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This study addresses the remarkable research gap in the existing literature on Chat Generative Pre-training Transformer (ChatGPT), which has primarily explored its functional benefits rather than the psychological states of its users. By integrating the self-concept theory and functional theory of attitudes, this study develops a moderated-mediating model to examine the impact of the bandwagon effect on users’ habit formation and subsequent feelings of pride associated with the ChatGPT application.

This study analyzed self-reported survey data from 568 respondents from mainland China using partial least squares structural equation modeling.

The findings reveal that the bandwagon effect indirectly influences users’ pride through the formation of habits related to ChatGPT applications. This study also identifies the boundary condition of social-adjustive attitude, which strengthens both the direct relationship between the bandwagon effect and habit formation and its indirect relationship with pride.

This study contributes to the field by offering a novel perspective on ChatGPT adoption, highlighting the role of self-concept and attitudinal functions in driving users’ intentions to utilize the technology, with a focus on the desire for pride as a motivating factor.

We demonstrate the practical application of machine learning (ML) techniques in document processing, addressing the increasing need for digitalization in the real estate industry and beyond. Our focus lies on identifying efficient algorithms for extracting individual documents from multi-page PDF files. Through the implementation of these algorithms, organizations can accelerate the digitization of paper-based files on a large scale, eliminating the laborious process of one-by- one scanning. Additionally, we showcase ML-powered methods for automating the classification of both digital and digitized documents, thereby simplifying the categorization process.

We compare two segmentation models that are presented in this paper to analyze the individual pages within a bulk scan, identifying the starting and ending points of each document contained in the PDF. This process involves extracting relevant features from both the textual content and page design elements, such as fonts, layouts and existing page numbers. By leveraging these features, the algorithm accurately splits multi-document PDFs into their respective components. An outlook is provided with a classification code that effectively categorizes the segmented documents into different real estate document classes.

The case study provides an overview of different ML methods employed in the development of these models while also evaluating their performance across various conditions. As a result, it offers insight into solutions and lessons learned for processing documents in real estate on a case-by-case basis. The findings presented in this study lay the groundwork for addressing this prevalent problem. The methods, for which we provide the code as open source, establish a solid foundation for expediting real estate document processing, enabling a seamless transition from scanning or inbox management to digital storage, ultimately facilitating machine-based information extraction.

The process of digitally managing documents in the real estate industry can be a daunting task, particularly due to the substantial volume of documents involved, whether they are paper-based, digitized or in digital formats. Our approach aims to streamline this often tedious and time-consuming process by offering two models as simplified solutions that encourage companies to embrace much-needed digitization. The methods we present in this context are crucial for digitizing all facets of real estate management, offering significant potential in advancing PropTech business cases. The open-source codes can be trained further by researchers and practitioners with access to large volumes of documents.

This study illustrates effective methods for processing paper-based, digitized and digital files, along with tailored ML models designed to enhance these methods, particularly within the real estate sector. The methods are showcased on two datasets, and lessons learned are discussed.

Toward sustainable development, radical green innovation (RGI) is necessary. Despite extensive research on the factors influencing green innovation, few studies have been conducted on the precursors. Based on upper echelons (UE) theory, dynamic capability (DC) theory, “stimulus-organism-response” (SOR) theory, social information processing (SIP) theory and cognitive appraisal (CA) theory of emotion, the study explores how digital leadership (DL) affects RGI and investigates the mediating effects of green organizational identity (GOI) and the moderating effects of digital threat (DT) and technology for social good (TSG), as well as the multiple concurrent causalities that trigger high RGI.

The method of combining structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fs QCA) is adopted in the study. Data from 233 questionnaires were collected at two different time points.

This study's findings indicate that the four dimensions of DL can positively influence RGI and GOI partially mediates between the four dimensions of DL and RGI. DT has a negative moderating effect between DL and GOI, while TSG is positively regulated between them, DT and TSG linkage moderates the partial mediating effect of GOI in DL and RGI. Further, fs QCA is used to analyze the causal complexity of DL dimensions and GOI to RGI and nine effective configuration paths are identified. It is found that the synergy of digital thinking ability (DTA), digital detection ability (DDA), digital social ability (DSA), digital reserve ability (DRA) and GOI is crucial to high RGI. Among them, GOI core appears the most times, indicating that GOI plays a vital role in improving enterprise RGI.

This study expands the literature on leadership and innovation by constructing a framework of “DL-GOI-RGI” and exploring the transmission of GOI and the boundary effect of DT and TSG. The study used fs QCA and SEM to better understand the statistical associations and the set relations between the conjunctions and conditions.

Laser hot wire additive manufacturing (LHWAM) is a newer technology within the space of large-scale directed energy deposition (DED) additive manufacturing (AM) processes. This study aims to map known AM flaw types such as lack of fusion and keyholing, as well as a dripping flaw unique to hot wire processes, across process parameter space using a small number of single-track experiments.

A semianalytical model was calibrated using a small initial set of experimental data. Lack of fusion and keyholing flaws were mapped across process space using existing models. The dripping flaw was modeled via analytical methods calibrated with experimental data, and then mapped across processing space. Further experimental data beyond the small initial set was used to evaluate the accuracy of the process maps developed. A website and executable were deployed to users of the process for convenient rapid process parameter selection.

With the process maps generated during this work, users can easily and rapidly generate desirable parameter sets for a range of conditions, enabling the intelligent utilization of the entire stable processing regime.

The methodology developed can be applied to other LHWAM machines or DED processes to rapidly and inexpensively generate a systematic understanding of processing space for build planning.

LHWAM shows advantages over other large-scale DED processes, but a systematic physically informed study of the key flaw regions across process space had not been conducted, limiting more widespread use of the process and creating a gap that this study fills.

The purpose of the study is to investigate the relationship between the big five personality traits and personal financial planning (PFP) by focusing on the mediating role of mental accounting among Indian service sector employees.

The present study used a data set comprising 649 valid responses obtained through the structured questionnaire that was specifically disseminated to employees working in the Indian service sector. Further, the study used a quantitative approach, partial least squares structural equation modeling, to examine the hypothesized relationship.

The study’s outcomes reveal that mental accounting completely mediates the relationship between conscientiousness and PFP. In addition, extraversion and neuroticism traits have directly influenced the PFP, but in the presence of mental accounting, these traits have partially influenced the PFP. Furthermore, the results suggest that agreeableness directly affects PFP, whereas openness does not demonstrate any significant influence.

The existing literature within the field of PFP has primarily focused on exploring various variables associated with mental accounting, such as monetary and time costs, mental budgeting process and tax liabilities. However, it has overlooked the potential mediating effect of mental accounting. This study bridges this gap by investigating the impact of mental accounting as a mediator in the relationship between personality traits and PFP. Moreover, recently, the Indian economy has undergone major overhauls especially due to enactment of Goods and Services Tax and the profound impact of COVID-19, leading to changes in financial behavior of individuals. Therefore, this study endeavors to shed light on the emerging dynamics within the PFP domain, particularly within the context of the newly accustomed economic circumstances in India.

This study aims to determine the influence of experiential value, authentic happiness and experiential satisfaction on the behavioral intention of luxury fashion.

Two hundred online questionnaires were collected from customers who rented or bought wedding dresses between 2015 and 2020, with at least one rental or purchase coming from an Indonesian bridal wedding brand that promotes the idea of marriage under Islamic law. The quantitative methodology used in this study was examined using the Structural Equation Model analysis method with the AMOS 22 software.

The results showed that all hypotheses were accepted with significant positive influences, including experiential value in halal fashion on authentic happiness, experiential satisfaction and luxury fashion behavioral intention, authentic happiness in luxury fashion behavioral intention and experiential satisfaction, as well as experiential satisfaction in the behavioral intention of luxury fashion.

The data were collected from respondents who have rented and/or purchased wedding dresses. However, the number of respondents who only rent or who only buy was not identified.

The value of experience in halal fashion needs to be increased. Based on the results of this study, it is hoped that marketers can create effective marketing policies and strategies by paying attention to the value of the consumer’s halal fashion experience because it will affect their authentic happiness, experience satisfaction and luxury fashion behavior intention.

This study has unique originality in measuring the variable of luxury fashion behavioral intention, which was adjusted to the object of research, namely luxury fashion.

Given the expansion of cities and urbanization, developing efficient and reliable transportation infrastructure, especially urban tunnels, is essential. Failure to maintain such complex construction facilities with intelligent equipment systems could result in human losses and impose huge costs on governments. Therefore, it is necessary to have practical maintenance plans and operational safety monitoring for urban tunnels, which leads to their long lifespan, increases users’ safety and reduces operation risks.

Hence, this research aims to evaluate the maintenance risks of urban tunnel lighting systems (UTLS) using a hybrid risk-based maintenance (RBM) approach. In this vein, three rounds of a fuzzy Delphi survey were conducted to consolidate the specific operation criteria and maintenance risk factors to the circumstances of Iran and UTLS. Furthermore, the fuzzy DEMATEL method was applied to determine the cause-and-effect relationships among the identified critical operation criteria. The identified risks associated with maintenance in UTLS were then analyzed and ranked using a combination of fuzzy ANP-VIKOR techniques.

The ranking of the various risks revealed that the “poor performance of switchboards in power supply due to faults in switchboard equipment” risk was ranked first, followed by the “poor performance of panels in the power supply due to unfavorable environmental conditions,” “The poor performance of panels in the power supply due to problems with switches (key failure)” and “The poor performance of panels in power supply due to burning fuses due to unauthorized current” risks. The findings of this study indicate that this hybrid maintenance method, developed as a risk-based network, provides reliability for maintaining urban tunnel lighting systems (UTLS).

It is anticipated that the findings of this research will considerably contribute to improving UTLS maintenance management while enhancing different stakeholders’ understanding of the most critical risks in maintenance, particularly toward the UTLS in Iran. An RBM management program can result in preparing and formulating policies, comprehensive guidelines or regulations for the maintenance of urban tunnels that are recommended for future research.

This research aims to examine the potential tensions and management strategies for adopting artificial intelligence (AI) within Sales and Operations Planning (S&OP) environments.

We conducted in-depth interviews with eight S&OP professionals from different manufacturing firms, supplemented by interviews with AI solutions experts and secondary document analysis of various S&OP processes, to scrutinize the paradoxes associated with AI adoption in S&OP.

We revealed 12 sub-paradoxes associated with AI adoption in S&OP, culminating in 5 overarching impact pathways: (1) balancing immediate actions with long-term AI-driven strategies, (2) navigating AI adoption via centralized systems, process redesign and data unification, (3) harmonizing AI-driven S&OP identities, collaboration and technology acceptance, (4) bridging traditional human skills with innovative AI competencies and (5) managing the interrelated paradoxes of AI adoption in S&OP.

The findings provide a roadmap for firms to proactively address the possible tensions associated with adopting AI in S&OP, balancing standardization with flexibility and traditional expertise with AI capabilities.

This research offers (1) a nuanced understanding of S&OP-specific paradoxes in AI adoption, contributing to the broader literature on AI within operations management and (2) an extension to Paradox Theory by uncovering distinct manifestations at the AI–S&OP intersection.

Current research in the field of behavioural finance has attempted to discover behavioural biases and their characteristics in individual investors’ irrational decision-making. This study aims to find out how biases in information based on knowledge affect decisions about investments.

In step one, through existing research and consultation with specialists, 13 relevant items covering major aspects of bias were determined. In the second step, multiple linear regression and artificial neural network were used to analyse the data of 337 retail investors.

The investment choice was heavily impacted by regret aversion, followed by loss aversion, overconfidence and the Barnum effect. It was observed that the Barnum effect has a statistically significant negative link with investing choices. The research also found that investors’ fear of making mistakes and their tendency to be too sure of themselves were the most significant factors in their decisions about where to put their money.

This research contributes to the expansion of the knowledge base in behavioural finance theory by highlighting the significance of cognitive psychological traits in how leading investors end up making irrational decisions. Portfolio managers, financial institutions and investors in developing markets may all significantly benefit from the information offered.

This research is a one-of-a-kind study, as it analyses the emotional biases along with the cognitive biases of investor decision-making. Investor decisions generally consider the shadowy side of knowledge management.