Abstract:

This study addresses the demand for high-quality economic development and is at the forefront of advancements in digitization and intelligent technology. It aims to uncover how intelligent data analysis dashboards (machines) and sellers (salespeople) collaborate to create value for multiple parties (sellers, buyers, and platform firms) in the context of human-machine collaborative selling. The study categorizes intelligent data analysis into descriptive (what happened), diagnostic (why it happened), predictive (what will happen next), and prescriptive (what should be done about it) types. It explores the following three research questions: (1) Under an exchange relationship orientation, what types of intelligent data analysis, when aligned with specific seller capabilities, can drive changes in seller behavior and enhance short-term transactional outcomes between sellers and buyers? (2) Under a communal relationship orientation, what types of intelligent data analysis, when integrated with specific seller capabilities, can improve buyer experiences and improve the long-term relationship quality between sellers and buyers? (3) How can platform operators, considering differences in market environments, determine the appropriate relationship (exchange relationship vs. communal relationship) orientation of intelligent data analysis to effectively balance short-term transactional outcomes and long-term relationship quality, thereby creating value for themselves?

The main innovations of this research are threefold. First, building on the human-machine collaboration research, this study is the first to explore how intelligent data analysis and seller capabilities can collaborate to create value for sellers, buyers, and platform operators. Specifically: (1) In terms of the focus on human-machine collaboration, existing research primarily compares the effects of humans, machines, and human-machine collaboration, demonstrating the necessity of the collaboration. In contrast, this study explores how humans and machines can collaborate, focusing specifically on augmented collaboration (i.e., how machines can augment human decision-making). It addresses the important question of what kind of seller capabilities are needed to align with different types of intelligent data analysis to create value. (2) In terms of research context, existing empirical studies mainly focus on customer service scenarios and sales-related contexts (e.g., sales training and recruitment). This study, however, examines a new context—how human-machine collaboration can drive sales conversion—offering fresh insights into human-machine collaboration research.

Secondly, this study systematically examines the value of four types of intelligent data analysis: descriptive, diagnostic, predictive, and prescriptive. It provides new theoretical insights into data analysis research: (1) In terms of data analysis, only two studies have focused on descriptive data analysis. This study not only considers descriptive analysis but also explores more intelligent forms of analysis, including diagnostic, predictive, and prescriptive analytics. (2) Regarding the impact of data analysis tools on stakeholders, existing research has primarily examined their effects on tool users (e.g., sellers), while this study expands the scope to include other stakeholders (i.e., buyers and platform operators). (3) In terms of the focus on the effectiveness of data analysis tools, previous research has primarily concentrated on the standalone effects of data analysis, with little attention given to the collaborative interaction between data analysis and human capabilities. This study addresses this gap by introducing seller capability characteristics and exploring how intelligent data analysis tools can work synergistically with human capabilities.

Finally, from the platform operator's perspective, this study innovatively focuses on intelligent data analysis as a decision-enhancing tool for sellers, and identifies its role in facilitating seller-buyer interactions within the value creation process of transactional platform operators. Existing research has focused on four aspects: attracting both supply and demand sides, facilitating seller-buyer interactions, achieving effective matching between the two, and platform governance. Regarding facilitating seller-buyer interactions, while scholars have examined the value of transaction mechanism design and various information communication tools, no research has yet focused on the role of intelligent data analysis tools for sellers in the value creation process. This study distinguishes itself from previous research by: (1) focusing on decision-enhancing tools for sellers and introducing relationship orientations (exchange vs. communal relationships) into research on platform value creation paths, thereby overcoming the prior limitation of confining relationship orientations to studies of single-sided markets；(2) addressing the diversity within platform ecosystems by exploring how to align intelligent data analysis orientations with different market environment characteristics (e.g., category development stage, competitive intensity, seller's price positioning) to balance the dual goals of short-term transaction growth and long-term supply-demand relationship quality, while creating value for multiple parties.

Overall, this study will offer new theoretical insights into research on human-machine collaboration, data analysis, and the value creation paths of platform operators. It will help platform firms and sellers understand and leverage intelligent data analysis to create value for sellers, buyers, and platform firms, promote innovation in human-machine collaborative selling practices, and enhance the efficiency and effectiveness of supply-demand matching.

Key words: human-machine collaboration, intelligent data analysis, selling, value creation