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The Ottoman cultural legacy in Algeria is made up of a diverse range of architectural structures. The Algerian government strategy in connection with the Ottoman old buildings is to restore them into museums. This study will attempt to present a contrastive analysis between two old palaces being under restoration (refurbishment), and the goal is to propose a museum route by calculating the ambiance aimed at circulating the rooms by visitors.

The authors analyzed the architectural components of the various entities by observing in situ and taking measurements for a single case study to get a sense of the results for the mid-season (spring and fall). The configuration was next evaluated by modeling the space syntax and combining it with a simulation of daylight luminance, for the period when the authors could not make measurements on-site. The ultimate goal is to combine these findings to suggest the ideal in-route for the future museum.

This research allowed the authors to propose a museum itinerary adapted to the new vocation of the palaces, which considers the daylight as an element of composition in the spaces of circulation.

The paper proposes solutions to a flow management problem encountered in several similar palaces converted into museums.

The study aims to raise questions on the museum, and to preserve such heritage from neglect by giving it a new life more adapted to the needs of the Algerian society.

The authors believe that this contribution will be a creative solution for issues related to the operation of palaces that have been converted into museums.

The purpose of this paper is to emphasize the needs to understand the barrier and determinant factors in knowledge sharing (KS), to find the common ones and subsequently to build a general framework that can be referred to in designing a KS tool that addresses the common factors.

The approach comprises of two major steps which are to survey the past literature to determine the most common barriers and determinant factors from various unique KS domains and to qualify the factor as the common one based on its presence in at least three to five KS domains. The grounded theory is used to analyze the past literature and to perform categorization.

This paper helps in the summarization of categories and subcategories of barriers and determinants and demonstration on the mapping between them.

This paper has not proved the actual use of the framework in building a KS tool based on the framework.

The common factors are based on at least 60 references of KS implementation such that it is useful for large area of application domains that require building KS tools.

This paper presents the understanding on the common factors and association between the barriers and determinants in building the general framework in which the application of the framework is demonstrated using actor network theory.

Technological start-ups are significant contributor to the innovation and employment provider in an economy. Numerous technological start-ups are established every year; however, only a miniscule percentage of these technological start-ups sustain and scale up in the long run. The aim of this study is to investigate the factors that affect Indian technological start-ups’ competitiveness.

Case study analysis of two technological start-ups (namely, WayCool and Moglix) is undertaken to study the factors affecting the competitiveness of technological start-ups in India. Being a relatively underexplored theme of study in entrepreneurship and strategy, case analysis facilitates exploration and validation of factors influencing competitiveness. Information for case study analysis is drawn from secondary sources of information. The collected data undergoes deductive thematic analysis to systematically identify and examine recurring themes and patterns relevant to the competitiveness of Indian technological start-ups.

Case analysis reveals that innovation intensity, organisational agility and internationalisation influence competitiveness of technological start-ups. The importance of the role of each of these factors for entrepreneurial ventures has been highlighted in literature; however, their effect on competitiveness has not been examined in extant literature.

Being among the few studies on the competitiveness of technological start-ups in specific and start-ups in general, this study highlights the gap in the literature and suggests the need for examining the competitiveness of technological start-ups.

For the practitioners, this study reinforces the need for entrepreneurs to emphasise fundamental factors that build competitiveness. Subsequently, the sources of competitiveness shall enable the start-up to gain a competitive advantage.

This is among the few studies to have explored the competitiveness of technological start-ups in the Indian context.

E-commerce expands product and service reach, emphasizing the need for strategic market approaches to enhance e-service quality and drive sales growth. This paper aims to assess the relationship between the perceived quality of e-commerce platforms (characterized by measures of order and return convenience), customer satisfaction with online shopping and repurchase intention from online stores as well as examine whether demographic variables such as age, gender and area of residency (urban/rural) influence the ratings of each of these variables.

An online, self-administered survey gathered 108 valid responses from e-commerce customers. Data were analyzed in Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) through principal component analysis, confirmatory factor analysis and structural equation modeling (SEM) as well as correlation, descriptive statistics, difference of means tests and nonlinear regression.

Online shopping on e-commerce platforms is seen as convenient for both placing orders and managing returns. Additionally, consumers express satisfaction with their online shopping experiences and exhibit a strong intention to repurchase. The analysis revealed linear relationships between order convenience and customer satisfaction, between order convenience and repurchase intention and nonlinear relationships between return convenience, customer satisfaction and repurchase intention. No significant difference was found between the way the demographic variables rated the convenience, satisfaction and repurchase intention constructs.

This study contributes to the empirical literature on service quality in e-commerce by providing a streamlined model of the interactions among the factors as well as by isolating the nonlinear relationships and comparing results across three demographic variables. From a managerial standpoint, the findings suggest that strategies aimed at providing complete qualitative information and enhancing order and return convenience improve customer satisfaction and foster repurchase intention.

Tech start-ups (TSs) functioning in different domains have a responsibility of ensuring that domestic knowledge and capabilities are leveraged to minimize dependence on foreign organizations. Despite the growth of the ecosystem, while numerous TSs emerge, very few of them are able to survive, and of those that survive, very few scale up. The aim of this study is to identify the factors influencing the competitiveness of technological start-ups and to study the interrelationship and interdependence of these factors.

Modified total interpretative structural modeling (m-TISM) was employed for the current research. The analysis of what factors have an effect on competitiveness, how they affect it and why they affect it should be explored. The study begins by developing the list of factors through literature search, and further it is validated by expert opinion. A hierarchical model has been developed using m-TISM and MICMAC analysis to analyze the driving and dependency power of factors at each level.

Results show that the competitiveness of TSs is affected by organizational agility and internationalization. Factors present at the bottom level, namely entrepreneurial intensity, act as a strong driver for TSs. Team member commitment, transformational leadership, strategic alliances, knowledge sharing and organizational ambidexterity are middle-level factors.

This study is among the few articles that have explored competitiveness of TSs in the Indian context.

The purpose of this paper is to investigate the determinants of users' intention to continue to invest in cryptocurrencies. The paper also aims to examine the impact of hedonic motivation and the legal environment on perceived value in cryptocurrencies.

A questionnaire was designed to obtain data from 258 respondents in UAE. The Structural Equation Modeling – Partial Least Squares (SEM-PLS) was used to evaluate the research model and test the hypotheses.

The results of smart PLS path analysis showed that perceived value, hedonic motivation, gambling attitude, and price volatility were significant determinants of the continued intention to invest in cryptocurrency. This study also revealed that hedonic motivation enhances perceived value and improves the perception of cryptocurrencies value from user's perspective.

This study provides new insights into the literature on cryptocurrencies adoption, and delivers advanced understanding about the determinants of user's intention to continue investing in cryptocurrencies. In addition, the study provides important practical implications for cryptocurrencies companies to promote this financial technology to users by enhancing the knowledge of policy makers about how investors think and get motivated towards a continued investment of cryptocurrencies.

This study investigates how the maximum cultural intelligence (Max CQ) within a team – defined as the highest cultural intelligence level of an individual member – affects intra-team communication, conflict dynamics and, ultimately, team satisfaction and performance in global virtual teams (GVTs).

Utilizing quantitative research methods, this investigation draws on a dataset comprising 3,385 participants, which forms a total of 686 GVTs.

The study reveals that MaxCQ significantly enhances team communication, which in turn mitigates conflict, increases satisfaction and improves performance. It is noteworthy that the influence of MaxCQ on GVT success is more significant than the average cultural intelligence (CQ) of team members, providing critical insights for effective GVT management strategies.

The findings suggest that managers may optimize team dynamics not by uniformly increasing each member’s CQ but by concentrating on maximizing the CQ of one individual who can act as an influencer within the team. Strategically placing individuals with high CQ in GVTs can enhance overall team function.

While existing literature primarily examines the individual effects of CQ on communication and conflict management, this study sheds light on the collective interplay between MaxCQ, communication and conflict. It highlights the importance of MaxCQ, along with the frequency of team communication and conflict, in influencing team satisfaction and performance in GVTs.

This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search.

A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed.

The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency.

This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA.

This study empirically explores the impact of human capital, structural capital, relational capital and absorptive capacity on Fintech innovation. This study aims to investigate the potential impact of Fintech innovation on competitive agility and financial inclusion.

Data was collected from 283 participants in Jordan. Smart PLS software was used to test the hypotheses.

The findings reveal that human capital, structural capital, relational capital and absorptive capacity plays a significant role in Fintech innovation. Also, the outcome of path analysis confirms a significant impact of Fintech innovation on competitive agility and financial inclusion.

This study emphasizes the practical value of intellectual capital in fostering Fintech innovation for managers, banks, financial institutions and policymakers. Prioritizing investment in human, structural and social capital enhances organizational innovation.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.