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In the modern increasingly competitive milieu of cause marketing activities, both profit and nonprofit organizations expect their advocation of prosocial programs to gain the support of target customers. Previous research shows the effect from adding participant's personal attributes or social influence factors. This study considers the effects of benefit incentives and cost/reward influences to enhance prosocial behaviors.

Three between-subject experiments were conducted and SPSS Statistics ANOVA was employed to analyze the experimental results.

Rewarding time delays and prosocial efforts have no significant impact on the relationship between other-benefit incentives and willingness to engage in prosocial behaviors, but do significantly impact the self-benefit incentives condition. However, the negative effect of self-benefit condition can be mitigated by high rewards.

Since prosocial campaigns proposed by organizations in this study include both profit and nonprofit organizations, perhaps two category organization types should be attentively classified to evaluate the effects.

Under social marketing campaigns with self-benefit incentives conditions, the empirical findings of this study show that profit and nonprofit organizations can provide higher reward values to mitigate the adverse effects of high participating costs.

Social marketing campaigns with other-benefit incentives are less affected by high participating costs and highlight the value of altruism.

This study provides valuable suggestions for both profit and nonprofit organizations to use self-benefit/other-benefit incentives under cost related factors influence to encourage customers' prosocial behaviors.

This paper aims to advance a configural asymmetric theory of the complex antecedents to hospitality employee happiness-at-work and managers’ assessments of employees’ quality of work performance. The study transcends variable and case-level analyses to go beyond prior statistical findings of small-to-medium effect sizes of happiness–performance relationships; the study here identifies antecedent paths involving high-versus-low happy employees associating with high-versus-low managers’ assessments of these employees’ performances.

The study merges data from surveys of employees (n = 247) and surveys completed by their managers (n = 43) and by using qualitative comparative analysis via the software program, fsQCA.com. The study analyzes data from Janfusan Fancyworld, the largest (in revenues and number of employees) tourism business group in Taiwan; Janfusan Fancyworld includes tourist hotels, amusement parks, restaurants and additional firms in related service sectors.

The findings support the four tenets of configural analysis and theory construction: recognize equifinality of different solutions for the same outcome, test for asymmetric solutions, test for causal asymmetric outcomes for very high versus very low happiness and work performance and embrace complexity.

Additional research in other firms and additional countries is necessary to confirm the usefulness of examining algorithms for predicting very high (low) happiness and very high (low) quality of work performance. The implications are substantial that configural theory and research will resolve perplexing happiness–performance conundrums.

The study provides useful case-level algorithms involving employees’ demographic characteristics and their assessments of work facet-specifics which are useful for explaining very high happiness-at-work and high quality of work performance (as assessed by managers) – as well as algorithms explaining very low happiness and very low quality of work performance.

The study is the first to propose and test the tenets of configural theory in the context of hospitality frontline service employees’ happiness-at-work and managers’ assessments of these employees’ quality of work performances.

This chapter describes and tests the principles of configural theory in the context of hospitality frontline service employees’ happiness-at-work and managers’ assessments of these employees’ quality of work performances. The study proposes and tests empirically a configural asymmetric theory of the antecedents to hospitality employee happiness-at-work and managers’ assessments of employees’ quality of work performance. The findings confirm and go beyond prior statistical findings of small-to-medium effect sizes of happiness-performance relationships. The method includes matching cases of data from surveys of employees (n = 247) and surveys completed by their managers (n = 43) and uses qualitative comparative analysis via the software program fsQCA.com. The findings support the four principles of configural analysis and theory construction: recognize equifinality of different solutions for the same outcome; test for asymmetric solutions; test for causal asymmetric outcomes for very high versus very low happiness and work performance; and embrace complexity. The theory and findings confirm that configural theory and research resolves perplexing happiness–performance conundrums. The study provides algorithms involving employees’ demographic characteristics and their assessments of work facet-specifics which are useful for explaining very high happiness-at-work and high quality-of-work performance (as assessed by managers) – as well as algorithms explaining very low happiness and very low quality-of-work performance.

This chapter describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e., 2–7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g., high A, high B, and low C) – and low X is an indicator of high Y as well when low X combines in other recipes (e.g., high A, low R, and high S), where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities – configural analysis – is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.