Generative AI Usage by Individuals During the 2024 U.S. Presidential Election: Symmetrical and Asymmetrical Analysis

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use.

PurposeThis article aims to investigate the influence of generative AI (GAI) use on digital performance (DP) from its formation and boundary mechanisms, as well as to further reveal specific solutions that achieve a high level of DP.Design/methodology/approachUsing social support (SS) theory, this article proposes a research model and then adopts a hybrid method of partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze 304 questionnaire data in China.FindingsThe results indicate that (1) GAI used for work and social positively influences SS, which in turn enhances DP. In particular, informational support exerts a greater effect on digital-enabled task performance. Emotional support exerts a greater effect on digital-enabled innovative performance. (2) Innovative culture (IC) strengthens the effect of GAI used for work on informational support and emotional support, as well as the effect of GAI used for social and emotional support. (3) Four solutions lead to a high level of digital-enabled task performance, and three solutions lead to a high level of digital-enabled innovative performance.Originality/valueFor academics, this article adopts a hybrid method to illustrate the relationship between GAI used for various purposes and different types of DP from the formation mechanism of SS and the boundary mechanism of IC, as well as to reveal the synergistic effect of GAI use, SS and IC on DP. For practices, this article provides managers with insights to increase DP regarding employee training and organizational culture building.

This study aims to investigate how ethical considerations—including security, privacy, non-deception, and fulfillment—in generative artificial intelligence (GenAI) affect users’ intentions to continue its usage. We developed a theoretical framework using a mixed-methods design that integrates “partial least squares structural equation modeling” (PLS-SEM) and “fuzzy-set qualitative comparative analysis” (fsQCA) to understand this impact. The PLS-SEM findings indicate that the ethical aspects of GenAI (security, privacy, non-deception, and fulfillment) positively influence users’ perception of GenAI identity, which in turn positively affects their intentions for continued usage. Additionally, fsQCA identifies three configurations that lead to users’ continued usage of GenAI. This study enriches the AI ethics framework by comprehensively analyzing ethical perceptions in GenAI, focusing on security, privacy, non-deception, and fulfillment. Moreover, by exploring GenAI’s ethical impact on user behavior, this study offers important insights for the responsible adoption of GenAI.

Among antibiotic resistance cases, resistance to β-lactam antibiotics is a major concern for the treatment of microbial infections. Furthermore, the prevalence of extended-spectrum β-lactamases (ESBL) Escherichia coli (E. coli) in environment, food, and human resources of Iran has increased over the past few years. This study aimed to predict the relationship between the prevalence of ESBL E. coli in the environment and the food chains with the presence of this infection in people suspected of septicemia using fuzzy set qualitative comparative analysis model. In this analytical cross sectional study samples were collected from the environment (hospital sewage, downstream and upstream urban sewage, and slaughterhouse sewage), food (chicken), and human chains (people suspected of septicemia) in Tehran province, Iran. This study was conducted from September to February 2019 and the prevalence of ESBL E. coli was calculated in each resource. Then, the relationship between the prevalence of ESBL E. coli in the environment and food chains and its prevalence in the human chain was predicted using the fuzzy set qualitative comparative analysis. The results showed the prevalence of ESBL E. coli in those suspected of septicemia in September, October, November, December, January and February was 58.1%, 60%, 33.3%, 100%, 43%, and 57.8%, respectively. Also, the results of the fuzzy set qualitative comparative analysis indicated hospital wastewater and chicken contamination with ESBL E. coli were the main causes of contamination with ESBL E. coli in people suspected of septicemia. According to the results of this study, if there is a contamination of hospital wastewater and chickens in an area, it can be claimed that people suspected of septicemia are infected with ESBL E. coli, and the percentage of this contamination can be high. On the other hand, controlling ESBL E. coli in hospital wastewater (environmental chain) and chickens (food chain) can prevent contamination in people with suspected septicemia.

Purpose This study aims to explore how generative AI enhances employee creativity and performance in international hotel marketing. It applies an integrated technology–organization–environment (TOE) and antecedents–behavior–consequences (ABC) framework to examine the role of technological competence, organizational support, government support and artificial intelligence (AI) strategy in fostering employee innovation and performance. Design/methodology/approach A mixed-method approach was adopted, combining survey data from 206 international hotel marketers with semi-structured interviews. The study uses partial least squares structural equation modeling to test relationships and fuzzy-set qualitative comparative analysis (fsQCA) to identify configurations leading to high employee performance. Findings Technological competence, organizational support and government support significantly influence AI-driven innovative behavior. Innovative behavior, in turn, enhances employee creativity and performance, with creativity acting as a mediator. AI strategy amplifies the impact of organizational support on employee innovation. The fsQCA results reveal multiple pathways to achieving high employee performance, demonstrating the multifaceted nature of AI-enabled outcomes. Practical implications Hotels can enhance employee innovation and performance by investing in AI training and aligning AI strategies with organizational support. Policymakers should promote AI-friendly policies and partnerships to foster adoption. Organizations can further benefit from integrating generative AI tools with workflows to boost creativity and service quality, enhancing competitive advantage in the hospitality sector. Originality/value This study contributes by integrating the TOE and ABC frameworks to explore the cognitive and behavioral mechanisms underpinning AI-driven performance. It introduces AI strategy as a boundary condition and offers new insights into the nuanced ways AI influences creativity and productivity in hospitality management.

Qualitative methods are incredibly beneficial to the dissemination and implementation of new digital health interventions; however, these methods can be time intensive and slow down dissemination when timely knowledge from the data sources is needed in ever-changing health systems. Recent advancements in generative artificial intelligence (GenAI) and their underlying large language models (LLMs) may provide a promising opportunity to expedite the qualitative analysis of textual data, but their efficacy and reliability remain unknown. The primary objectives of our study were to evaluate the consistency in themes, reliability of coding, and time needed for inductive and deductive thematic analyses between GenAI (ie, ChatGPT and Bard) and human coders. The qualitative data for this study consisted of 40 brief SMS text message reminder prompts used in a digital health intervention for promoting antiretroviral medication adherence among people with HIV who use methamphetamine. Inductive and deductive thematic analyses of these SMS text messages were conducted by 2 independent teams of human coders. An independent human analyst conducted analyses following both approaches using ChatGPT and Bard. The consistency in themes (or the extent to which the themes were the same) and reliability (or agreement in coding of themes) between methods were compared. The themes generated by GenAI (both ChatGPT and Bard) were consistent with 71% (5/7) of the themes identified by human analysts following inductive thematic analysis. The consistency in themes was lower between humans and GenAI following a deductive thematic analysis procedure (ChatGPT: 6/12, 50%; Bard: 7/12, 58%). The percentage agreement (or intercoder reliability) for these congruent themes between human coders and GenAI ranged from fair to moderate (ChatGPT, inductive: 31/66, 47%; ChatGPT, deductive: 22/59, 37%; Bard, inductive: 20/54, 37%; Bard, deductive: 21/58, 36%). In general, ChatGPT and Bard performed similarly to each other across both types of qualitative analyses in terms of consistency of themes (inductive: 6/6, 100%; deductive: 5/6, 83%) and reliability of coding (inductive: 23/62, 37%; deductive: 22/47, 47%). On average, GenAI required significantly less overall time than human coders when conducting qualitative analysis (20, SD 3.5 min vs 567, SD 106.5 min). The promising consistency in the themes generated by human coders and GenAI suggests that these technologies hold promise in reducing the resource intensiveness of qualitative thematic analysis; however, the relatively lower reliability in coding between them suggests that hybrid approaches are necessary. Human coders appeared to be better than GenAI at identifying nuanced and interpretative themes. Future studies should consider how these powerful technologies can be best used in collaboration with human coders to improve the efficiency of qualitative research in hybrid approaches while also mitigating potential ethical risks that they may pose.

Purpose: This study investigated the simultaneous impact of conditions on employee's job satisfaction in Polish small and medium-sized enterprises (SMEs).
 Methodology: This study used the survey technique to better understand the determinants of job satisfaction the fuzzy set Qualitative Comparative Analysis (fs/QCA) was preferred. Qualitative comparative analysis (QCA) is a widely used method in the field of political science and sociology. In recent years, the use of the fuzzy set Qualitative Comparative Analysis (fs/QCA) in business and management research has also increased.
 Result: The results of our empirical study contribute to research on job satisfaction by presenting several conditions that create constellations affecting employee job satisfaction in Polish SMEs. The results certify previous research on employee satisfaction, exploring the important factors such as: organizational identification, co-workers support, rewards, supervisor relationship and quality of work life. It is worth noting that our research contributes to different constellations lead to job satisfaction by investigating the effect of all of selected conditions simultaneously.
 Applications: This finding can be useful for small and medium enterprises to enhance employee job satisfaction, which in turn translates into the results of the entire organization.
 Novelty/Originality: In this research, the model of conditions affecting employee job satisfaction in polish SMEs, a qualitative comparative analysis is presented in a comprehensive and complete manner.

As the digital economy and society rapidly grow, individual and organizational adaptation to technology has gained substantial concern across various sectors. However, this process involves many challenges, including uncertainty and complexity arising from factors such as the reliability, feasibility, and compatibility of technologies. Based on evidence from existing literature, this study proposes applying the fuzzy set qualitative comparative analysis (fsQCA) approach as a valuable tool in investigating associated challenges and complex configurations of influential factors within the context of individual and organizational technology decision-making in technology adoption. The fsQCA has emerged as a popular tool in qualitative analysis, particularly in recent years, where its use has grown substantially. This paper conducts a systematic literature review of journal articles published between 2015 and 2023 using fsQCA, focusing on digital transformation, AI, IoT, e- and m-commerce applications, digital assistants, business analytics, sustainable development, and machine learning. This study offers a detailed review of related research, the implications of the identified trends, and the potential for future research utilizing fsQCA to explore performance and human behavior in technology adoption and organizational technology decision-making contexts.

Scholars are the main force behind academic entrepreneurship. The method of how to stimulate scholars’ academic entrepreneurial intention and how to further promote social and economic development are important questions for the academic community. Research on the “net effect” of the factors affecting academic entrepreneurial intention has achieved some theoretical results. However, the results that affect academic entrepreneurial intention are complex and not influenced by a single factor, but rather by the interaction between various factors. Therefore, this study used a fuzzy set qualitative comparative analysis research method to explore how various factors can affect scholars’ academic entrepreneurial intention from two dimensions: the Big Five personality traits and academic entrepreneurial motivation. Our findings showed two configurations that affect high academic entrepreneurial intention of university scholars: the openness to experience—ribbon—dominant path, and the ribbon—dominant path. Additionally, two configurations were revealed for the formation of not-high academic entrepreneurial intention: extraversion—conscientiousness—inhibition and extraversion—agreeableness—gold—hindrance paths. Moreover, this study revealed that a causal asymmetry exists between the high and the not-high academic entrepreneurial intention configurations. This study broadens the application of the fuzzy set qualitative comparative analysis method in the research of academic entrepreneurial intention and provides theoretical and practical insights for researchers and practitioners on how to effectively stimulate scholars’ academic entrepreneurial intention.

This article is based on the mixed ownership reform, with energy enterprises as the research object, and constructs a fuzzy set qualitative comparative analysis game framework for path optimization in complex systems. Using fsQCA and game theory from the perspective of knowledge sharing, reasonable assumptions are made for the sharing of green innovation knowledge between state-owned and non-state-owned shareholders in energy enterprises, providing policy support and institutional guarantee for the sharing of similar green innovation knowledge in multiple industries, to some extent, it facilitates the exchange and flow of green innovation knowledge among different enterprises, providing the optimal path for complex systems. This article can draw the following conclusions: 1. Government led, government enterprise driven, and enterprise led are the three major influencing paths for promoting green innovation knowledge sharing. The final trend of stable gaming among different types of shareholders depends on sharing profits. 3. The fuzzy set qualitative comparative analysis game framework has certain universality.

With the advancement of artificial intelligence (AI) and the Internet of Things (IoT), smart clothing, which has enormous growth potential, has developed to suit consumers' individualized demands in various areas. This paper aims to construct a model that integrates that technology acceptance model (TAM) and functionality-expressiveness-aesthetics (FEA) model to explore the key factors influencing consumers' smart clothing purchase intentions (PIs). Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the data, complemented by fuzzy-set qualitative comparative analysis (fsQCA). The PLS-SEM results identified that the characteristics of functionality (FUN), expressiveness (EXP), and aesthetics (AES) positively and significantly affect perceived ease of use (PEOU), and only EXP affects perceived usefulness (PU). PU and PEOU positively impact consumers' attitudes (ATTs). Subsequently, PU and consumers' ATTs positively influence PIs. fsQCA revealed the nonlinear and complex interaction effects of the factors influencing consumers' smart clothing purchase behaviors and uncovered five necessary and six sufficient conditions for consumers' PIs. This paper furthers theoretical understanding by integrating the FEA model into the TAM. Additionally, on a practical level, it provides significant insights into consumers' intentions to purchase smart clothing. These findings serve as valuable tools for corporations and designers in strategizing the design and promotion of smart clothing. The results validate theoretical conceptions about smart clothing PIs and provide useful insights and marketing suggestions for smart clothing implementation and development. Moreover, this study is the first to explain smart clothing PIs using symmetric (PLS-SEM) and asymmetric (fsQCA) methods.

PurposeReacting to the calls in the contemporary literature to further examine the relationship between board attributes and firms’ decisions to obtain corporate social responsibility assurance (CSRA) through the use of pioneering techniques, this study aims to analyse the influence of such attributes together with the existence of a corporate social responsibility (CSR) committee on the adoption of CSRA using fuzzy set qualitative comparative analysis (Fs-QCA).Design/methodology/approachFs-QCA was performed on a sample of nonfinancial European companies listed on the STOXX Europe 600 index over the period 2016–2018.FindingsThe study findings indicate that the decision to obtain a CSRA report depends on a complex combination of the influence of the CSR committee and certain board attributes, such as size, experience, independence, meeting frequency, gender and CEO separation. These attributes play essential contributing roles and, if suitably combined, stimulate the adoption of CSRA.Practical implicationsThe study findings are important for policymakers, professionals, organisations and regulators in forming and modifying the rules and guidelines related to CSR committees and board composition.Originality/valueTo the best of the authors’ knowledge, this study represents the first examination of the impact of board attributes and CSR committees on the adoption of CSRA using Fs-QCA method. It also offers a novel methodological contribution to the board-CSRA literature by combining traditional statistical (logistic regression) and Fs-QCA methods. This study emphasises the benefits of Fs-QCA as an alternative to logistic regression analysis. Through the use of these methods, the research illustrates that Fs-QCA offers more detailed and informative results when compared to those obtained through logistic regression analysis. This finding highlights the potential of Fs-QCA to enhance our understanding of complex phenomena in academic research.

Entrepreneurial competence is a critical skill for twenty-first-century learners, yet effective instructional models for fostering this ability remain underexplored. This study investigates the impact of an AI-assisted, argumentation-driven learning approach on the development of entrepreneurial competence among university students. Utilizing a quasi-experimental design, 86 students participated in a scientific argumentation-driven entrepreneurship education course, with one group receiving Generative Artificial Intelligence (GAI) support and the other following a traditional instructional approach. Structural Equation Modeling (SEM) results revealed that data literacy played a pivotal role in predicting entrepreneurial competence, acting as a mediator between AI assistance, teacher guidance, and students’ ability development. Additionally, Fuzzy-Set Qualitative Comparative Analysis (fsQCA) identified seven distinct paths to entrepreneurial competence, highlighting the interplay between cognitive and environmental factors. The findings underscore the transformative potential of AI-assisted learning models in enhancing entrepreneurial competence, providing insights for the personalization of entrepreneurship education and the promotion of educational equity.

PurposeThis study aims to investigate the key factors that motivate learners to use handheld devices to access library resources. To do so, this study integrates the technology acceptance model (TAM) and the DeLone and McLean information systems success (D and M-ISS) model.Design/methodology/approachThe relationship between the causes and the outcomes may not be symmetrical. To test this proposition, data were collected from 210 respondents in a Gulf country and analysed using structural equation modelling (SEM) and complemented by fuzzy set qualitative comparative analysis (fsQCA).FindingsThe SEM results revealed that three constructs – perceived ease of use (PEOU), service quality (SQ) and system quality (SEQ) are strong drivers of students’ continuous intention to use handheld devices to access library resources. However, perceived usefulness (PU) and information quality (IQ) do not significantly influence students’ intentions. Besides, SQ and PEOU are positively related to PU. Furthermore, fsQCA results show that two different conjunctions, PU*PEOU*IQ*SEQ and PEOU*SQ*IQ*SEQ, cause the students to show a continuous intention to use handheld devices to access library resources.Originality/valueUnlike previous studies on mobile library resource utilization, this analysis extends TAM to investigate the linear additive influence of two basic TAM constructs: PEOU and PU, and three constructs, namely, SEQ, SQ and IQ of the ISS model, on students’ library resource utilization. Furthermore, the findings of SEM were complemented by a set theory-based configuration method, fsQCA, to investigate the asymmetrical, equifinal and configurational causation leading to the desired outcome. The findings of this study have theoretical and practical implications.

Corporate social responsibility (CSR) has become an international phenomenon, and while enterprises are engaging in related activities for the benefit of stakeholders, one might question whether CSR has a key driving role at establishing corporate reputation and brand equity. To address the issue, this research explores the causal impact of CSR and corporate reputation on brand equity by using structural equation modeling and fuzzy set qualitative comparative analysis. The findings from structural equation modeling show that CSR has a positive effect on brand equity and corporate reputation. In addition, fuzzy set qualitative comparative analysis identifies several causal configurations of CSR and corporate reputation in building brand equity. Overall, the driving factors of CSR and corporate reputation, depending on enterprise characteristics, have different impacts on brand equity. These results suggest that companies need to rethink their strategic actions for brand equity building.