The Impact of Country-of-Origin and Joy on Product Evaluation: A Comparison of Chinese and German Intimate Apparel

Abstract

Consumer's differentiated perception and appraisal of a product's country-of-origin (COO) exerts large influence on perception of the COO-image (COI) and on the perceived product quality.At peripheral information processing COO-Image (COI) provides a basis to ease requirements for information processing in form of heuristics.Most of internationalization literature examines the COO effect with respect to high-involvement products and in context of cognitive processing. However studies from Maheswaran and Chen (2006) point out, that besides cognitive influences, also specific emotions impact on COO-effects. Whereas the influence of negative emotions like anger, sadness or frustration on processing and judgments could be exposed, the issue of positive emotions' impact still stays open.Since the question should be answered, if emotions impact on COO-Effects, it is emphasized to focus on COO as an affective heuristic operating as a "halo" effect on attitudes towards a product. Because both marketing and evolutionary theories of emotion act on the assumption of satisfaction of human needs, latter is specified, describing emotions as complex chains of events with stabilizing feedback loops. Particularly, in the context of impulsive purchasing emotion plays a vital role. Impulsive buyers are more emotionalized, experiencing more enthusiasm, joy, and glee than nonbuyers. Thus "joy" leads to impulsive decision making behavior.Drawing on the extant literature, we develop our conceptual framework to analyze whether, and if so, how positive emotions (such as joy) affect the impact of COO on product evaluation. Based on an experimental study including 130 respondents, we test three hypotheses: whether the COO has an impact on product evaluation (H1), whether the positive emotion of joy has a stronger positive influence on product

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“…Geisser (1975) and Stone (1974) stated that "predictive relevance is evaluated using the Q 2 test", as it shows the competence of the model and its parameters (Ommen et al, 2010) in reconstructing the observed values. Smart PLS v.3 was used to get the Q 2 value by "blindfolding one case".…”

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