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This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.

This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.

This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.

The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

This paper aims to analyze the firm level determinants of profitability of Indian drug and pharmaceutical industry which is known for historically weak R&D initiatives.

The change in the economic environment brought out by the Trade-Related Intellectual Property Rights (TRIPS) compliance, this industry was found to have fast adjusted to a new working environment by substantially modifying its strategies. This study aims at using inflation-adjusted panel data for a period 2000-2013 and applies the fixed effects regression model with cluster standard errors.

The study has found that export intensity, A&M intensity, firm’s market power and stronger patent regime dummy have exercised positive influence on profitability. The negative and statistically significant influence of R&D intensity and raw material import intensity points to the need for firms to adopt suitable investment strategies.

The study suggests that firms are required to pay far more attention to optimize their operating expenditures, advertisement and marketing expenditures and improve their export orientation, as part of the long-term strategy.

This study uses a recent data-set to analyze the firm level profitability determinants in the Indian pharmaceutical industry and captures the effect of change in profitability pre and post-TRIPS.

The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.

The new greedy algorithm is proposed to balance the energy consumption in edge computing.

The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.

The results are shown in this paper which are better as compared to existing algorithms.