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Understanding Stakeholder Perspectives on Smart Home Technology Adoption: A Case Study of Mumbai, India

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Energy Informatics (EI.A 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15272))

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Abstract

Smart home technology integrates various devices and systems that upgrade the convenience, security, and efficiency of a home. Due to these benefits, this technology is gaining global attention. In this paper, we have explored the perspectives of the residents of Mumbai on smart home technology adoption. A survey of 425 respondents was conducted, over a period of 3 months, covering demographics, awareness, preferences, concerns, and willingness to adopt. The key factors influencing their willingness to adopt such technology have been identified. The findings reveal insights into the perceived benefits, barriers, and demographic influences on smart home technology adoption in Mumbai. Most respondents show a high level of willingness to install smart home technology. More than 40% of respondents consider security their top priority when thinking about smart home features. Data privacy and security are the top concerns among the respondents, as they are worried about their personal data management and device vulnerabilities. The results are valuable for policymakers, advertisers, and developers working to promote smart home technology in the urban areas of India.

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Notes

  1. 1.

    A Likert scale is psychometric tool used in questionnaires, respondents have to indicate their level of agreement or disagreement with a series of statements. It is used to measure attitudes, opinions and perception [29]

  2. 2.

    Topic modelling is a statistical text-mining method used to uncover hidden structures in a large text dataset. It is a method to identify patterns in text collections and group the texts which show similar patterns. It includes techniques like Latent Dirichlet Association (LDA), it is a method that assumes text documents are a mixture of topics with certain probabilities [30].

  3. 3.

    Terms included in categories for aggregated weights; Security and Control: control and security; Automation and Convenience: automation, convenient, ease, convenience, and automatic; Advanced Technology: smart, technology, AI, internet, and Alexa; Energy Efficiency: energy, efficient, and saving; Interconnectivity: connected, gadgets, and devices; Comfort and Safety: comfort and safety.

  4. 4.

    It is a matrix that represents the frequency of the value pairs in a dataset, used to identify relationships between variables. It is visually represented using a heatmap to highlight patterns and associations using color gradients.

  5. 5.

    It is a statistical test used to measure the difference between observed and expected frequencies in contingency tables. It is used to determine significant associations between categorical variables. A higher chi-square value denotes greater difference, while a lower p-value denotes more significant association, indicating that the observed differences are less likely to have occurred by chance [26].

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Acknowledgments

We extend our heartfelt gratitude to the local people of Mumbai for their invaluable responses, which have significantly strengthened our research study. Your participation and insights were crucial in shaping our findings. Thank you for your support and collaboration. This research was funded by Indorama Ventures Center for Clean Energy, Plaksha University.

Author information

Authors and Affiliations

  1. Center for IT in Building Science, International Institute of Information Technology Hyderabad, Hyderabad, Telangana, India

    Sahil Chilana & Vishal Garg

  2. Indorama Ventures Center for Clean Energy, Plaksha University, Mohali, India

    Aaina Dutta, Vishal Garg & Prakarsh Singh

Authors
  1. Sahil Chilana
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  2. Aaina Dutta
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  3. Vishal Garg
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  4. Prakarsh Singh
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Corresponding author

Correspondence to Sahil Chilana .

Editor information

Editors and Affiliations

  1. University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen

  2. University of Southern Denmark, Odense, Denmark

    Zheng Grace Ma

  3. Universitas Gadjah Mada, Yogyakarta, Indonesia

    Fransisco Danang Wijaya

  4. Universitas Gadjah Mada, Yogyakarta, Indonesia

    Roni Irnawan

  5. Universitas Gadjah Mada, Yogyakarta, Indonesia

    Sarjiya Sarjiya

1. Appendix

1. Appendix

1.1 List of abbreviations

  • 1.CAGR: Compound Annual Growth Rate

  • 2.USD: United States Dollar

  • 3.UK: United Kingdom

  • 4.IoT – SHT: Internet of Things – Smart Home Technology

  • 5.ICT: Information and Communication Technology

  • 6.Ward F/S: Ward F South

  • 7.BHK: Bedroom, Hall and Kitchen

  • 8.INR: Indian Rupee (1 USD = 83.96 INR as of 28–08-2024)

  • 9.DIY: Do It Yourself

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Chilana, S., Dutta, A., Garg, V., Singh, P. (2025). Understanding Stakeholder Perspectives on Smart Home Technology Adoption: A Case Study of Mumbai, India. In: Jørgensen, B.N., Ma, Z.G., Wijaya, F.D., Irnawan, R., Sarjiya, S. (eds) Energy Informatics. EI.A 2024. Lecture Notes in Computer Science, vol 15272. Springer, Cham. https://doi.org/10.1007/978-3-031-74741-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-74741-0_13

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  • Online ISBN: 978-3-031-74741-0

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