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Bicycle sharing offers a novel way to create smart and sustainable mobility solutions for the future. The purpose of this study is to draw on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) framework for identifying the factors necessary to predict bike-sharing intention among users in India.

Data were collected through a questionnaire distributed across four major cities in India, and 515 responses were analyzed. A sequential approach was employed to analyze the data using Partial Least Square–Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA).

The findings from PLS analysis revealed that performance expectancy, effort expectancy, facilitating conditions, hedonic motivation and price value are the salient variables that affect users' intentions to participate in bike sharing. In addition, based on fsQCA, six configurations of causal conditions are presented as intermediate solutions that produce the same results. Although antecedent conditions, such as habit and social influence, had an insignificant effect on individuals' BSI, they create conditions sufficient to encourage users' participation in bike sharing in combination with other variables.

A few limitations of this research and the implications of the findings in terms of theory and policy implications are also discussed.

The reported study is one of the earliest to explain bike-sharing adoption in India using the UTAUT 2 model.

Because family businesses are highly complex enterprises, researchers need appropriate theoretical and methodological tools to study them. The neoconfigurational perspective and its accompanying method, qualitative comparative analysis, are particularly well suited to phenomena characterized by complex causality, but their uptake in family business research has been slow and fragmented. To remedy this, the authors highlight their unique ability to address research questions for which other approaches are not well suited and discuss how they might be applied to family business phenomena.

The authors introduce the core tenets of the neoconfigurational perspective and how its set-theoretic epistemology differs from traditional approaches to theorizing and analysis. The authors then use a dataset of family firms to present a primer on conducting qualitative comparative analysis and interpreting the results.

The authors find that family firm resources can be combined in multiple ways to affect business survival, suggesting that resources are substitutable and complementary. The authors discuss how the unique features of the neoconfigurational approach, namely equifinality, conjunctural causation and causal asymmetry, can be fruitfully applied to break new ground in scholarly understanding of family businesses.

This article allows family business researchers to apply the neoconfigurational approach without first having to consult multiple and disparate sources often written for other disciplines. This article explicates how to leverage the theoretical and empirical advantages of the neoconfigurational approach in the context of family businesses, supporting a more widespread adoption of the neoconfigurational perspective in family business research.

Failed deliveries (i.e. deliveries not accomplished due to the absence of customers) represent a critical issue in B2C (Business-to-consumer) e-commerce last-mile deliveries, implying high costs for e-commerce players and negatively affecting customer satisfaction. A promising option to reduce them would be scheduling deliveries based on the probability to find customers at home. This work proposes a solution based on presence data (gathered through Internet of Things [IoT] devices) to organise the delivery tours, which aims to both minimise the travelled distance and maximise the probability to find customers at home.

The adopted methodology is a multi-method approach, based on interviews with practitioners. A model is developed and applied to Milan (Italy) to compare the performance of the proposed innovative solution with traditional home deliveries (both in terms of cost and delivery success rate).

The proposed solution implies a significant reduction of missed deliveries if compared to the traditional operating mode. Accordingly, even if allocating the customers to time windows based on their availability profiles (APs) entails an increase in the total travel time, the average delivery cost per parcel decreases.

On the academic side, this work proposes and evaluates an innovative last-mile delivery (LMD) solution that exploits new AI (Artificial Intelligence)-based technological trends. On the managerial side, it proposes an efficient and effective novel option for scheduling last-mile deliveries based on the use of smart home devices, which has a significant impact in reducing costs and increasing the service level.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.

This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.

The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.

This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.

Supply chain has long faced a persistent workforce shortage. To help both organizations and the field create environments that are more conducive to employee retention, the authors investigate the outcomes of supply chain employee trust in their supervisor.

Applying person-environment fit theory, the authors evaluate the well-established antecedents to trust in supervisor ability, benevolence and integrity (ABI) relative to person-job (P-J) fit and person-vocation (P-V) fit of US supply chain employees.

Confirmatory factor analysis indicates that ABI is best modeled as dimensions of a second-order formative trust construct rather than as its antecedents. However, PLS-SEM provides somewhat unconvincing support for the impacts of ABI-trust. Instead, qualitative comparative analysis (QCA) delineates that all three ABI dimensions are not always needed for P-J and P-V fit in supply chain. Some employees respond to affective-based (i.e. benevolence) trust and others to cognitive-based (i.e. ability and integrity) trust.

The QCA results offer specific recommendations for supply chain organizations to enhance employee trust in supervisors to succeed in the struggle for labor.

The results counter extant trust theory, encouraging scholars to consider ABI as distinct dimensions of trust. The study also demonstrates the importance of considering QCA in supply chain research to meaningfully expand contributions to theory and practice.

This paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.

This paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.

The model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.

The model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.

The applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.

The model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

As a developing nation, Bangladesh still has scarce technological applications in the construction sector, which results in construction delays. This paper aims to propose a framework that will diminish manual labor, reduce human error and apply four-dimensional (4D) building information modeling (BIM)-based solutions to mitigate and prevent construction project delays.

First, a systematic literature review was conducted on analyzing the construction delay scenario in the context of Bangladesh and other countries. Next, a 4D BIM-based framework was developed using Autodesk Navisworks Manage. Finally, it was used to run on-site simulations on an ongoing construction project which faced delays because of design errors and inefficient planning.

Affirmative results were found from applying these methods through real-time project simulation. The current status of the project and the status after using BIM technology were compared. It was observed that during both the preconstruction and execution phases, the application of 4D BIM could reduce the delay posed by design error and inefficient planning.

The project manager and the design engineers can use these frameworks to review their projects. For the design engineers, the preconstruction phase portion of the framework will help identify the probable errors in the design. For the project managers, keeping track of time using the execution phase portion of the framework will be resourceful.

To the best of the authors’ knowledge, this study is the first to assess the significant delay factors endemic in Bangladesh and develop a BIM-based technological solution. This study is solely dedicated to reforming the construction techniques in Bangladesh through the application of 4D BIM technology.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

This study’s goal is to research and describe developing green technology and green business models, as well as their commercial advantages. However, it focuses on how old business breakthroughs and models may be transformed into new green technologies and innovations that can affect the global business environment. This research concentrates on the technology component of green technologies and provides information on a variety of relevant emerging green business models and innovations such as energy efficiency, renewable energy consumption, commodities and systems, environmental protection, nontoxic materials, and waste minimization, among others. Additionally, this research connects Emerging Green Technology (EGT) innovations to the business for sustainability. It also emphasizes the advantages of green technology adoption in business specially in developing economies.

For that purpose, a Systematic Literature Review (SLR) was performed based on the “inclusion/exclusion criteria” and “PRISMA flow diagram method”. The first screening and quality evaluation rejected 67 publications, 8 in eligibility and 10 in credibility. In the subsequent round, 45 papers out of 210 that met the search parameters were included to access the findings.

This study provides a foundation for future research into sustainable economic potential by examining the development, breakthroughs, obstacles, future trends and new research prospects of EGTs. As such, this research will serve as a helpful resource for economically growing nations looking to foster long-term growth in their businesses through sustainable development strategies.

The first limitation of the study is the generalization of the findings. The sample size of the study is limited. For that purpose, an SLR and “PRISMA flow diagram” methods were performed by “inclusion/exclusion criteria” for literature review. Except these, there are many new techniques of SLR. Further research with more sampling and sophisticated tests could produce better results for this study.

This research provides valuable insights for businesses and politicians seeking to protect the environment, promote economic growth and create a sustainable society. The findings will be particularly useful for emerging nations grappling with issues related to technical innovation, safe environmental practices, reliable renewable energy sources, and ecological and economic growth.

This study’s findings will aid in the design and implementation of new green technology and innovations in current businesses, which will assist in limiting climate change and eventually affect their move to sustainable growth paradigms. For emerging nations, this study will be useful in addressing issues about technical innovation, safe environmental conditions, reliable renewable energy sources as well as ecological and economic growth. According to our research results, the authorities of developing nations would gain from utilizing green business models and technology to spur economic development. Our findings should thus add to the current body of knowledge.