Applying asymmetric, case-based, forecasting modeling in service research: Cultures’ consequences on customers’ service gratuities

• Published in: Australasian marketing journal 2018-11, Vol.26 (4), p.369-381
• Main Authors: Ferguson, Graham, Megehee, Carol M., Woodside, Arch G.
• Format: Article
• Language: English
• Subjects: Asymmetry; Complexity; Configuration; Recipe; Restaurants; Taxicabs; Tipping
• ISSN: 1441-3582; 1839-3349
• DOI: 10.1016/j.ausmj.2018.10.009

This study provides a theory of the influences of alternative national cultures (as complex wholes) on customers’ tipping behaviors following receiving of services in restaurants and taxis. Based on complexity theory tenets, the study constructs and tests models asymmetrically—offers separate models for explaining and forecasting high tipping versus low tipping national cultures. The study uses multiple sources of secondary data for 30 nations including Hofstede's first four culture values, religiosity, Gini index, and GDP_PPP. Model construction includes computing-with-words (CWW) screens that prior theory forecasts to be accurate in identifying high (low) tipping behavior. Analysis includes using fuzzy-set qualitative comparative analysis (fsQCA) and somewhat precise outcome testing (SPOT) of the consistency (degree of accuracy) and coverage for each model. Model testing includes predictive as well as fit validation. The findings support core tenets of complexity theory (e.g., equifinality of different recipes for the same outcome, both negative and positive associations of individual ingredients in different recipes contribute to the same outcome, and causal asymmetry). Because national cultures are complex wholes, hospitality researchers need to embrace the complexity theory tenets and asymmetric tools to achieve deep understanding and for accurately forecasting of customer responses to hospitality services. This study provides new theory and methodological tools for recognizing the complexities and forecasting customers’ behavior in their responses following receiving hospitality services. 本研究提供了一个理论, 说明在餐馆和出租车接受服务后, 另一种民族文化 (作为复杂的整体) 对顾客给予小费的影响. 该项研究基于复杂性理论原则, 不对称地构建并测试模型—提供独立的模型来解释和预测高小费与低小费的民族文化. 该研究使用了30个国家的多种二手数据来源, 包括Hofstede的前四种文化价值观,宗教信仰,基尼指数和GDP_PPP. 模型构建包括文字计算 (CWW) 屏幕, 一种在识别高 (低) 小费行为时比较准确的先验理论预测. 分析包括对一致性 (准确度) 和每个模型覆盖范围的模糊集定性比较分析 (fsQCA) 和一定程度精确结果测试 (SPOT). 模型测试包括预测以及拟合验证. 该研究结果支持复杂性理论的核心原则 (例如, 对于相同结果的不同配方的等同性,不同配方中各个成分的负面和正面关联导致相同结果, 以及因果不对称). 由于民族文化是复杂的整体, 酒店研究人员需要使用复杂性理论原则和不对称工具, 从而能够深入了解并准确预测客户对酒店服务的反应. 本研究提供了新的理论和方法工具, 用于识别顾客在接受酒店服务后的反应中的复杂性, 并预测他们的行为.