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Management journals are currently responding to challenges raised by the “replication crisis” in experimental social psychology, leading to new standards for transparency. These approaches are spilling over to qualitative research in unhelpful and potentially even dangerous ways. Advocates for transparency in qualitative research mistakenly couple it with replication. Tying transparency tightly to replication is deeply troublesome for qualitative research, where replication misses the point of what the work seeks to accomplish. We suggest that transparency advocates conflate replication with trustworthiness. We challenge this conflation on both ontological and methodological grounds, and we offer alternatives for how to (and how not to) think about trustworthiness in qualitative research. Management journals need to tackle the core issues raised by this tumult over transparency by identifying solutions for enhanced trustworthiness that recognize the unique strengths and considerations of different methodological approaches in our field.

This article studies the global variation in economic preferences. For this purpose, we present the Global Preference Survey (GPS), an experimentally validated survey data set of time preference, risk preference, positive and negative reciprocity, altruism, and trust from 80,000 people in 76 countries. The data reveal substantial heterogeneity in preferences across countries, but even larger within-country heterogeneity. Across individuals, preferences vary with age, gender, and cognitive ability, yet these relationships appear partly country specific. At the country level, the data reveal correlations between preferences and biogeographic and cultural variables, such as agricultural suitability, language structure, and religion. Variation in preferences is also correlated with economic outcomes and behaviors. Within countries and subnational regions, preferences are linked to individual savings decisions, labor market choices, and prosocial behaviors. Across countries, preferences vary with aggregate outcomes ranging from per capita income, to entrepreneurial activities, to the frequency of armed conflicts.

Purpose Artificial intelligence chatbots are shifting the nature of online services by revolutionizing the interactions of service providers with consumers. Thus, this study aims to explore the antecedents (e.g. compatibility, perceived ease of use, performance expectancy and social influence) and consequences (e.g. chatbot usage intention and customer engagement) of chatbot initial trust. Design/methodology/approach A sample of 184 responses was collected in Lebanon using a questionnaire and analyzed using structural equation modeling (SEM) by AMOS 24. Findings The results revealed that except for performance expectancy, all the other three factors (compatibility, perceived ease of use and social influence) significantly boost customers’ initial trust toward chatbots. Further, initial trust in chatbots enhances the intention to use chatbots and encourages customer engagement. Research limitations/implications The study provides insights into some variables influencing initial chatbot trust. Future studies could extend the model by adding other variables (e.g. customer experience and attitude), in addition to exploring the dark side of artificial intelligence chatbots. Practical implications This study suggests key insights for marketing managers on how to build chatbot initial trust, which, in turn, will lead to an increase in customers’ interactions with the brand. Originality/value The current study marks substantial contributions to the artificial intelligence marketing literature by proposing and testing a novel conceptual model that examines for the first time the factors that impact chatbot initial trust and the key outcomes of the latter.

In this essay, we discuss how tables can be used to ensure—and reassure about—trustworthiness in qualitative research. We posit that in qualitative research, tables help not only increase transparency about data collection, analysis, and findings, but also—and no less importantly—organize and analyze data effectively. We present some of the tables most frequently used by qualitative researchers, explain their uses, discuss how they enhance trustworthiness, and provide illustrative examples to inspire readers in their use of tables in their own research.

Our research empirically assesses two distinct bases for trust: calculative trust, based on a structure of rewards and penalties, versus relational trust, a judgment anchored in past behavior and characterized by a shared identity. We find that calculative trust and relational trust positively influence supplier performance, with calculative trust having a stronger association than relational trust. Yet, important boundary conditions exist. If buyers invest in supplier-specific assets or when supply side market uncertainty is high, relational trust, not calculative trust, is more strongly associated with supplier performance. In contrast, when behavioral uncertainty is high, calculative trust, not relational trust, relates more strongly to supplier performance. These results highlight the value of examining distinct forms of trust.

PurposeChatbots are increasingly prevalent in the service frontline. Due to advancements in artificial intelligence, chatbots are often indistinguishable from humans. Regarding the question whether firms should disclose their chatbots' nonhuman identity or not, previous studies find negative consumer reactions to chatbot disclosure. By considering the role of trust and service-related context factors, this study explores how negative effects of chatbot disclosure for customer retention can be prevented.Design/methodology/approachThis paper presents two experimental studies that examine the effect of disclosing the nonhuman identity of chatbots on customer retention. While the first study examines the effect of chatbot disclosure for different levels of service criticality, the second study considers different service outcomes. The authors employ analysis of covariance and mediation analysis to test their hypotheses.FindingsChatbot disclosure has a negative indirect effect on customer retention through mitigated trust for services with high criticality. In cases where a chatbot fails to handle the customer's service issue, disclosing the chatbot identity not only lacks negative impact but even elicits a positive effect on retention.Originality/valueThe authors provide evidence that customers will react differently to chatbot disclosure depending on the service frontline setting. They show that chatbot disclosure does not only have undesirable consequences as previous studies suspect but can lead to positive reactions as well. By doing so, the authors draw a more balanced picture on the consequences of chatbot disclosure.

Judging by the millions of reviews left by guests on the Airbnb platform, this trusted community marketplace for accommodations is fulfilling its mission of matching travelers with hosts having room to spare remarkably well. Based on our analysis of ratings, we collected for millions of properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average star-rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5 star-rating. We contrast this with the ratings of roughly 700,000 hotels, B&Bs, and vacation rentals worldwide that we collected from TripAdvisor. We find that hotel and B&B average ratings are much lower—3.8 and 4.1 stars, respectively—with much more variance across reviews. TripAdvisor vacation rental ratings are more similar to Airbnb ratings, but only about 85% of properties have an average rating of 4.5 or 5 stars. We then consider properties cross-listed on both platforms. For these properties, we find that even though the average ratings on Airbnb and TripAdvisor are more similar than hotels and B&Bs, proportionally more properties receive the highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only a weak correlation in the ratings of individual cross-listed properties across the two platforms. Finally, we show that these differences are consistent when considering data from two different time periods: 2015 and 2018.

In an ever more transparent, digitalized, and connected environment, customers are increasingly pressuring brands to embrace genuine corporate social responsibility (CSR) practices and co-creation activities. While both CSR and co-creation are social and collaborative processes, there is still little research examining whether CSR can boost co-creation. In addition, while previous research has mainly related co-creation to emotional outcomes (e.g., customer affective commitment), limited empirical research has related it to rational (e.g., customer trust) and behavioral outcomes (e.g., customer loyalty). To address these shortcomings in the literature, this paper examines the influence of CSR on customer loyalty, considering the mediating roles of co-creation and customer trust. It also investigates the influence of co-creation on customer trust. The data were collected in Spain in late 2017 using an online survey, and the sample contained 1101 customers of health insurance services brands. Structural equation modeling was used to test the hypothesized relationships simultaneously. The results show that CSR influences customer loyalty both directly and indirectly through co-creation and customer trust. However, the indirect impact is the stronger of the two, implying that embracing co-creation activities and developing customer trust can make it easier for CSR practices to enhance customer loyalty. In addition, co-creation has a direct effect on customer trust.

During the 2008–2009 financial crisis, firms with high social capital, as measured by corporate social responsibility (CSR) intensity, had stock returns that were four to seven percentage points higher than firms with low social capital. High-CSR firms also experienced higher profitability, growth, and sales per employee relative to low-CSR firms, and they raised more debt. This evidence suggests that the trust between a firm and both its stakeholders and investors, built through investments in social capital, pays off when the overall level of trust in corporations and markets suffers a negative shock.