主题:Exploring Online Help-Seeking Tendencies: The Influence of Experience Type and Helper Identity
中文题目：探索在线求助倾向：经验类型与帮助者身份的影响
时间: 2025年12月30日 下午2:00 – 3:30
地点:管理学院105讨论室（二案）
主讲人:李少波，副教授，博导，南方科技大学
Bio:
Dr. Li Shaobo is a tenured Associate Professor (Research Fellow and PhD Supervisor) at the Business School of Southern University of Science and Technology (SUSTech) and a recipient of a national-level young talent program. He received his PhD in Business Administration from Nanyang Technological University, Singapore, in 2019. His primary research focuses on social well-being in the digital era. His research has been published in UTD24 top-tier international journals, including Journal of Consumer Research, Management Science, MIS Quarterly, Information Systems Research, and Production and Operations Management. He currently serves as an Associate Editor of the Journal of Business Research (JBR). Dr. Li has led more than ten national- and provincial-level research projects, including grants from the National Natural Science Foundation of China, and has participated in the Ministry of Science and Technology’s “2030” Major Science and Technology Project under the National Key R&D Program. His academic work has received over ten awards, including the AMS Best Doctoral Dissertation Proposal Award.
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Abstract:
Businesses increasingly provide online assistance through help features to enhance user experiences in the digital era. Understanding factors influencing users’ tendencies to seek online help is crucial for optimizing resources and improving support and overall user experience. This paper examines the impact of experience type and helper identity on users’ online help-seeking behavior. Across multiple studies, we found that users are more (vs. less) inclined to seek help when encountering difficulties in utilitarian (vs. hedonic) experiences. This pattern was driven by users’ greater focus on achieving specific outcomes in utilitarian contexts, in contrast to their emphasis on experiential enjoyment in hedonic contexts. Importantly, users showed a stronger preference for seeking assistance from human service staff over service robots when facing challenges in utilitarian experiences. Nevertheless, during hedonic experiences, no notable difference emerged in their inclination to seek help between human staff and service robots. These findings highlight the importance of considering experience type and helper identity when planning online support services, contributing valuable insights to the literature on hedonic versus utilitarian motivation systems, users’ online help-seeking behavior, and user experience.