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Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.

This study aims to outline the research field of initial public offerings (IPOs) pricing and performance by combining bibliometric analysis with a systematic literature review process.

The study uses over three decades of IPO publication records (1989–2020) from Scopus and Web of Science databases. An analysis of keyword co-occurrence and bibliometric coupling was used to gain insights into the evolution of IPO literature.

The study categorized the IPO research field into four primary clusters: IPO pricing and short-run behaviour, IPO performance and influence of intermediaries, venture capital financing and top management and political affiliations and litigation risks. The results offer a framework for delineating research advancements at different stages of IPOs and illustrate the growing interest of researchers in IPOs in recent years. The study identified future research potential in the areas of corporate governance, earning management and investor sentiments related to IPO performance. Similarly, the study highlighted the opportunity to test multiple theoretical frameworks on alternative investment platforms (SME IPO platforms) operating under distinct regulatory environments.

To the best of the authors’ knowledge, this paper represents the first instance of using both bibliometric and systematic review to quantitatively and qualitatively review the articles published in the area of IPO pricing and performance from 1989 to 2020.

The purpose of this study is to extend Bippus and Young’s (2005) study and examine the effectiveness of the “I-you,” “I,” “You,” “We,” “But” and Question-based “Why” statements from Winer’s (2021) verbal coding program of conflict management using Bandura’s (1977) social learning theory (SLT).

Mixed methods were used using 175 university students from Texas and New York. A cross-sectional convenience sampling approach was conducted. Survey data was collected using Qualtrics.

Descriptive results demonstrated that the “We” statement was the most passive, the “I-you” statement was the most assertive and the “But,” “I,” “You” and Question statements were perceived to be aggressive. In addition, assertive “I-You” statements were perceived to be more effective in resolving the conflict and maintaining a relationship, whereas aggressive statements were less likely to resolve the conflict and maintain the relationship. Qualitative themes also support the “I-You” statement as the most assertive, while the “But,” “You” and “I” statements were found to be the most aggressive statements.

Implications and applications are discussed to stimulate future research among researchers and practitioners when addressing conflict. Being aware of the verbal statements that de-escalate conflict may be helpful in solving conflict in interpersonal, family and professional relationships. Future trainings can adopt effective verbal statements to resolve conflict when experiencing anger issues. Future research can continue to investigate verbal communication statements using SLT to help practitioners and managers address conflict in interpersonal relationships.

This study examines verbal statements in relation to communication styles and conflict management.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

The research aims to identify the United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site (WHS) managers' perception of cultural sustainability concepts and elements in tourism.

A literature review was carried out to identify the main speeches and characteristic elements of cultural sustainability present in the academic field, and then a quantitative survey was carried out with 60 managers of UNESCO WHSs to evaluate their perception of the theme.

The results show that managers believe that the concept of cultural sustainability is much more closely linked to the social dimension of sustainability than to the economic and environmental dimensions. Thus, elements such as the preservation of tangible and intangible assets, the participation of society in the management of heritage, the democratization of access, the guarantee of the appreciation of culture and its preservation for future generations are basic elements for cultural sustainability. Finally, although they believe that understanding the concept of cultural sustainability is fundamental to their activities, they still lack knowledge of how to measure cultural sustainability in their spaces.

Although the theme of sustainability has several published studies, most of the work focuses only on studies of its social, economic and environmental dimensions. The debates on the role of culture in sustainability are still incipient, and understanding the managers' perception of the topic enables the creation of more effective strategies that guarantee cultural sustainability in heritage by tourism.

This study aims to reveal the knowledge structure of social media influencer marketing literature by performing science mapping analysis through a state-of-the-art bibliometric approach to determine the current and future trends. Social media influencer marketing is one of the most effective approaches to presenting a brand and offering value to consumers via social media.

This study evaluates the knowledge structure to uncover the emerging trends and future predictions in social media influencer marketing through bibliographic coupling and co-word analysis. In total, 917 journal publications were retrieved from the Web of Science database and analyzed using VOSviewer software.

The central theme in social media influencer marketing reflects digital engagement between influencers and followers and communication between influencers and followers. The theoretical and managerial implications are discussed.

This study unleashes the knowledge structure according to the fundamental literature of social media influencer marketing and the underlying themes related to the phenomenon.

This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst antecedents of financial literacy and how they influence each other.

A two-phased multicriteria decision-making (MCDM) technique consisting of best-worst method and interpretive structural modeling (BWM-ISM) was employed for pair-wise comparison, assigning weights, ranking and establishing the relationship among antecedents of financial literacy.

Results suggest that use of Internet (SF1), role of financial advisors (SF3) and education level of individuals (DS7) are top ranked antecedents, whereas masculinity/feminity, language and power distance in society are the least ranked antecedents of financial literacy. Findings will help both academicians and practitioners focus on the key factors and make efforts to increase financial literacy by minimizing resource usage.

The current study provides clarity among antecedents of financial literacy by following BWM-ISM approach for the first time in the financial literacy context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0746