Young adults’ experience of housing and real estate chatbots in India: effort expectancy moderated model

Abstract

Purpose This study aims to recognize the role of information system (IS) model on young adults’ experience of housing and real estate chatbots. This model of IS takes into account the quality of information, the quality of system and the quality of service. Design/methodology/approach This study uses a sample frame for analysis which comprises young adult population in India, i.e. between the ages of 18 and 35. A questionnaire consisting of five components was used to collect information in a structured manner. The 386 responses thus collected were analysed using the structural equation model. Findings It was found that there is a significant influence of the quality of information, quality of system and quality of service on young adults’ experience of housing and real estate chatbots. The findings also showed that there is moderation role of effort expectancy between the quality parameters and young adults’ user experience of housing and real estate chatbots. Research limitations/implications This study focusses exclusively on the young adults from various parts of India. Future research can consider larger population categories across age groups and across sectors employing chatbots. Practical implications This study will enable in-depth understanding of IS model – quality dimensions’ relation with the user experience. In particular, housing and real estate organisations will profit from the expanded usage of artificial intelligence through chatbots for user correspondence and communication. Originality/value To the best of the authors’ knowledge, this study is first of its kind, as it investigates how IS model – quality dimensions affect the young adults’ experience of housing and real estate chatbots in India. This study also ventures into identifying the moderation role of effort expectancy between the quality dimensions as per IS model and young adults’ experience of housing and real estate chatbots. This study will be useful for the stakeholders of housing and real estate industry.

Does individuals' age matter? A comparative study of generation X and generation Y on green housing purchase intention

“…As the same respondents measured all variables simultaneously, the authors employed established statistical approaches to evaluate common method bias. Following the recommendation of James et al (2023), Harman's single-factor test was applied to study the existence of CMB in the data. The results depict that the first factor accounted for only 30.12% of the variance below 50%, suggesting that CMB was not a significant concern.…”

“…As the same respondents measured all variables simultaneously, the authors employed established statistical approaches to evaluate common method bias. Following the recommendation of James et al. (2023), Harman’s single-factor test was applied to study the existence of CMB in the data.…”

“…Although green housing is a holistic approach to a home's design, development and operation, with the initial aim of conserving water, energy and building material at each stage of a house's life cycle, the extant literature on house-buying behavior is still at an initial growth stage (Kaynak et al, 2022). Prior research on green housing mainly focused on young adults' experience using chatbots in green housing (James et al, 2023), green building housing products (Huang, 2022) and real estate investment decisions (Kabir et al, 2023). Despite this, Jacob et al (2023) found a need to clarify the factors influencing purchasing and promoting green homes.…”

Factors Affecting Customer Readiness to Trust Chatbots in an Online Shopping Context

“…Almaiah et al (2022) have applied the innovation diffusion theory within the online learning domain. James et al (2023) have applied the information systems success model to examine users' experience of housing and real estate chatbots. Ashfaq et al (2020) combine the technology acceptance model with the information system success model and the expectationconfirmation model to examine users' continuance intention to use chatbot customer service.…”