Revealing the sustainable consumption barriers based on the product-service system: social media analytics approach

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Abstract

Purpose: Given the growing emphasis on environmental consciousness and sustainability as core principles within most companies, product-service systems are recognized as strategic approaches to achieving sustainability objectives. Consequently, understanding consumer acceptance of these systems is of paramount importance. This study seeks to explore users' perspectives on the barriers that impede the adoption of product-service systems, intending to prioritize these obstacles. Design/methodology/approach: This study utilizes a social media-based approach, specifically analyzing tweets related to Zipcar, an American car rental company that exemplifies a usage-oriented product-service system. The analysis identifies the factors influencing the acceptance of this system. The study utilizes topic modeling and sentiment analysis techniques to analyze the tweets. The opportunity value of each topic is determined, aiding in the identification of topics that require improvement. Furthermore, the interrelation between topics is explored, followed by correlation analysis to assess their significance. Findings: Eight topics strongly related to the keywords are identified. Among them, “responsiveness”, “responsibility”, and “trust” hold the highest opportunity values. The findings emphasize the importance of service providers proactively addressing the obstacles that impede consumers' willingness to adopt product-service systems. Prioritization should be given to topics with higher opportunity values. Originality/value: This research uncovers the primary obstacles to adopting the product-service system by directly considering consumer opinions and providing a prioritized list of these obstacles.

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APA

Pourranjbar, A., Shokouhyar, S., Shahidzadeh, M. H., Nikookar, E., Shokoohyar, S., & Pirmoradian, Z. (2024). Revealing the sustainable consumption barriers based on the product-service system: social media analytics approach. Industrial Management and Data Systems, 124(12), 3240–3273. https://doi.org/10.1108/IMDS-03-2023-0199

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