Hasil untuk "Business"

G. Robinson, K. Dechant

Pramodita Sharma, James J. Chrisman, J. Chua

B. Mahadevan

D. Ford, Lars-Erik Gadde, Håkan Håkansson et al.

R. Freeman, J. McVea

Hans Eriksson, Magnus Penker

Mike W. Peng

Wil M.P. van der Aalst, H. Reijers, A. Weijters et al.

Sea-Jin Chang, Jaebum Hong

Johan Wiklund, H. Patzelt, D. Shepherd

The purpose of this article is to develop an integrative model of small business growth that is both broad in scope and parsimonious in nature. Such a “big picture” model provides an opportunity (1) to gauge how much we really know about small business growth, when we simultaneously consider the constructs from the dominant perspectives, (2) to assess the contribution of each of these perspectives, (3) to examine the indirect effects that some constructs from one perspective might have on small business growth through constructs from another perspective, and (4) to consider different levels of analysis. Based on an analysis of data from 413 small businesses, we derive a set of propositions that suggest how entrepreneurial orientation, environmental characteristics, firm resources, and managers’ personal attitudes directly and/or indirectly influence the growth of small businesses.

Howard Smith, Peter Fingar

G. Guizzardi, Nicola Guarino, P. A. Almeida

Borağan Aruoba, F. Diebold, Chiara Scotti

M. North, C. Macal

Ugo Albertazzi, L. Gambacorta

B. Wirtz, Oliver Schilke, S. Ullrich

Diego Comin, Diego Comin, M. Gertler et al.

Maximilian Röglinger, J. Pöppelbuß, J. Becker

C. Baden-Fuller, V. Mangematin

Yizhen Wei, David (Jingjun) Xu, Kai Li

As artificial intelligence (AI) increasingly engages in managing consumer interactions on e-commerce platforms, an important question arises: how do public AI-generated replies to negative reviews compare with human replies in fostering consumer trust? This study investigates consumer trust in reply source (AI review bots vs. human agents) across three reply strategies, namely, default, thinking, and feeling, in a public review context. AI review bots are defined as automated systems that publicly respond to consumer reviews. Using a controlled laboratory experiment, we find that when using the default strategy, human replies elicit greater trust than AI replies, mediated by higher perceived authenticity and persuasiveness. Conversely, when using the thinking strategy, AI replies outperform human replies in building consumer trust, as they are perceived as more authentic and persuasive. In the feeling strategy, there is a convergence between AI and human replies. These findings demonstrate that the effectiveness of AI versus human replies depends on the strategy adopted. Theoretically, this study extends research on human–AI interaction by introducing a three-strategy framework to systematically compare AI and human communicators in public review contexts. Practically, the results guide e-commerce sellers and platforms on when and how to deploy AI review bots to effectively manage consumer trust in response to negative reviews.