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With the widespread use of online communications, users are extremely vulnerable to a myriad of deception attempts. This study aims to extend the literature on deception in computer-mediated communication by investigating whether the manner in which popularity information (PI) is presented and media richness affects users’ judgments.

This study developed a randomized, within and 2 × 3 between-subject experimental design. This study analyzed the main effects of PI and media richness on the imitation magnitude of veracity judges and the effect of the interaction between PI and media richness on the imitation magnitude of veracity judges.

The manner in which PI is presented to people affects their tendency to imitate others. Media richness also has a main effect; text-only messages resulted in greater imitation magnitude than those viewed in full audiovisual format. The findings showed an interaction effect between PI and media richness.

The findings of this study contribute to the information systems literature by introducing the notion of herd behavior to judgments of truthfulness and deception. Also, the medium over which PI was presented significantly impacted the magnitude of imitation tendency: PI delivered through text-only medium led to a greater extent of imitation than when delivered in full audiovisual format. This suggests that media richness alters the degree of imitating others’ decisions such that the leaner the medium, the greater the expected extent of imitation.

This study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC).

The study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions.

The study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F-statistic indicates the general model's acceptability in the results.

The study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market.

This study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

The recent livestreaming commerce has magnified the role of influencer marketing, where the influencers are partnering with brands for product promotion. This study examines the impact of influencer attributes, interaction strategies and parasocial relationships on impulsive buying in livestreaming commerce.

A survey with 368 livestreaming commerce users was analyzed using the symmetric-thinking approach – partial least squares structural equation modeling (PLS-SEM) and asymmetric thinking approach – fuzzy set qualitative comparative analysis (fsQCA).

The results of PLS-SEM indicate that influencer trustworthiness, influencer interactivity and self-disclosure determine parasocial relationships, which in turn influence impulsive buying. The fsQCA finding returned three configurations with various combinations of the causal conditions (i.e. influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty) explaining the formation of impulsive buying.

These findings provide unique linear and nonlinear insights to explain the combinatory effects of influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty on impulsive buying in livestreaming commerce.

This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

The purpose of this paper is to provide an integrative picture of the state of the art of the literature on digitalisation of small and medium-sized enterprises (SMEs) as an enabler for their internationalisation process and as a comprehensive view of the specific domains impacted by digital technologies as well as their repercussions on the international outreach.

A systematic review which leverages a descriptive analysis of extant literature and an axial coding technique has been conducted to shed light on the current knowledge and to identify primary research areas and future research lines.

The research indicates that digitalisation impacts the internationalisation of SMEs in three specific domains: (1) internationalisation through the adoption of information and communication technologies (ICT) technologies and e-commerce platforms; (2) international expansion through the digitalisation of value chain activities and (3) international outreach through knowledge acquisition on digital platforms.

The value of this study is threefold. First, the authors attempt to systematically review the literature on SMEs digitalisation and internationalisation and provide a holistic perspective on the intertwining of these two research streams. Second, the authors propose a novel conceptualisation on the dimensions of SMEs digitalisation as enablers to internationalisation. Third, the authors put forward promising future lines of research.

1. Digitalisation represents a pivotal strategy that allows companies to build new strategic capabilities and is a propeller for SMEs internationalisation.

2. Through e-commerce, SMEs could compete at the same level of multinational companies but enduring lower costs of expansion.

3. Digital platforms allow SMEs to enhance the learning processes about international markets through an immediate access to relevant information.

4. Digital entrepreneurship has enabled SMEs to develop new configurations of value chain activities, evolving their business model or reaching new markets.

5. SMEs are changing the “business as usual” paradigm offering digital tools to build modular architectures that are scalable and agile in their evolution ability.

Digitalisation represents a pivotal strategy that allows companies to build new strategic capabilities and is a propeller for SMEs internationalisation.

Through e-commerce, SMEs could compete at the same level of multinational companies but enduring lower costs of expansion.

Digital platforms allow SMEs to enhance the learning processes about international markets through an immediate access to relevant information.

Digital entrepreneurship has enabled SMEs to develop new configurations of value chain activities, evolving their business model or reaching new markets.

SMEs are changing the “business as usual” paradigm offering digital tools to build modular architectures that are scalable and agile in their evolution ability.

Social media has progressively upgraded an interactive domain via online sociability and information-sharing. This study aims to formulate an information-sharing intention model by identifying the decisive role of intrinsic and extrinsic motivations.

Empirical data from 508 participants were collected to examine the structural model using structural equation modeling.

Results indicate that information-sharing intention is strongly promoted by intrinsic and extrinsic motivations. Furthermore, perceived herding, perceived crowd and intrinsic motivation boost substantially extrinsic motivation. Perceived herding is of utmost importance to extrinsic motivation, whereas emotional appeal and informative appeal are of paramount importance to intrinsic motivation. Moreover, source trust and exhibitionism are underlying motivations for intrinsic motivation.

The findings provide useful guidelines for practitioners to urge users into information-sharing via social media.

This study contributes significantly to the current literature by developing an effective mechanism of information-sharing through social media based on the motivational theory.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.

To remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.

Employing longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.

The results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.

This paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

The purpose of this study is to discover the motivating factors for cryptocurrency investment during an economic crisis in the MENA region, with reference to the economic crisis of 2019–2022, in Lebanon.

The authors used t-test, and logistic regressions on a sample of 254 Lebanese investors to differentiate between cryptocurrency investors, and non-investors. Linear regressions of a subsample of cryptocurrency investors determined the factors that explained increasing cash investment in cryptocurrencies. Data were collected from investors in Lebanon, which could limit the generalization of the research results across the MENA region.

Investors differed from non-investors in that they were male, owned investments in the stock, bond and commodity markets, had prior investment experience in cryptocurrencies, were risk-takers and had expectations of high returns. Investors increased the dollar investment in cryptocurrencies, if they were male, as they invested more funds in securities, had previously invested in cryptocurrencies and had stronger risk-taking propensity. Expectations of high returns drove investors to cryptocurrencies, but such expectations do not stimulate further cryptocurrency investment.

This study is an initial attempt to comprehend the reactions of investors in the MENA region to a currency crisis that triggered investment in cryptocurrencies following the collapse of fiat currencies, central bank default and restrictions on bank withdrawals.