Exploring Sustainable Leisure Farm with Intelligent of Things (IoT) Technology Solution for Aging

Chun-Min Kuo, Ching-Hsin Wang, Chin-Yao Tseng and Ying-Chen Lo ()
Additional contact information
Chun-Min Kuo: Department of Healthcare Industry Technology Development and Management, College of Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Ching-Hsin Wang: Department of Healthcare Industry Technology Development and Management, College of Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Chin-Yao Tseng: Department of Tourism and Leisure Management, College of Health Sciences, Yuanpei University of Medical Technology, Hsinchu 30015, Taiwan
Ying-Chen Lo: Department of International Business Administration, College of Business, Chinese Culture University, Taipei 1114, Taiwan

Sustainability, 2024, vol. 16, issue 15, 1-18

Abstract: Amid the increasingly severe challenges faced by traditional agricultural development, it has become necessary for farms to undergo operational transformations. In considering the direction of this transformation, the growing proportion of older adults in the population and the maturation of modern smart technologies applied to industries must be taken into account. By integrating intelligent Internet of Things (IoT) solutions to aid business operations, leisure farms are expected to provide significant benefits to both operators and visitors. Taiwan, which has long been a leader in precision agriculture, serves as a benchmark in Asia for the successful transformation of traditional farms into leisure farms, becoming a model for neighboring countries. This study investigates the transformative potential of intelligent IoT technology solutions on leisure farms, highlighting their capacity to attract senior citizens and create sustainable business models in competitive, homogeneous markets. The primary objective of this research is to uncover the advantageous factors associated with the adoption of intelligent IoT technology solutions in leisure farms. Employing a grounded theory approach, this research conducted face-to-face semi-structured interviews with 40 leisure farm operators to gain insights into the innovative and sustainable value propositions of leisure farms. This study identifies six key advantageous factors and six constraint factors. This research provides forward-looking insights into the application of intelligent IoT technology solutions in leisure farms, emphasizing strategic directions for operators. The integration of these solutions presents a unique opportunity for leisure farms to meet the demands of elderly individuals seeking safe, natural environments without compromising their interests. By offering tailored leisure activities and entertainment, these solutions enhance the quality of life of seniors and promote rural lifestyles, positioning leisure farms as innovative and competitive players in the market. The insights provided in this study can also inform government policymakers and serve as a foundation for future researchers to extend related studies from a customer perspective.

Keywords: agritourism; entrepreneurial opportunities; internet of things (IoT) technology; sustainable leisure farms; seniors; grounded theory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/15/6311/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/15/6311/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:15:p:6311-:d:1441381

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().