Abstract
The research design and research methodology of this work is oriented on the theoretically derived latent variables of the regional success factor model, and its respective measurement models, which we presented in the previous chapters. For the measurement of these latent constructs, in addition to utilizing secondary data, the use of primary data is highly encouraged to improve methodological rigor (Bergh et al. 2006: 91; Yang et al. 2006: 603) – as this contributes to avoiding a common method bias (Chang et al. 2010: 179; Homburg and Klarmann 2009: 149; Podsakoff et al. 2003: 882). Due to the fact that methodological rigor – according to our explanations in Sect. 2.4.1 – represents a necessary condition for achieving “high” rigor, we will apply both data sources in this work. Here, secondary data is related to the more “objective” firm-level data, whereas primary data focuses on the “subjective” experimental, cultural, and knowledge (or information) related variables (Buckley et al. 2007: 1071; Hult et al. 2008a: 1066; Venkatraman and Ramanujam 1986: 804). In this work, the former is given by a database with firm-level information about MNCs, whereas the latter corresponds to a survey-based inquiry of these firms. In the following chapter, we will present this database of our research sample – before we will outline in the subsequent chapter the research methodology for our survey-based research, as well as for the explorative analysis and modeling of our data.
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Notes
- 1.
- 2.
Cf. Sect. 2.1.2.4.
- 3.
Cf. Sect. 1.2.
- 4.
Cf. Sect. 2.4.2.
- 5.
Alternatively, each MNC’s financial year could be portioned to the base period of the research sample (January–December). This would imply, for example, splitting an annual report ending on 31 March into two periods from 1 January–31 March and 1 April–31 December, allocating the former to the previous year and the latter to the actual reporting period. This would not be reasonable, as the annually reported financial statements of a MNC are a cohesive presentation of their situation according to the financial accounting rules relevant at the respective closing date, and is thus not necessarily comparable with its annual report of the previous or next financial year. This is illustrated by the frequent restatements of financial data from one reporting period to another. We considered such effects from restatements by collecting the reported data for each firm in a reverse order – starting with 2008, then 2007, and so on, to improve the longitudinal comparability of each MNC’s reported information.
- 6.
This is due to the fact that most MNCs, or 587 firms, of our research sample published their financial information in millions.
- 7.
Differences in the data reported by these MNCs on their employees included distinctions of year-end and average numbers – each on a full-time equivalent basis – as well as different terminologies (e.g., headcount, personnel, or staff). As most of the MNCs in our research sample reported year-end figures, these differences were not material and thus were not further differentiated in our database.
- 8.
Cf. Sect. 6.1.
- 9.
If a MNC published its financial statements according to both a local accounting standard (e.g., Australian GAAP) and a more international standard (e.g., US GAAP), then the information based solely upon the more international reporting standard (here US GAAP) was utilized for comparability reasons.
- 10.
Cf. Chap. 6.
- 11.
It should be noted that in this analysis – by drawing on Rugman and Verbeke’s (Rugman 2005b: 3–4; Rugman and Verbeke 2008b: 311) approach for assessing regional success, as outlined in Sect. 4.1.4 – we investigate different measures of home-regional success. More specifically, we calculate for all of our 663 sample firms the average values of their relative shares of home-regional sales and assets – leading to nine figures for these two regional success metrics over our sample period. This methodology for assessing home-regional success of MNCs is different from the classification of home-regionally successful firms, which we presented before – where the regional success of each company is evaluated separately by Rugman’s (2005b: 4) classification scheme according to the 50% home region and 20% host region thresholds. In the latter approach, the number of those MNCs that are classified as being home-regionally successful is counted – which also leads to nine figures for both the relative amount of sales-based and assets-based home-regionally successful firms. This shows that the respective nine figures derived by the former and the latter methodology have a different meaning. The latter approach takes a broader view on the relative importance of firms being classified as home-regionally successful vis-à-vis alternative regional success classifications – which corresponds to the methodological basis of the very early influential work of those academic scholars that discovered the phenomenon of the dominance of home-regional success (Rugman 2003b: 412; Rugman 2005b: 4; Rugman and Verbeke 2004: 7). The former approach concentrates specifically on the home-regional success patterns of MNCs – which was applied more recently, to further exclusively explore the development of this dominant form of regional success over time (Oh 2009: 341; Rugman and Oh 2007: 36–37; Rugman and Verbeke 2008c: 328). In this work, we apply both approaches in the same sequence as these IB scholars – following its underlying logic from broad to specific – as both methodological techniques look at the same phenomenon in different ways, and thus contribute to our understanding of the multi-facetted nature of the (home-)regional success of our sample firms.
- 12.
A subtraction of the values of HRS/TS and HRA/TA from a 100% leads to rest of world sales and assets, respectively (Rugman and Oh 2007: 36–37).
- 13.
Cf. Sect. 4.1.4.
- 14.
Cf. Sect. 2.2.8.
- 15.
The home-regional investment quota is calculated by dividing the MNC’s home-regional investments by its sales revenue in the home region. This illustrates the portion of sales that MNCs invest in their home region.
- 16.
Cf. Sect. 4.1.4.
- 17.
Cf. Sect. 1.2.
- 18.
The highest value of 95% for the 50% home region threshold seems reasonable, to include MNCs at a very high level of home-regionalization as well as the 100%-HR firms. To distinguish these 100%-HR firms from the other home-regional MNCs, the 10% cut-off point appears adequate – given the accounting rule outlined above, according to which MNCs have to report their geographical activities in foreign host regions that exceed 10% of the firm’s total amount, or value, of the respective geographical segment reporting data.
- 19.
The methodology for our survey-based inquiry of the Fortune Global 500 firms will be introduced in the next chapter.
- 20.
To differentiate regional strategies form one another, we utilize Rugman’s (2005b: 4) sales-based classification of regional strategies according to the 50% home region and 20% host region cut-off points. The selection of this classification scheme is mainly due to its prevailing application in the IB literature, which ensures the comparability of our results with other studies in this field.
- 21.
Given their importance for our regional success factor model of Fig. 4.6, we will devote particular attention to regional management autonomy, regional product/service adaptation, regional orientation and inter-regional distance in this analysis of industry-specific differences.
- 22.
Cf. Sect. 2.3.2.1.
- 23.
These four elements of the MNCs’ geographical segment information were chosen, as they are also published by the magazine Fortune (2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009) on a consolidated basis. This allows an examination of their overall plausibility – which we perceived as being necessary here, given the importance of this analysis.
- 24.
We will show in Sect. 6.1 that – from 2004 to 2008 – most MNCs prepare their financial statements according to IFRS, US GAAP, or Japan GAAP. The focus on corporate sales revenue and profits is based on Rugman’s (2005b: 231) definition of corporate success, as a combination of sales-based success and the profitability of a MNC.
- 25.
Cf. Sect. 6.3.
- 26.
Cf. Sect. 1.2.
- 27.
- 28.
Cf. Sect. 2.1.2.1.
- 29.
The resulting key informant bias will be controlled by various measures, which we will describe below in the presentation of the outline and the pretest of our survey.
- 30.
For creating our online questionnaire, we utilized the online survey software provided by SurveyMonkey (http://www.surveymonkey.com). The selection of this web-based software was mainly due to its advantages for contacting the MNC managers of our research sample, given for example by design aspects (such as optical appeal, seriousness) and its functionality (such as progress bars).
- 31.
In this company report, the specific results of the responding MNC are compared with the average results of the survey as a whole.
- 32.
As explained in Sect. 5.1.2, the control variable firm size will be measured by secondary data.
- 33.
In our regional success factor model, as outlined in Sect. 4.1.4, regional success will be measured solely by means of objective data. However, to draw proper conclusions on the quality of regional performance measurement (Hult et al. 2008a: 1072), subjective estimations of regional success are also assessed to validate the secondary data of our database (Meffert et al. 2008: 159). In assessing this criterion validity of our success measurement (Fritz 2004b: 29), we focus on the subjective estimations of regional sales revenue and profit generation, which – according to Rugman (2005b: 231) – are the most important indicators of a MNC’s performance.
- 34.
These different coordination mechanisms are assessed by the decision-making structures of MNCs. Here, directive-regional decision-making usually implies that the parent headquarters takes most decisions about regional products/services. In adaptive-regional decision-making, generally the regional management center(s) develop(s) many products/services for the national subsidiaries within the region. A manifestation of a trans-regional (world mandate) coordination mechanism is that the regional management center(s) develop(s) many products/services for the national subsidiaries outside the region. Furthermore, the establishment of cooperative-regional decision-making structures typically leads to the fact that the national subsidiaries in the region participate strongly in the development of regional products/services within the region.
- 35.
Measures of a MNC’s regional commitment include its emphasis placed upon management continuity (e.g., continuity of the senior management team), market-oriented investments (i.e., resource commitments to attract potential foreign customers such as sales offices, plants, R&D centers), and its staying power (i.e., staying in a region while it faces periods of difficulty, such as economic downturns). Cf. Sect. 2.1.2.3.
- 36.
A MNC’s management levels were differentiated by the reporting structures within the firm (Stopford and Wells 1972: 10), which led to the distinction of senior management – those MNC managers that form part of the Board of Management or directly report to the Board of Management – and middle management with no direct reporting to the Board of Management.
- 37.
It should be noted that multi-informant designs can also lead to measurement problems (Homburg and Klarmann 2009: 148). Due to the fact that usually only very few respondents in the MNCs of our research sample – apart from the Board of Management and the corporate strategy department – are deeply involved in the conceptualization of the firm’s regional strategies, a key informants approach appears to be more reasonable for the research of this work (Fritz 2004b: 26–27, 30).
- 38.
- 39.
In the case of the five-point Likert scale, a “don’t know” field – whereas for closed questions an “other” field – was also added. To better understand the reasoning behind selecting these answer choices, for both questioning techniques, we asked the respondents to specify reasons or to provide additional details for choosing these answers. These scales of our survey have been developed for the purpose of this work and have not been utilized before.
- 40.
The respective test results – relating to the \( {p_{sa}} \) index of the indicator’s proportion of substantive agreement, and to the \( {c_{s\upsilon}} \) index of its substantive-validity coefficient – will be presented in more detail in Sect. 6.3.
- 41.
We conducted our pretest over 6 weeks from the end of February 2009 to the beginning of April 2009.
- 42.
The six academic experts included Alan M. Rugman of the University of Reading (UK), Alain Verbeke from the University of Calgary (Canada), André Sammartino from the University of Melbourne (Australia), Michael Behnam from Suffolk University in Boston (MA, USA), and Dirk Ulrich Gilbert and Jürgen Kähler from the University of Erlangen-Nürnberg (Germany). The cultural background of these academic experts relates to Asia, Europe, and North America – which represent the most frequently encountered regions of our research (cf. Sect. 6.1). This is an important condition for the cross-cultural application of our survey, for which we aim to ensure that the numbers on the response scales, or the items that the respondents are responding to, have the same meaning across cultures (Hult et al. 2008b: 1036). In addition, the six practitioners were comprised of two heads of corporate strategy in each the manufacturing and the service sector, as well as one director and one associate principal of McKinsey & Company. The high cross-sectional experience and competence of these practitioners regarding regional strategies of MNCs appeared necessary to ensure that our survey can be applied to the diverse industries of our sample firms. Furthermore, the six methodological experts included German professionals in survey-based research from the Gesellschaft für Konsumforschung (GfK) and TNS Infratest GmbH, as well as other questionnaire specialists and PLS modeling experts.
- 43.
For example, our pretest participants advocated a change in the order of regional management autonomy and regional product/service adaptation, as a beginning with the latter area of interest was perceived by our pretest participants as facilitating the respondents’ involvement with the theoretical concepts of the survey.
- 44.
Concerning the inquiry techniques, some pretest participants observed that respondents could also strongly agree to some of the reversely coded survey statements – even though here we would expect a disagreement. Consequently, to avoid such wrong implications of our control questions, we decided to directly ask the respondents, if we would encounter such ambiguous cases – where they answered to the reversely coded statements in the opposite direction of our expectation – in the returned responses to our final survey.
- 45.
Due to the fact that also non-home-regional MNCs (e.g., a bi-regional or tri-regional MNC) may be most successful in their home region, for these MNCs we included the possibility to complete the questionnaire for their home region. It only had to be assured that the respondents of our sample firms complete the survey for only one, or more specifically, their most successful region.
- 46.
Throughout all these adjustments, particular attention was paid to the total response time of the survey, which should not exceed a maximum of 15 min. Furthermore, by means of a professional proofreading service, the linguistic accuracy of the English survey was confirmed.
- 47.
The contact details of the respondents mostly consisted of their direct email addresses (in 633 cases). Mainly due to their company policy, 30 respondents did not want to give out their email address and preferred either the contact form on their firm’s webpage or a fax for the transmission of the survey.
- 48.
The transmission of our survey was always accompanied by a brief email-based cover letter and the link to our online questionnaire. The content of this cover letter was largely identical to the first page of our survey – illustrated in Fig. A.1 – including a deadline for completing the questionnaire within 4 weeks after initial transmission.
- 49.
From 24 April 2009 to 8 June 2009, we sent our survey to 621 MNCs, and from 4 August 2009 to 4 September 2009 to another 42 firms of our sample. our questionnaire was due to the fact that the Fortune Global 500 firms for the year 2008 were published by the magazine Fortune (2009) on 20 July 2009 – thus after our initial survey transmission phase. We followed the same procedure as in the earlier phase – including the direct contacting of our respondents and the number of remainders. The response rate of 23.8% for the later responding 42 MNCs was even above the response rate of 16.7% for the earlier 621 responding firms. This might be due to the fact that during our first transmission period, many MNCs were preparing their annual financial statements – leading to time constraints for the participation in our survey. However, regarding the content of their survey responses, we could not find any systematic differences between early and late respondents.
- 50.
The implausibility of responses was assessed by reversely poled items and by an overall judgment of the answers. To understand the reasons for not completing the survey and for the encountered implausible answers, each of the respondents for these 18 sample firms was contacted, before we eliminated their responses. The main reasons for not completing the questionnaire were time constraints or company policy matters (e.g., no participation in strategy-related surveys).
- 51.
A “rest-regional” orientation of a MNC results for example, if a substantial part of the firm’s revenue is derived from the “rest” category in its geographical segment reporting. Such “rest-regional” orientations of MNCs can be explained by their orientation to emerging markets – such as the BRIC countries, Brazil, Russia, India, and China – if the corresponding financial data was allocated to the “rest” category in their geographic segment reporting, instead of one of the five regions that we outlined before in Sect. 5.1.1 (Banalieva and Santoro 2009: 347).
- 52.
The number of missing values increased considerably by the lack of annual reporting information on the regional profitability of our survey sample firms – which was published by only 54 of these MNCs.
- 53.
One of these 36 survey respondents completed the questionnaire for a foreign region that was not declared in an isolated form in the respective firm’s annual reports. Based on an analysis of the financial statements of this MNC, we utilized the figures of the reported “other” figure in its geographical segment reporting as the best possible approximation of its regional success in this particular foreign region.
- 54.
Regarding multicollinearity, we evaluated the VIF values of all sets of formative indicators in SPSS (2009) – where all VIF values were lower than their limit value of 10 (Giere et al. 2006: 687), as outlined in Sect. 3.2.3.2. By applying an even more conservative approach – assuming that any VIF substantially greater than one indicates multicollinearity (Henseler et al. 2009: 302) – we were able to ensure that no multicollinearity exists in this modeling alternative (given for example by the fact that no indicator weight loadings were greater than one). The underlying indicator data was checked mainly regarding the comparability of the applied objective performance data – as explained in Sect. 5.1.2 – and the appropriateness of the subjective responses to reversely coded questions. Here, no discernible implausibilities in the underlying indicator data could be found.
- 55.
An indexation of the formative indicators for each of these four first-order dimensions is possible, as they fully capture the respective constructs’ domain of content – and as they share a common meaning, given by changes in the degree of regional management autonomy (Diamantopoulos et al. 2008: 1212; Diamantopoulos and Winklhofer 2001: 271–272).
- 56.
We calculated the factor scores after having ensured that no multicollinearity exists between the formative indicators. Following our explanations before, this was achieved by analyzing VIF values on the basis of SPSS (2009) – assuming that any VIF substantially greater than one indicates multicollinearity (Henseler et al. 2009: 302). By means of these analyses, we could identify two formative indicators (the coordination of regional legal entities and operations, and the regional liaison center for the parent company) which – to avoid effects from multicollinearity, such as for example indicator weight loadings greater than one – we did not use in the calculation of the factor scores.
- 57.
As a rule of thumb, sample size in PLS should at least be equal to the larger of the following: (1) five times the scale with the largest number of formative indicators (the scales for constructs specified in a reflective manner can be ignored); or (2) five times the largest number of structural paths directed at a particular latent variable in the structural model (Chin et al. 1996: 39; Sosik et al. 2009: 15; Tabachnick and Fidell 1989: 129). Here, the first criterion of the rule of thumb applies to the case at hand, resulting in the minimum number of 20 observations for a proper modeling the regional success factor model of Fig. 4.6.
- 58.
The disadvantage of utilizing mean values is given by the fact that the influence of the non-explained variance, or the measurement error, is not explicitly included in the estimation of model parameters, which may lead to biases in the estimates (Albers and Götz 2006: 674; Homburg and Baumgartner 1995: 1092, 1102–1103).
- 59.
This two-step procedure has also been described by Chin et al. (2003: Appendix D).
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Heinecke, P. (2011). Research Design and Research Methodology. In: Success Factors of Regional Strategies for Multinational Corporations. Contributions to Management Science. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2640-1_5
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