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The discussion about using Chat Generative Pre-Trained Transformer (ChatGPT) by teachers is making notable progress on a daily basis. This research examines the teachers' adoption intention to adopt ChatGPT by focusing on perceived trust and perceived risk. The study seeks to elucidate the impact of these two factors on teachers' adoption intentions towards ChatGPT.

This study was exclusively conducted at private higher educational institutions in Gujarat, India. Data collection was done through a cross-sectional survey design. The proposed conceptual model was examined with the help of structural equation modelling (SEM).

The outcome of the study confirms the significant contribution of perceived usefulness, perceived ease of use, perceived trust, perceived intelligence, perceived anthropomorphism and social influence to teachers' intention to adopt ChatGPT. The findings of the study show that perceived risk exerts a negative moderating effect between perceived usefulness and adoption intention as well as between perceived trust and adoption intention.

This study fills the knowledge gap about teachers’ adoption of ChatGPT at private higher education institutions, thus contributing to the existing literature. Specifically, the distinctive role of key variables like perceived risk and perceived trust helps increase the existing body of knowledge.

Several practical implications are presented on the basis of the conclusions from the outcome of the study that would help increase teachers’ adoption intention of ChatGPT in higher education institutions. These implications include recommendations to promote the integration of ChatGPT in educational set-ups to help teachers leverage its potential benefits into their teaching practices.

This research study goes deeper into the subject than previous research, which mainly focused on the possible advantages and downsides of ChatGPT applications in the field of education. It makes a substantial contribution to our understanding of ChatGPT adoption among teachers for educational purposes by investigating through the lens of perceived risk and perceived trust. The study offers fresh understandings that were previously ignored and brings new perspectives to the body of literature.

The recent economic changes in India and the gender discrimination practices of the patriarchal society have forced Indian women to turn to the financial sector as an essential means of generating returns. This study aims to identify the factors influencing investors’ investment frequency in India’s two most recognized metropolitan areas.

The authors applied structural equation modeling to augment Allport’s consumer behavior model and the social influence theory for assessing the frequency of investments made by 690 investors. The direct and indirect linkages in the proposed model were evaluated using moderation and mediation techniques.

The study’s findings show that investors’ perceptions of gender discrimination practices and social influence considerably increase investors’ involvement, magnifying their investment frequency. In addition, access to reliable information reinforces the relationship between investors’ involvement and their frequency of investments, whereas the low-risk tolerance weakens this association.

The findings could help policymakers, investors, financial media outlets, financial experts, educational institutions and society strengthen India’s financial sector by leveraging the linkage between the underlying constructs and investors’ behavior.

The aspects of involvement and gender inequality have not garnered enough attention in the previous studies on behavioral finance. The study delves deeper into investor behavior by establishing a link between the underlying constructs and broadening the horizons of prominent consumer behavior models. It also unfurls the moderating role of access to information and risk tolerance to comprehend the association better.

This study aims to examine member attachment in hybrid identity organizations (HIOs), assessing the distinct effects of identification with respect to two elements (normative and utilitarian) of a hybrid’s identity. Specifically, the author explored how such dual identifications influence commitment and exit/voice/loyalty.

To distinguish the effects of the two identities, the author used the mechanism of identity congruence – the gap between identity perceptions and expectations – as an analog of identification. The models of identity gap, commitment and exit/voice/loyalty were examined via a survey of agricultural cooperative members.

Both the social and economic forms of identity gap were significantly related to commitment and exit/voice/loyalty. In addition, commitment mediated the relationship between identity gap and exit/voice/loyalty.

The results demonstrate the distinctive effects of the dual identities and reinforce the importance of delineating such differences when examining identification in hybrid organizations.

Managers should recognize the duality inherent in hybrid organizational identification and understand the potential for different outcomes stemming from the separate identities.

This study represents the first quantitative examination of an integrated model of dual identification and commitment in HIOs. It is also unique in exploring the exit/voice/loyalty framework as a consequent of identification.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.

Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.

In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.

The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.

This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Performance feedback can be constructed using firms’ own (historical) performance, or the performance of peers (social). Those two types of performance feedback can be consistent (both positive, both negative) or inconsistent (one positive, the other negative). The research on the impact of consistent versus inconsistent feedback has been inconclusive, suggesting that inconsistent feedback might lead to more intense or less intense responses, or no response. In this paper, we theorize and test how firms respond to (in)consistent performance feedback.

We test our hypotheses on a longitudinal sample of 2,819 private, high-growth firms in the US with 6,688 observations between the years 2007 and 2016. Our dataset comprises 25 different industries. We use topic modeling on textual data from firms’ web pages to capture portfolio expansion.

We find that consistent negative performance feedback strengthens portfolio expansion, but consistent positive feedback does not influence portfolio expansion. We also find that inconsistent performance feedback weakens portfolio expansion, but only with negative historical feedback and positive social feedback.

We contribute to the Behavioral Theory of the Firm by improving our understanding of mechanisms of feedback configurations. Specifically, we elaborate on the role of (in)consistent social feedback when firms respond to historical performance feedback. We also contribute to the theory by better understanding private firms’ responses to performance feedback.

This study investigates the multifaceted barriers and facilitators affecting research data sharing across the research data lifecycle. It aims to broaden the understanding of data sharing beyond the publication phase, emphasizing the continuous nature of data sharing from generation to reuse.

Employing a mixed-methods approach, the study integrates the Theory of Planned Behavior, the Technology Acceptance Model, and the Institutional Theory to hypothesize the influence of various factors on data sharing behaviors across the lifecycle. A questionnaire survey and structural equation modeling are utilized to empirically test these hypotheses.

This study identifies critical factors influencing data sharing at different lifecycle stages, including perceived behavioral control, perceived effort, journal and funding agency pressures, subjective norms, perceived risks, resource availability, and perceived benefits. The findings highlight the complex interplay of these factors and their varying impacts at different stages of data sharing.

This study illuminates the dynamics of research data sharing, offering insights while recognizing its scope might not capture all disciplinary and cultural nuances. It highlights pathways for stakeholders to bolster data sharing, suggesting a collaborative push towards open science, reflecting on how strategic interventions can bridge existing gaps in practice.

This study offers actionable recommendations for policymakers, journals, and institutions to foster a more conducive environment for data sharing, emphasizing the need for support mechanisms at various lifecycle stages.

This study contributes to the literature by offering a comprehensive model of the research data lifecycle, providing empirical evidence on the factors influencing data sharing across this continuum.

The widespread use of mobile apps in marketing has resulted in in-app advertising to promote products and services. Research on in-app advertising has focused on several dimensions but not on the modality of ad generation. The use of artificial intelligence (AI) and memes as advertisements has paved the way for multiple ways to create them. This study aims to understand the effect of various advertisement generation modalities on an individual’s trust, attitude toward the advertisement, subjective norms, intentions and use of a particular product.

Using the theoretical lens of reasoned action and trust, the authors explored through an experimental study (five treatments-AI-generated ad and meme, human-created ad and meme and user-generated meme, and (n = 300) the consumer’s intention to purchase a fictitious shampoo brand based on in-app advertising. The respondents were exposed to one of the treatments without knowledge of the ad generation modality.

Trust differed significantly across all the experimental conditions. Furthermore, the authors observe that the theory of reasoned action holds for all advertising generation modalities.

The use of AI in advertising is increasing exponentially, and brands are using AI-generated content to engage with their audiences on various platforms. To the best of the authors’ knowledge, this is one of the first studies to attempt to understand the effects of various ad generation modalities on the trust, attitude and behavior of individuals. Furthermore, this study examines both AI and human-created memes and their effects. The authors suggest optimizing the prompt engineering to develop AI-generated images.

Liquid-assisted laser surface texturing technology was used to create composite microtextures on triangular guide rail surfaces to enhance their tribological properties.

Numerical simulations were used to investigate the impact of various microtextures on fluid dynamic lubrication. Reciprocating friction and wear tests, followed by mechanistic analysis, examined the combined tribological effects of microtextured surfaces and lubricants.

The numerical simulation outcomes reveal a significant augmentation in the influence of fluid dynamic pressure due to composite microtextures, consequently amplifying the load-bearing capacity of the oil film. The average friction coefficient of composite microtextured samples was approximately 0.136 in reciprocating pin-on-disk friction tests, representing approximately 17% decrease compared to polished samples. Triangular guide rails with composite microtextures demonstrated the lowest average coefficient under conditions of high-speed and heavy-loading in the reciprocating friction and wear tests. Additionally, the presence of composite microtextures was found to promote the formation of adsorbed and friction films during friction, potentially contributing to the enhancement of tribological properties.

Triangular guide rails face high friction and wear, limiting their stability in demanding applications like machine tool guideways. This paper proposes a novel approach for steel triangular guide rails, involving composite microtexturing, numerical fluid simulations, liquid-assisted laser surface texturing and friction-wear testing. By implementing composite microtextures, the method aims to reduce friction coefficients and extend guideway service life, thereby saving energy and reducing maintenance costs. Enhancing the antifriction and antiwear properties of machine tool guideways is crucial for improving performance and longevity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0183/