PurposeThis study attempts to develop a practical understanding of the positive and negative employee experiences due to artificial intelligence (AI) adoption and the creation of technostress. It unravels the human resource development-related challenges with the onset of Industry 4.0.Design/methodology/approachSemi-structured interviews were conducted with 32 professionals with average work experience of 7.6 years and working across nine industries, and the transcripts were analyzed using NVivo.FindingsThe findings establish prominent adverse impacts of the adoption of AI, namely, information security, data privacy, drastic changes resulting from digital transformations and job risk and insecurity brewing in the employee psyche. This is followed by a hierarchy of factors comprising the positive impacts, namely, work-related flexibility and autonomy, creativity and innovation and overall enhancement in job performance. Further factors contributing to technostress (among employees): work overload, job insecurity and complexity were identified.Practical implicationsThe emerging knowledge economy and technological interventions are changing the existing job profiles, hence the need for different skillsets and technological competencies. The organizations thus need to deploy strategic manpower development measures involving up-gradation of skills and knowledge management. Inculcating requisite skills requires well-designed training programs using specialized tools and virtual reality (VR). In addition, employees need to be supported in their evolving socio-technical relationships, for managing both positive and negative outcomes.Originality/valueThis research makes the unique contribution of establishing a qualitative hierarchy of prominent factors constituting unintended consequences, positive impacts and technostress creators (among employees) of AI deployment in organizational processes.

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includes research articles that focus on the analysis and resolution of managerial and academic issues based on analytical and empirical or case research KEY WORDS Consumer ResponsivenessMobile AdvertisingPurchase Decision Customization CustomerizationIn the current scenario, mobile internet applications enable consumers to access a variety of services: Web information search, SMS (short message service), MMS (multimedia message service), banking, payment, gaming, e-mailing, chat, weather forecast, GPS (global positioning service), and so forth. Collectively, we denominate this wide array of services as "mcommerce." These digital media are considered to potentially improve the possibilities to reach consumers by allowing personalization of the content and context of the message. Combining customer's user profile and the context situation, advertising companies can provide the target customers exactly the advertisement information they desire, not just "spam" them with irrelevant advertisements.Drawing from Nysveen, Pedersen, and Thorbjornsen's (2005) grid of mobile internet services classification, this study attempts to critically analyse "person interactive" (goal-oriented) information and "person interactive" (experiential) messaging, targeting both utilitarian and hedonic benefits from the consumers' perspective. It analyses the effectiveness of mobile advertising in its current format (as prevalent in India). 'Effectiveness' for the purpose of this study has been concretized in terms of impact of mobile advertising on the purchase decision of the consumer. However, results of binary logistic regression indicate that mobile advertising in its current format does not have a significant impact on the purchase decision of a consumer, and that there might be other significant factors like a firm's marketing efforts (marketing mix), a consumers' socio-cultural environment (family, informal sources, non-commercial sources, social class, culture and sub-culture), and an individual's psychological field (motivation, perception, learning, personality, and attitudes) that affect his purchase decision. Mobile advertising in its current format is very generic in its approach, as substantiated by factor analysis performed on the data -marketing communication through mobiles primarily lacked in contextualization and perceived usefulness (for the target customers), and were disruptive in nature. Although mobiles are a powerful mode of marketing communication, the important issues at stake here are-what to say, how to say it, to whom, and how often.

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Service failures in retail banking tend to have a negative impact on service quality and, consequentially, on customer satisfaction. This heightens the need for effective complaint handling. This article attempts to profi le and segment bank customers on the basis of complaining attitudes and perception of complaint handling mechanisms (of banks). Cluster analysis suggested that bank customers can be divided into four segments: non-complainers, switchers, prompt complainers and positive thinkers . Further, AHP has been deployed to prioritize complaint categories for different clusters. This study attempts to provide a roadmap (for banks) for designing effective and customized service recovery strategies.