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Social commerce (s-commerce) offers community-based platforms that facilitate customer-to-customer interactions and the development of customers' social shopping-based experience. While prior research has addressed the role of customer engagement (CE) in boosting s-commerce-based sales and performance, insight into the effect of s-commerce attributes on CE remains tenuous. Addressing this gap, this study examines the role of specific s-commerce attributes (i.e. community, collaboration, interactivity and social dynamics) on CE, which is, in turn, proposed to impact customers' repurchase- and electronic word of mouth (eWOM) intention.

A web-based survey was deployed to target users of a popular s-commerce platform, Etsy.com. Partial least squares structural equation modeling (PLS-SEM) was, then, used to analyze the survey data collected from 390 users.

The results reveal that the four examined attributes positively affect CE. The findings also demonstrate CE's positive effect on customers' repurchase- and eWOM intention.

Though CE has been identified as a key s-commerce performance indicator, little remains known about the role of specific s-commerce attributes in driving CE, as, therefore, explored in this research. Specifically, the authors examine the role of s-commerce-based community, collaboration, interactivity and social dynamics on CE. Their analyses also corroborate that CE, in turn, drives customers' post-purchase (i.e. repurchase/eWOM) intention. Managerially, our findings can be used to develop more engaging s-commerce platforms.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

This study aims to investigate to what extent the predictability of the standard and poor’s 500 (S&P 500) price levels is enhanced by investors’ sentiments extracted from social media content, specifically platform X.

Two recurrent neural network (RNN) models are developed. The first RNN model is merely based on historical records and technical indicators. In addition to the variables included in the first RNN model, the second RNN model comprises the outputs of the sentiment analysis, performed using the TextBlob library. The study was conducted between December 28, 2011, and December 30, 2021, over 10 years, to obtain better results by feeding the RNN models with a significant quantity of data by extending the period and capturing an extensive timespan.

Comparing the performance of both models reveals that the second model, with sentiment analysis inputs, yields superior outcomes. The mean absolute error (MAE) of the second model registered 72.44, approximately 50% lower than the MAE of the technical model, its percentage value, the mean absolute percentage error, recorded 2.16%, and finally, the median absolute percentage error reached a value of 1.30%. This underscores the significant influence of digital platforms in influencing the behavior of certain assets like the S&P 500, emphasizing the relevance of sentiment analysis from social media in financial forecasting.

This study contributes to the growing body of literature by highlighting the enhanced predictive power of deep learning models that incorporate investor sentiment from social media, thereby advancing the application of behavioral finance in financial forecasting.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

Considering information processing theory, this study aims to examine how big data analytics (BDA) mediates the influence of e-procurement coordination (EPC) and e-procurement transactional (EPT) applications on transparency and accountability (TA) in the procurement processes of firms within the United Arab Emirates' private sector. Furthermore, it investigates the moderating role of information processing capabilities (IPCs) in the relationships among EPC, EPT and BDA to clarify their collective impact on enhancing TA and procurement performance.

Data were collected from procurement and information technology professionals in the UAE’s private sector through a Web-based survey. Established scales were used to assess e-procurement, BDA, TA, procurement performance and IPCs. Data were analyzed using partial least squares structural equation modeling.

Integrating e-procurement with BDA demonstrates the potential to improve TA and procurement performance in the UAE’s private sector. BDA is positively associated with EPC and EPT applications use, contributing to increased procurement TA and enhancing overall procurement performance.

Organizations can enhance procurement TA by adopting e-procurement and BDA technologies.

This study identifies the mediating role of BDA in the relationship between e-procurement and procurement TA. In addition, it investigates the moderating role of IPCs in the relationship between e-procurement and BDA.

This study assesses whether the strategic orientation of financial institutions to provide value to customers influences the dimensions of personal data privacy perceived by consumers of banking services. We also analysed whether these dimensions directly influence the value in use and, indirectly, the reputation of financial institutions.

Based on the literature, a model was developed to verify the proposed relationships. To test the model, we collected data via an online questionnaire from 2,422 banking customers, with analysis using structural equation modelling with partial least squares estimation.

The results suggest that strategic value orientation tends to have a direct positive influence on the constructs knowledge, control, willingness to value privacy and trust in sharing personal information and a direct negative influence on the personal data privacy experience. Three dimensions of personal data privacy (knowledge, willingness to value privacy and trust in sharing personal information) tend to have a direct positive influence on value in use. The results showed that the dimensions of personal data privacy experience and control had a significant and negative impact on the value in use construct. Another finding is the positive influence of value in use on organizational reputation. Investing in strategic value orientation can generate consumer perceptions of personal data privacy, which is reflected in the value in use and reputation of banks.

This study is theoretically original because it brings up the organizational reputation of financial institutions based on the strategic orientation to offer value to customers, personal data privacy and the value in use of banking services. The study of these relationships is unprecedented in the literature.

Gillette’s historically successful “razor and blade” business model (RBM) has been a promising benchmark for multiple businesses across diverse industries worldwide in the past several decades. The extant literature deals with very few nuances of this business model notwithstanding the fact that there are several variants of this business model being put to practical use by firms in diverse industries in grossly metaphorically equivalent situations.

This study adopts the 2 × 2 truth table framework from the domains of mathematical logic and combinatorics in fleshing out all possible (four logical possibilities) variants of the razor and blade business model for further analysis. This application presents four mutually exclusive yet collectively exhaustive possibilities on any chosen dimension. Two major dimensions (viz., provision of subsidy and intra- or extra-firm involvement in the making of razors or blades or both) form part of the discussion in this paper. In addition, this study synthesizes and streamlines entrepreneurial wisdom from multiple intra-industry and inter-industry benchmarks in terms of real-time firms explicitly or implicitly adopting several variants of the RBM that suit their unique context and idiosyncratic trajectory of evolution in situations that are grossly reflective of the metaphorically equivalent scenario of razor and recurrent blades. Inductive method of research is carried out with real-time cases from diverse industries with a pivotally common pattern of razor and blade model in some form or the other.

Several new variants of the razor and blade model (much beyond what the extant literature explicitly projects) have been discovered from the multiple metaphorically equivalent cases of RBM across industries. All of these expand the portfolio of options that relevant entrepreneurial firms can explore and exploit the best possible option chosen from them, given their unique context and idiosyncratic trajectory of growth.

This study has enriched the literature by presenting and analyzing a more inclusive or perhaps comprehensive palette of explicit choices in the form of several variants of the RBM for the relevant entrepreneurial firms to choose from. Future research can undertake the task of comparing these variants of RBM with those of upcoming servitization business models such as guaranteed availability, subscription and performance-based contracting and exploring the prospects of diverse combinations.

Smart entrepreneurial firms identify and adopt inspiring benchmarks (like razor and blade model whenever appropriate) duly tweaked and blended into a gestalt benchmark for optimal profits and attractive market shares. They target diverse market segments for tied-goods with different variants or combinations of the relevant benchmarks in the form of variegated customer value propositions (CVPs) that have unique and enticing appeal to the respective market segments.

Value-sensitive customers on the rise globally choose the option that best suits them from among multiple alternatives offered by competing firms in the market. As long as the ratio of utility to price of such an offer is among the highest, even a no-frills CVP may be most appealing to one market segment while a plush CVP may be tempting to yet another market segment simultaneously. While professional business firms embrace resource leverage practices consciously, amateur customers do so subconsciously. Each party subliminally desires to have the maximum bang-to-buck ratio as the optimal return on investment, given their priorities ceteris paribus.

Prior studies on the RBM have explicitly captured only a few variants of the razor and blade model. This study is perhaps the first of its kind that ferrets out many other variants (more than ten) of the razor and blade model with due simplification and exemplification, justification and demystification.

The purpose of this research is to address the complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance. The primary goal is to minimize total tardiness, earliness and total completion times simultaneously. This study aims to provide effective solution methods, including a Mixed-Integer Programming (MIP) model, an Epsilon-constraint method and the Nondominated Sorting Genetic Algorithm (NSGA-II), to offer valuable insights into solving large-sized instances of this challenging problem.

This study addresses a multiobjective unrelated parallel machine scheduling problem with sequence-dependent setup times and periodic machine maintenance activities. An MIP model is introduced to formulate the problem, and an Epsilon-constraint method is applied for a solution. To handle the NP-hard nature of the problem for larger instances, an NSGA-II is developed. The research involves the creation of 45 problem instances for computational experiments, which evaluate the performance of the algorithms in terms of proposed measures.

The research findings demonstrate the effectiveness of the proposed solution approaches for the multiobjective unrelated parallel machine scheduling problem. Computational experiments on 45 generated problem instances reveal that the NSGA-II algorithm outperforms the Epsilon-constraint method, particularly for larger instances. The algorithms successfully minimize total tardiness, earliness and total completion times, showcasing their practical applicability and efficiency in handling real-world scheduling scenarios.

This study contributes original value by addressing a complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance activities. The introduction of an MIP model, the application of the Epsilon-constraint method and the development of the NSGA-II algorithm offer innovative approaches to solving this NP-hard problem. The research provides valuable insights into efficient scheduling methods applicable in various industries, enhancing decision-making processes and operational efficiency.

The need for mechanization in company operations during the pandemic has been further illustrated by the pressure to use recent technologies for sustainable business practices. Recent technologies like Internet of Things (IoT), artificial intelligence (AI), big data and cloud are being abandoned in favour of automating business activities during and after the pandemic, to build flexibility and sustainability. The objective of this paper is to give an outline of the literature on the bang of digital metamorphosis on organizational adaptability. The paper focuses on the future of business sustainability from dislocations by espousing recent technologies from different perspectives. As well as the anticipated disruptive developments, the benefits of technology on economics and business are also being felt, but still in their early stages. Similar ideas and methods must be implemented as quickly as is practical, and governments and enterprises must be ready and willing to do so. The transition to a commercial environment that emphasizes technology from alternative distribution channels will have a direct influence on organizational structures. Additionally, they could have training in or experience in positive sciences, which will aid in creating the corporate environment of the future sustainably. Absolutely the variety of technologies in business helps to accelerate business activities and attain the maximum goal before and after the pandemic. It still appears that a hypothetical model is required that could simplify the incorporation of using these technologies during a disaster with business processes. The findings may be applied to manage technology and speed up corporate resilience for a better economy.