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The increased digital interactions in the B2B industry have enhanced the importance of customer engagement as a measure of firm performance. This study aims to map and analyze temporal and spatial journeys for customer engagement in B2B markets from a bibliometric perspective.

The extant literature on customer engagement research in the B2B context was analyzed using bibliometric analysis. The citation analysis, keyword analysis, cluster analysis, three-field plot and bibliographic coupling were used to map the intellectual structure of customer engagement in B2B markets.

The research on customer engagement in the B2B context was studied more in western countries. The analysis suggests that customer engagement in B2B markets will take centre stage in the coming times as digital channels make it easier to track critical metrics besides other key factors. Issues like digital transformation, the use of artificial intelligence for virtual engagement, personalization, innovation and salesforce management by leveraging technology would be critical for improved B2B customer engagement.

The study provides a comprehensive reference to scholars working in this domain.

The study makes a pioneering effort to comprehensively analyze the vast corpus of literature on customer engagement in B2B markets for business insights.

This study aims to identify and rank the key success factors linked with digitalization in the Indian construction sector. Because the construction firms in India are in the early stages of implementing digitalization in their operations, it provides a framework from which they may focus on the effectiveness of digitalization.

This research study examines 12 success factors related to digitalization in construction, which are derived from various sectors. Furthermore, experts from the construction industry and academia have validated these factors with respect to the Indian construction sector. The multi-criteria decision-making techniques are further used to examine the interrelationship, ranking and weightage of digitalization success. Finally, the success factors are validated through a questionnaire-based empirical study followed by ranking using a t-test. The results from both approaches (company-specific and generalized) are compared and discussed.

This research identifies that selecting appropriate digital methods and techniques is a critical success factor as far as digitalization in the Indian construction scenario is concerned. Besides that, continuous monitoring and control in digital implementation significantly impact other factors.

While similar results are obtained from approaches adopted in the study, a few success factors appear to differ in terms of their ranking position. Further studies can explore the finer details that can explain the behavior pattern. This study can also be extended by assessing the structural relationship among the identified factors that can throw more light on the dynamics of the continuation of digitalization in construction which can further help in formulating policies or digitalization rollout.

The outcome of this study sheds light on construction business knowledge by stressing key success elements connected to digitalization in construction processes in the Indian construction sector. Moreover, this study shows that the success of digitalization in construction is similar to that of transformation in the information technology industry, where adopting suitable digital methods and techniques plays a vital role in the transformation process.

Despite the multiple benefits of construction digitalization, limited research focuses on digitalization success factors, making this study unique. Furthermore, this study demonstrates that integrating Fuzzy decision-making trial and evaluation laboratory and maximum mean de-entropy approaches may be used to successfully prioritize success factors in the nascent stage of construction digitalization.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

This study aims to study the interaction effects between a rectangular supersonic jet with a flat wall computationally using wall length as a parameter. The purpose of this study is to investigate the effect of change in wall length on supersonic core length (SCL) reduction, jet deflection and jet decay behavior.

The design Mach number and aspect ratio at the rectangular exit were 1.8 and 2, respectively. To study the wall length effects on jet-wall interactions, wall length (L_w) was varied as 0.5D_h, 1D_h, 2D_h, 4D_h and 8D_h, where D_h was the hydraulic diameter of the nozzle exit. The flat wall with the matching width of the rectangular exit section of a supersonic nozzle was placed at the nozzle exit such that the supersonic jet grazed past the wall. The studies were carried out at over-expansion [nozzle pressure ratio (NPR) = 4], near optimum expansion (NPR = 6) and under-expansion (NPR = 8) levels.

Results indicated that significant reduction in wall-bounded SCL was noticed in the range of 0.5D_h  ≤  L_w ≤ 1D_h for both over-expansion and under-expansion conditions. At L_w ≥ 4D_h, SCL got enhanced at NPR = 4 and 6 but had a negligible effect at NPR = 8.

Thrust vector control, noise reduction and easy take-off for high-speed aircraft.

The effect of change in flat wall length on interaction characteristics of a rectangular supersonic jet was not studied before in terms of SCL reduction and jet decay behavior.

Education is one among the major service sectors which is continuously growing and contributing significantly to a country’s economy. Students’ positive feedback through word-of-mouth (WOM) is one of the key influences attracting new admissions thereby providing competitive advantage for a university to sustain. There are numerous antecedents identified and implemented to enhance positive WOM and increase intakes in higher education however the students’ choice is still being unpredicted. This study attempts to develop a framework that exemplifies the links between service quality (SQ), relational trust (RT) and students' attitudes toward institutions.

A correlational research design was adopted with a non-probability convenience sampling technique, the data were collected from students in public and private higher education institutions (HEIs) across India. Multivariate regression was the statistical tool used to estimate the path model. SmartPLS 3.0 software performing structural equation modelling (SEM) helped in determining the coefficient values.

The result of the study indicated the magnitude and directional relationship between SQ and trust and justified that they are the key determinants of building a positive attitude towards the institution, enhancing the intention to recommend it among peer groups.

Academic institutions and their public relations departments must prioritize reducing SQ gaps and create strategies to build strong RT among all institution stakeholders to gain a competitive advantage. Socially, this study aims in assisting universities in establishing high-quality education.

The empirical estimation of the relationships between trust, attitude, quality and intention provides the reasons for incorporating and building positive WOM among students’ benefit institutions over the long run.

The purpose of this study is to delve into the intricate terrain of assimilating sustainability practices into digital accounting and finance, centring on the transformative dynamics introduced by artificial intelligence (AI)-enabled FinTech. The primary objective is to scrutinize critical lacunae in existing literature, exploring how organizations can meticulously construct comprehensive sustainability frameworks. Simultaneously, the study investigates the protracted repercussions of AI-enabled FinTech on the enduring sustainability paradigms.

Executing a systematic literature review, the research engaged in the meticulous identification and assessment of a voluminous pool of 1,158 articles. Using a judicious two-phase strategy, the scrutiny distilled a mere 64 pertinent articles, subjecting them to rigorous evaluation encompassing methodologies, contributions and overall quality. The Fuzzy Delphi method was used to elicit expert opinions and facilitate consensus-building, leveraging fuzzy logic to accommodate uncertainties in the data.

The review navigates the convoluted impact of AI across diverse sectors, accentuating its transformative imprint on realms such as health care, finance and transportation. Specifically, in the financial domain, the discerning eye of AI-enabled FinTech optimizes investment portfolios, augments risk assessment, propels financial inclusion and streamlines the intricate landscape of sustainability reporting. The study meticulously pinpoints research gaps encompassing investment optimization, risk management, financial inclusion, sustainability reporting and ethical considerations within the intricate milieu of AI-enabled FinTech. This research contributes to the existing body of knowledge by synthesizing intricate thematic strands, discerning overarching trends and spotlighting critical voids in the synthesis of sustainability practices and AI-enabled FinTech. The findings resonate with far-reaching implications, emphasizing the exigency of comprehensive investigations into the longitudinal sustainability ramifications instigated by AI-enabled FinTech.

The study underscores the imperative of crafting robust ethical frameworks for the equitable and transparent deployment of AI solutions within the intricate landscape of FinTech. Moreover, this research stands poised to shape organizational strategies, inform regulatory frameworks and guide investment decisions, thereby catalyzing the cultivation of conscientious and sustainable financial practices.

This paper aims to present a study on stability analysis of Jeffrey fluids in the presence of emergent chemical gradients within microbial systems of anisotropic porous media.

This study uses an effective method that combines non-dimensionalization, normal mode analysis and linear stability analysis to examine the stability of Jeffrey fluids in the presence of emergent chemical gradients inside microbial systems in anisotropic porous media. The study focuses on determining critical values and understanding how temperature gradients, concentration gradients and chemical reactions influence the onset of bioconvection patterns. Mathematical transformations and analytical approaches are used to investigate the system’s complicated dynamics and the interaction of numerous characteristics that influence stability.

The analysis is performed using the Jeffrey-Darcy type and Boussinesq estimation. The process involves using non-dimensionalization, using the normal mode approach and conducting linear stability analysis to convert the field equations into ordinary differential equations. The conventional thermal Rayleigh Darcy number RDa,c is derived as a comprehensive function of various parameters, and it remains unaffected by the bio convection Lewis number Łe. Indeed, elevating the values of ζ and γ′ in the interval of 0 to 1 has been noted to expedite the formation of bioconvection patterns while concurrently expanding the dimensions of convective cells. The purpose of this investigation is to learn how the temperature gradient affects the concentration gradient and, in turn, the stability and initiation of bioconvection by taking the Soret effect into the equation. The results provide insightful understandings of the intricate dynamics of fluid systems affected by chemical and biological elements, providing possibilities for possible industrial and biological process applications. The findings illustrate that augmenting both microbe concentration and the bioconvection Péclet number results in an unstable system. In this study, the experimental Rayleigh number RDa,c was determined to be 4π2at the critical wave number ( δcˇ) of π.

The study’s novelty originated from its investigation of a novel and complicated system incorporating Jeffrey fluids, emergent chemical gradients and anisotropic porous media, as well as the use of mathematical and analytical approaches to explore the system’s stability and dynamics.

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.

Digital platforms that offer on-demand gig work, while providing work opportunities in the economy, have raised social and economic concerns. Though extensive research on regulation of the gig economy exists, the effect of economic regulations on the welfare of the workers is not well understood. In this work, this paper aims to specifically study the effect of minimum wage and leisure regulations on the unemployment rate and offered wages. This paper also analyses the effect of up-skilling of the workers on unemployment and wages.

This paper builds an agent-based model of the labour market with heterogeneous workers and online platform firms that interact to match supply and demand. This paper also interviewed online workers in the two under-studied markets in online beauty and house maintenance services in India and included salient observations in to the model. This paper further validates the model findings with the interview observations.

Extensive simulations of the model indicate that the regulator's intervention on minimum wage and leisure reduces unemployment and offers better wages/leisure in the short term. However, these cannot be sustained unless the workers upgrade their skills, thereby improving their value to the employers. This paper also corroborates the authors’ interview observations on platforms deviating on stated worker contracts by simulating the same in the model. This paper finds that when platforms deviate on their stated incentive schemes, the unemployment rate tends to increase. This paper also finds that the emergence of online platforms in an erstwhile off-line market decreases the average unemployment rate with a moderate increase in the offered wage and leisure.

In this work, the focus has been to determine the worker-platform dyadic relationship. However, this is affected by consumer-related attributes such as ratings and associated reputation systems to promote trust between different stakeholders. Examining such a triadic relationship between consumers, platform and workers is required to comprehensively address the challenges of online gig economy.

Skilling and training are critical for worker mobility across tasks and jobs, especially in the gig economy. Gig workers, in general, seek to improve their skill level through self- or platform-enabled training programmes. The workers are able to generate more revenue through the new skills and hence can improve their reservation wage as well. This in turn increases average offered wages and reduces the overall unemployment in the sector. Despite attempts to classify gig workers as formal workers by labour laws across countries, there is resistance from online platforms. This is due to increased liability and responsibility that the platforms have to incur that possible increase their costs and expenses. This study shows that regulations, such as minimum leisure or minimum wage, increase the average wage or leisure in the market and increase unemployment. However, this might be a short-term phenomenon. In the long term, the gig workers benefit by enhancing their skills to not only stay employed but also bargain for better wages and leisure. The governments can play a larger role by facilitating upskilling programmes for the gig workers.

An extensive literature survey indicates that while most of the work on gig economy regulation emphasises the social and legal aspects, this work is unique in modeling the techno-economics of gig work. Further, while most of the economic research on gig work, focuses on consumers, this work focuses on the under-researched area of worker welfare. This paper also validates the model results with findings from the interviews with gig workers.

The purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.

This study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.

The study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.

The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.

The results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.