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PurposeThe purpose of his paper is to define the role of cyber‐intermediaries based on several theories such as transaction cost analysis, agency, social exchange, and relationship marketing.Design/methodology/approachThis is a conceptual piece that uses arguments from well‐established theories in marketing and managementFindingsThis paper suggests that cyber‐intermediaries will continue to add value to the producer‐consumer chain, benefiting both the producers and the consumers.Practical implicationsThe nature and type of products and services will define the role of the intermediary. They may be more useful in the low cost but frequently purchased product categories.Originality/valueThe paper presents an integration of existing theories to understand the role of cyber‐intermediaries.

This study examines two issues which have challenged prior experimental or survey research: (1) whether the time-varying effects of service recovery on customer satisfaction may follow a long decay or short decay and (2) why and what service recovery efforts have a higher and quicker buildup, with respect to the significance and timing of recovering customer satisfaction losses due to service failures. The authors do so with a real-world data set from China's mobile phone markets. The authors developed multivariate time-series model to simulate the dynamic service recovery process and implemented Bayesian estimation to resolve overparameterization problem. The empirical results surprisingly reveal that apology-based service recovery efforts are the least effective in salvaging customer satisfaction, with the shortest decay and lowest buildup intensity. In contrast, quality improvement is the most effective, with the highest buildup and longest decay but slowest buildup toward the peak impact point. Compensation has moderate and stable impact overtime. Communications' impact on customer satisfaction builds up the quickest, though with mild endurance and magnitude. Also, the decomposition models enable managers to monitor how many percentages of customer satisfaction gains are originated from which types of service rescue efforts.

Focusing on three input variables (i.e., government efficiency, business efficiency, and infrastructure advancement) and one output variable (i.e., economic performance), we employ the Data Envelopment Analysis (DEA) technique in an attempt to make an efficiency comparison between the developed economies (i.e., OECD countries) and the less developed economies (i.e., non-OECD countries). Among the studied sample nations, Indonesia and Argentina are found to outperform other nations across all efficiency measures. Our findings also reveal a significant difference between the OECD and the non-OECD countries in terms of the scale efficiency and overall technical and scale efficiency . We recommend further studies to incorporate additional inputs/outputs (e.g., risk factor) in the DEA model and find innovative ways and means to facilitate the cross-country benchmark process.