of responses cited a lack of information about customers and a failure to understand customer needs. Many comments also cited deficiencies in the new product development process. These comments are consistent with our earlier analysis of processactivity proficiencies, which revealed that proficiencies in product development, market research, and preliminary market assessment had the strongest correlations with product success. The responses contained in Exhibit 1 also raise the issue of market-derived ideas. A number of respondents indicated that development people sometimes tended to focus on technical aspects of development projects, instead of focussing on customer benefits. In fact, several respondents specifically mentioned the distinction between technology-push and market-pull ideas. Finally, Exhibit 1 confirms the negative correlation between “Newness of Production Process to Firm” and product success rates. This result is also consistent with the comments of Simon, who observed about the Chinese electronics industry an “inability to utilize at

the factory level technological advances made in the lab” [26, p. 241. These production problems are no doubt compounded in state-owned enterprises, because many of these enterprises have been converted from military production [2, p. 401. It should also be noted that these production problems have important implications for foreign partners in joint ventures in China. In particular, the strong correlation between new product failure and the newness-of-the-production-technology variable indicates that foreign partners should pay particular attention to the transfer of the necessary production skills to their Chinese partners. This observation is consistent with the recommendations of Newman, who observed that, in successful joint ventures, Output standards for each [production] step are clearly defined, the equipment and conversion technology are specified, the work methods for individual operators are set, performance is monitored, coordination is made routine. In most respects, the manner of operating is copied from time-tested practices of leading companies in the world (usually the foreign partner). This careful specification and control of the way work is to be performed obtaining

is a vital element

in

desired products and services [20, p. 691.

In summary, our analysis suggests that Chinese managers should be concerned first and foremost with relative ‘product advantage. This conclusion assumes particular significance in light of reports suggesting that many Chinese industries rely on obsolete technologies and produce inferior goods [1,2,16,21]. As Cooper explained, “The product is the core or central strategy in most industrial new product ventures; and it Table 11. Summary

of Reasons for Product Failure Number of responsesa

Reason for failure Relative product advantage/understanding customer needs R&D/marketing/manufacturing interface problems Proficiencies of development activities Product idea was market-derived Proficiency of product introduction Newness Other

of the production

process

28 10 9 8 7 3 30

0 We asked respondents to indicate the major reasons for the failure of the unsuccessful projects that they selected. We received 95 responses to this open-ended question.

29

J PROD INNOV MANAG 1994;I I:1530

IDENTIFYING NEW PRODUCT SUCCESSES IN CHINA

Exhibit 1. Reasons Why Products Failed Relative Product tomer Needs

Advantage/Understanding

Cus-

We did not understand the value of the product at the customer level. We did not perform a careful performance-cost analysis. Customer needs were difficult to quantify-we missed a key requirement. We failed to define the need that our product would satisfy. Our knowledge of customers’ perceived needs was inadequate. We did not know exactly what the customer wanted. We lacked information about customer needs. We did not conduct enough market research to understand customer needs and requirements. Product Idea Was Market Derived

A good technology failed because we lacked a good understanding of the market. We introduced a “technology-push” product; we would have been more successful with a “market-pull” idea. We placed too much emphasis on patentable/ proprietary technology; a radically new technology was not valued by the market as highly as we valued it. R&D believed that the best technical solution would win; they would not abandon an idea that wasn’t going anywhere. We did not recognize that a technical success could be a complete failure in the field. We were euphoric over the technical features of a product that was not matched to market demand. We introduced a “technology-push” product with no defined target market and need. We did not have a customer and his or her problem in mind when we designed and developed our product.

is through the product that the firm must seek its differential advantage” [4, p. 1001. Achieving this goal will require a careful analysis and thorough understanding of customer needs. In addition, Chinese

Problems terfaces

at the R&D/Marketing/Manufacturing

In-

R&D and marketing did not share the same language. There was also a lack of mutual respect and trust. There was a lack of functional involvement among operations, R&D, marketing, and the customer. R&D did not challenge marketing and sales. Proficiencies

of Development

Activities

We lacked a well-planned product introduction. We took too long to develop the product. We failed to meet performance specifications. Technical development and market development were not coordinated. We overestimated what we could accomplish technically and the time and resources required. Our pilot tests, product modification, and customer confirmation activities were insufficient. Our pilot tests were inadequate. Proficiency of Product Introduction We were unable to develop samples in a timely fashion. Marketing did not push the product hard enough. We did not understand the distribution channels. The education of our field sales force was inadequate. Newness of Production

Process to Firm

We lacked timely development of the manufacturing process. We introduced the product before its manufacturing parameters were fully defined. We did not adequately test the production process before designing and constructing the commercial plant.

firms must become more proficient in important new product development activities such as preliminary market assessment, market research, and the initial screening of new product ideas.

30

M. E. PARRY AND X. M. SONG

J PROD INNOV MANAG 1994:11:15-30

Authors’ names appear in alphabetical order. Both contributed equally to the research. The authors gratefully acknowledge the financial assistance of Dan Newton, John Tyler Professor of Business Administration at the Darden School, the Darden School, China National Aero-Technology Import and Export Corporation (CATIC), and the Citicorp Global Scholar’s Fund. The authors thank Xian Yi Xia for his assistance in survey administration and data entry. The authors also thank Thomas Hustad, Robert Spekman, Robert B. Woodruff, and two anonymous reviewers for helpful comments on earlier drafts of this article.

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