Franz HAAS

Deciding the Entry Mode in Chinese Markets. Practices Managers Should Avoid Pursuing

This research project considers Austrian enterprises, which have been pursuing business partnerships and relations with Chinese companies. One specific question of the research aimed at finding out which factors lead to pronounced difficulties experienced by companies having chosen a specific market entry strategies. This research question was explored through a primary research of 86 managers participating in an online survey in 2016. The data analysis implied an exploratory factor analysis of ten pre-defined factors that led to a new model of five components depicting narrow decision-making processes, namely: relying too much on company-related external expertise, national- or international-related external expertise, isolated practices like adopting what other companies do, purely financial-driven processes, or encapsulated decision processes. The factor scores’ analysis shows that there are significant differences between companies from different economic sectors.
JEL Classification M00, F21, F23

The author gratefully thanks all Austrian participants of the online survey in 2016. Likewise, the authors wishes to thank Reinhard Neumeier for discussions regarding feasible applications of statistical methods according to the specific question of research of this study.

Full Article

1. Introduction

The aim of an ongoing research project is to look at Austrian enterprises and their business trajectory to China, which means establishing business relations with hitherto unknown partners in a rather far-flung country. It also means taking considerable risks in markets with different structures within a fundamentally different context.

One overall research question of this study is: What is the impact of particular methods and achieved decision-making processes for the subsequent development of business in China? Consequently, a sub-question has developed as follows: What factors are responsible for companies facing subsequent pronounced difficulties? Or to formulate it in different way: Which patterns of deciding the entry-mode are not advisable?

2. Literature Review

Finding a suitable institutional frame for international business activities is not a simple task. Selecting a specific entry mode in a foreign market is usually based on a large set of careful considerations.

Therefore, it does not come as a surprise that a uniform theoretical framework is lacking for this management-decision. Moreover, internationalization has gained pace within the last years. Many theories and models of internationalization have been proposed, each focusing on different aspects (Malhotra, 2003). Influencing factors can broadly be divided into internal (company-related) and external (environmental-related) factors. For instance, on the basis of internal internationalization, the company moves to outward internationalization due to relationships with suppliers, which are already active in foreign countries (Jones, 1999). An important internal factor is the size of the company as it usually determines both resources as well as the extent of experiences in internationalization. This means that there is a tendency that large enterprises prefer proprietorship over partnership. But this relation had not been generally supported by research so far (Ninerola et al., 2017, p.39).

The choice of an entry mode has to be classified as a strategic decision. In practice, frequently top-managers come to these decisions with poor information on the base of narrow analysis and/or within a limited schedule of time (Li et al., 2013, p.162). Also, it has to be taken into account that quality of information differs: Messages in local, regional or national settings are relatively uniform, they often contain just different versions of the same core of information. Whereas messages from global sources usually contain various and substantial different information (Vorobeychik et al., 2017). Hence, it seems to be important to apply a more sophisticated way of decision-making. Moreover, concentration on narrowed decision-making processes is maybe not advisable. Therefore, it could be useful to know specific ways one should avoid. To identify such narrow decision-making processes is the aim of this paper.

3. Research Methodology

A research model has been stated where various company-internal and company-external factors have been defined as independent variables. A primary research has been conducted in spring 2016, which was aimed at asking managers of Austrian enterprises, who have already established business relations with companies from China. This online questionnaire contained both closed-ended and open-ended questions. 86 sets of data could be obtained and used in the following analysis.

The chosen method of analysing data for patterns, which should be avoided, is an exploratory factor analysis. This factor analysis is applied on those selected sets of data where pronounced difficulties (troubles) have been reported. These difficulties have been extracted out of the answers of an open-ended question (number 13 in the questionnaire) and are represented as a super-category. This super-category covers three categories of answers to question 13: cultural difficulties, missing mutual trust and great time delays of protracted negotiations. As both basic research models are used in this paper (the quantitative approach and the qualitative approach), the statistical calculations and qualitative-based assignments of developed codes as a filtering procedure can methodologically be called an application of triangulation or mixed-method research approach - although on a small scale, based on a pilot sample.

Out of 86 sets of data, the size class of small and medium sized enterprises is represented by 50 sets, and the size class of large enterprises by 33 sets. Three sets of data contained no information in the variable size class. For statistical reasons and for drawing conclusions, it is important to note that large enterprises are overrepresented in this study.

Question 9 of the questionnaire deals with the process of decision-making. Ten pre-defined factors concerning main participants of the decision-process for the first business relation could be answered. In addition, multiple factors could be assigned. Hence, two questions arise: first, which combinations of factors are essential in the decision making process? And second, can new insights be concluded or can a new model of factors be detected? To answer these two methodological questions, an exploratory factor analysis has been executed.

4. Analysis and Results

The analysis, achieved by principal component analysis (PCA), proposes that five new components can explain 71,7% of the total variance in the basic data set, as observed in table 1.

Table 1. Exploratory Factor Analysis: Total Variance Explained (Principal Components Extraction Method)

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 1.865 18.649 18.649 1.865 18.649 18.649 1.683 16.828 16.828
2 1.648 16.484 35.133 1.648 16.484 35.133 1.453 14.528 31.356
3 1.374 13.739 48.872 1.374 13.739 48.872 1.390 13.895 45.251
4 1.180 11.797 60.669 1.180 11.797 60.669 1.376 13.757 59.008
5 1.105 11.052 71.721 1.105 11.052 71.721 1.271 12.714 71.721
6 .898 8.983 80.704            
7 .706 7.057 87.761            
8 .609 6.086 93.847            
9 .366 3.659 97.507            
10 .249 2.493 100.000            

Note: Only cases for Q13 Supercategory: cultural difficulties + no mutual trust + great time delays

Source: Output SPSS 20

New component 1 explains approximately 19% of variance, component 2 explains 16%, component 3 explains 14%, component 4 explains 12%, and component 5 explains 11%. As the Eigenvalues of the remaining five components are below 1, they can be omitted (due to the traditional Kaiser-criterion). Interpreting these new five components (that are latent, unobserved factors) is central. This is the most interesting task of a factor analysis (Bühl, 2016, p. 603). Interpretation is based on the correlation between each of the original variables and the new components. To facilitate interpretation of the new components, the component matrix can be rotated (Backhaus et al., 2011, p. 262). Used method is Varimax, which orthogonally rotates the factor axis. The following table (table 2) presents correlations between each of the original factors and the unobserved, latent and new resulted components.

Table 2. Exploratory Factor Analysis: Rotated Component Matrix, 2016

1 2 3 4 5
F9_Decision: Owner       -.687  
F9_Decision: Management     -.510   .507
F9_Decision: Sales / Marketing -.697        
F9_Decision: Finance/Controling     .821    
F9_Decision: Institutionalized Group of Decision         .894
F9_Decision: Austrian Chamber of Commerce (WKO)   .907      
F9_Decision: External Advisers .732        
F9_Decision: Best Practice in other enterprises       .778  
F9_Decision: Chinese Native Speaking Person .720        
F9_Decision: Chinese Enterprise   .670      

Note: Only cases for Q13 Supercategory: cultural difficulties + no mutual trust + great time delays

Source: Output SPSS 20

To ease the notation of rotated component matrix, small coefficients (less than .40) are not displayed. Component 1 is loading positively high on factors external advisors and Chinese native speaking person and negatively on vice-directors of sales-/marketing department. This new component is named Company-related External Expertise. The second component is loading high on Austrian chamber of commerce (WKO) and Chinese enterprise as part of the decision-making process. Therefore, this new component is named National- or International-related External Expertise.

The third component is loading positively high on the head of the Finance/Controlling department, but negatively on general management; this new component is named purely finance-driven. The fourth component loads positively high on best practice in other enterprises as part of the decision-making process and negatively on the company owner. The fourth component is named adopting what other companies do. The fifth component loads on general management and establishing within the company an institutionalized group of decision. This fifth component is named encapsulated decision processes.

The interdependences between these new and latent assumed components is shown in table 3.

Table 3. Exploratory Factor Analysis: Component Transformation Matrix, 2016

Component 1 2 3 4 5
1 -.686 .400 .494 .215 -.282
2 .611 .576 .383 -.307 -.232
3 .248 .406 -.198 .820 .250
4 -.112 .134 .343 -.223 .896
5 .287 -.570 .673 .372 -.044

Note: Only cases for Q13 Supercategory: cultural difficulties + no mutual trust + great time delays

Source: Output SPSS 20

There are higher correlations between component 1 (Company-related External Expertise) and 2 (National- or International-related External Expertise), between 3 (purely finance-driven) and 4 (adopting what other companies do) and between 4 (adopting what other companies do) and 5 (encapsulated decision processes). Based on PCA factor scores for each case and each variable (now inclusive the new components) can be calculated. To bring foreword details just the first two components are shown, because they are the most important ones, as a result Figure 1 shows a scatter plot between factor scores 1 and factor scores 2.

Figure 1. Scatterplot Factor Score 1 * Factor Score 2, 2016

Source: SPSS 20

Note the accumulation of points in the right upper corner above an imaginary diagonal. This scatter plot affirms an association between component 1 Company-related External Expertise and component 2 National- or International-related External Expertise. Therefore, companies showing influence by factor 1 have also a tendency to show influence by factor 2. Based on the labelling of these two new components, the complementary characteristics, as different kind of expertise, are obvious as well. It seems that the calculated five components build two super-groups: component 1 and 2 as one super-group, component 3, 4, 5 as the other super-group.

Having obtained factor scores for each case, it is advisable to examine if the differences between groups of cases can be detected within the new factor model, as the new five components represent a new factor-model. For this purpose, the data (factor scores for each five new components) are ranked and assigned to two groups where the median is the separating point. These groups can be tested with company-related demographical variables.

No differences could be detected within analysis based on variables such as year of first business or size of the company. However, significant differences as well as considerable effect sizes can be seen for branch variable (economic sector) and components of the second super-group: factor 3, factor 4, factor 5. Calculations by chi-square-tests reveal p-values of .031, .056,.. .012 for the likelihood ratio (level of significance is assigned to .05). Whereas these tests indicate if there is a statistical identifiable difference, calculations of effect sizes show how large or intense the interrelationship is. Hence, it is necessary to calculate corresponding effect sizes. Effect sizes (examine with Cramer’s V) for the second super-group in relation to branch/ economic sector are .303, .291, .346. These effect sizes can be interpreted as such of a medium strength (Cohen 1988, p. 224).

Within the variable branch, three subsets have been defined: production of business-to-business goods like machinery, production of consumer goods, consulting rights- and service-companies. Results of the calculations with factor scores: Companies offering consumer goods are less influenced by components such as purely finance-driven or encapsulated decision processes. Rights and services-companies have a tendency for adopting what other companies do. This last result is shown in figure 2.

Figure 2. Split of branch/ economic sector for component 4

Note: blue = higher ranks of factor scores, green = lower ranks of factor scores,

Source: SPSS 20

5. Conclusions and Discussion

Based on companies which experienced heightened difficulties during or after the market-entry in China, an explanatory factor analysis showed that a new model of factors in the decision-making process can be revealed. This new model comprises of five factors which can be pooled into two super-groups. The first super-group of factors consists of relying predominantly on expertise: Company-related External Expertise and National- or International-related External Expertise. The second super-group of factors consists of companies which, more or less, rely on isolating practices like adopting what other companies do, purely finance-driven or encapsulated decision processes.

It seems to be advisable for managers, especially going into the Chinese market, to avoid these five isolating practices, because as presented in this study, they will confront even more difficulties thereafter than companies adopting a broader approach.

Important limitations of this study are the rather limited number of participating companies and the concentration on one research method (an online survey). Further research should encompass methods like personal face-to-face interviews of management, as time and proximity are needed to build trust between researchers and managers for such a delicate subject like single-tracked decision-making processes.

Appendix 1. Survey Questions with Demographic Purposes

The survey was in German language, because most of the companies / persons working there are Austrians and it included a total of 19 questions. The following questions were aimed at providing a better understanding of general aspects from the companies’ representatives.

1. How many employees did your company have in 2015? Please click on the appropriate answer.

  • 1 – 9
  • 10 – 49
  • 50 – 249
  • 250+


2. In which industry is your company active?

  • Capital goods (for other companies / machines, etc.)
  • Consumer goods (for end users)
  • Services


3. When was the first time your company did business in China?

  • before 1989
  • 1990 - 1999
  • 2000 - 2009
  • 2010 – 2016


4. Who or what was essential in the decision-making process for establishing the first business relationship in China? Please click as many choices that apply.

  • Owner of the company
  • Management
  • Sales
  • Finance / Controlling
  • Decision-making body
  • Austrian Economic Chamber (WKO)
  • External consultants
  • Best practices from other companies
  • A Chinese company


5. What difficulties were encountered in realizing the business in China, for the first time, in terms of business transaction and sales? Please describe your experiences.

  1. Backhaus, K., Erichson, B., Plinke, W. and Weiber, R.,. 2011. Multivariate Analysemethoden. Eine anwendungsorientierte Einführung. !3., überarbeitete Auflage. Heidelberg: Springer.
  2. Bühl, A. 2016. SPSS 23. Einführung in die moderne Datenanalyse. 15., aktualisierte Auflage. Hallbergmoos: Pearson.
  3. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NL: Lawrence Erlbaum.
  4. Jones, M., 1999. The Internationalization of Small High-Technology Firms. Journal of International Marketing, 7(4), pp.15-41.
  5. Li, H., Jin, Z., Li, V., Liu, G. and Skitmore, R., 2013. An entry mode decision‐making model for the international expansion of construction enterprises. Engineering, Construction and Architectural Management, 20(2), pp.160-180, doi: 10.1108/09699981311303026.
  6. Malhotra, N., 2003. The nature of knowledge and the entry mode choice decision. Organization Studies, 24(6), pp. 935-959.
  7. Ninerola, A., Sánchez-Rebull, M. and Hernandez-Lara, A., 2017. Entry modes and barriers to internationalization in China: an overview of management consulting firms. Measuring Business Excellence, 21(1), pp.37-49. doi: 10.1108/MBE-04-2016-0022
  8. Vorobeychik, Y., Joveski Z. and Yu, S., 2017. Does communication help people coordinate? PLoS ONE, 12(2): e0170780. doi:10.1371/journal.pone.0170780

Article Rights and License
© 2017 The Author. Published by Sprint Investify. ISSN 2359-7712. This article is licensed under a Creative Commons Attribution 4.0 International License. Creative Commons License
Corresponding Author
Franz Haas, Faculty of Economics and Management, University of Latvia, Latvia
Download PDF


Franz HAAS
University of Latvia