Understanding your overseas work force

Sirota, David; Greenwood, Jeremy

doi:10.1002/tie.5060130209

Cited by 42 publications

(37 citation statements)

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“…Empirical support for regional variations in consumer lifestyle, values, attitudes, and consumption behavior is provided by, e.g., Gentry et al (1988), Hawkins et al (1981), Kahle (1986), and Parker and Tavassoli (2000). Empirical support for spatial association in socioeconomic, cultural, attitudinal, and behavioral patterns can also be found in international segmentation research (Askegaard 1993, Kale 1995, Ronen and Kraut 1977, Sirota and Greenwood 1971, Steenkamp 2001, Ter Hofstede et al 1997.…”

Section: Spatial-association Modelmentioning

confidence: 95%

Identifying Spatial Segments in International Markets

2002

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The identification of geographic target markets is critical to the success of companies that are expanding internationally. Country borders have traditionally been used to delineate such target markets, resulting in accessible segments and cost efficient entry strategies. However, at present such ''countries-as-segments'' strategies may no longer be valid. In response to the accelerating trend toward global market convergence and within-country fragmentation of consumer needs, cross-national consumer segmentation is increasingly used, in which consumers in different countries are grouped based on the similarities in their needs, ignoring the country borders.In this paper, we propose new methodology that helps to improve the identification of spatial segments by using information on the location of consumers. Our methodology identifies spatial segments based on consumer needs and at the same time uses spatial information at the subcountry level. We suggest that segments of consumers are likely to demonstrate spatial patterns and develop a hierarchical Bayes approach specifying several types of spatial dependence. Rather than assigning consumers to segments, we identify spatial segments consisting of predefined regions. We develop four models specifying different types of spatial dependence. Two models characterize situations of spatial independence and countries-as-segments, which represent existing approaches to international segmentation. The other two models accommodate spatial association within and spatial contiguity of segments and are new to the segmentation literature. The models account for within-segment heterogeneity in multiattribute-based segmentation, covering numerous applications in response-based market segmentation. We show that the models can be estimated using Gibbs sampling, where for the spatial contiguity model, a rejection sampling procedure is proposed.We conduct an analysis of synthetic data to assess the performance of the most restrictive spatial segmentation model in situations where spatial patterns do or do not underlie the data-generating process. Data for which the true properties are known were analyzed with models of spatial contiguity and spatial independence of segments. The results indicate that a substantial improvement in parameter recovery may be realized if a spatial pattern underlies the data-generating process, but that the spatial-independence model may provide a better alternative when this is not the case.We empirically illustrate our approach in the setting of international retailing, using survey data collected among consumers in seven countries of the European Union. A store image measurement instrument was used. This instrument is based on the multiattribute model of store image formation, with overall evaluations of stores as a dependent variable and image perceptions as predictor variables. The segmentation basis consists of (latent) importances of store image attributes, i.e., product quality, service quality, assortment, pricing, store atmosphere, and...

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Section: Spatial-association Modelmentioning

confidence: 95%

Identifying Spatial Segments in International Markets

2002

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“…In management, country clustering emerged with the seminal work of Haire, Ghiselli, and Porter (1966), followed by Sirota and Greenwood (1971), Ronen and Kraut (1977), Hofstede (1980Hofstede ( , 1991, Schwartz (1999), Smith et al (2002), and GLOBE (House, Hanges, Javidan, Dorfman, & Gupta, 2004), who grouped countries based on the relative similarity of employee attitudes and behavior. Clustering solutions based on work-related values appear in the synthesis of Ronen and Shenkar (1985), but that map is now dated and missing key regions, for example China, that could not be accessed at the time.…”

Section: Introductionmentioning

confidence: 98%

Mapping world cultures: Cluster formation, sources and implications

2013

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This paper extends and builds on Ronen and Shenkar's synthesized cultural clustering of countries based on similarity and dissimilarity in work-related attitudes. The new map uses an updated dataset, and expands coverage to world areas that were non-accessible at the time. Cluster boundaries are drawn empirically rather than intuitively, and the plot obtained is triple nested, indicating three levels of similarity across given country pairs. Also delineated are cluster adjacency and cluster cohesiveness, which vary from the highly cohesive Arab and Anglo clusters to the least cohesive Confucian and Far Eastern clusters. Exploring predictors of cluster formation, we draw on the ecocultural perspective and other inputs, and examine the combined role of language, religion, and geography in generating cluster formation. We find that these forces play a prominent yet complex role: for instance, the religion and language brought by the Spanish fail to create a singular, cohesive Latin American cluster akin to the Anglo cluster. The role of economic variables is similarly considered. Finally, comparing the current map with that of 1985, we find strong support for the divergence (vs convergence) argument. Implications for international business are delineated.

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“…For example, there are similarities in the importance of job facets among different nationalities (Haire, Ghiselli, & Porter, 1966;Sirota & Greenwood, 1971). Other studies (Bigoness & Hofstede, 1987) have shown that the rankings of work goals by importance remained highly similar from country to country.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Haire et al (1966) examined work goal importance by using an 11-item scale, which was later utilized by Redding (1976). Sirota and Greenwood (1971) used 14 work goals, and Ronen and Kraut (1977) listed 22 work goals. However, all of the scales used are based on a modified version of Maslow's (1954) list of categories, which appears to be a reasonable basis for comparison.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Cross-Regional Study of Work Goals in China

2012

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Cluster analysis shows that China can be clustered into three groups in a meaningful way. After clustering China into three groups based on statistical data, we surveyed Chinese employees to examine the work goals differences among the three groups. Chinese employees considered opportunities for promotions and interpersonal relations as important work goals in all three regions. In contrast, job security was considered the most important goal by the employees in region 1 (low economic development and harsh weather), whereas the employees in region 3 (high economic development and less harsh weather) ranked job security as a less important work goal (i.e., the 10th most important work goal).

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Understanding your overseas work force

Cited by 42 publications

References 0 publications

Identifying Spatial Segments in International Markets

Identifying Spatial Segments in International Markets

Mapping world cultures: Cluster formation, sources and implications

A Cross-Regional Study of Work Goals in China

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