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This study assesses postreplanting oil palm farming risks, analyzes seed procurement parameters, investigates seed institutions' performance factors and develops a framework for improved sustainability.

Incorporating data from 219 smallholder farmers in designated replanting areas, our study comprehensively evaluates seed supply performance, examining the roles of stakeholders and identifying potential risks in seed management. We assess these risks using the Risk Priority Number (RPN) methodology and Multidimensional Scaling (MDS) techniques.

The results show that the timing and quantity of oil palm seed supply have a relatively small impact on postreplanting failure risk. To mitigate this risk, focus on monitoring seed purity using high-quality Tenera oil palm-type seeds and early detection technology. Encourage seed-producing cooperatives to become legal seed producers for an inclusive system and consider smallholders' variety preferences.

This study’s significance lies in its comprehensive assessment of the risks associated with oil palm replanting on smallholder plantations, detailed analysis of critical parameters in seed procurement, investigation into the performance of palm oil seed institutions across various dimensions and development of a strategic framework to strengthen inclusive seed institutions for sustainable oil palm farming. This strategy holds valuable potential for the development of oil palm in Indonesia, particularly in expediting the smallholders' replanting program.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2023-0811

Sustainable construction deficits are common in developing economies, and resolutions are constrained by the failure to prioritise the plethora of available indicators. This study aims to report on overlapping indicators for benchmarking sustainable construction for construction organisations.

Online survey data were collected from construction professionals, academics and senior managers in government bodies. Pearson chi-squared tests and overlapping analysis were used to determine significant indicators. Kruskal–Wallis tests were used to determine statistically significant differences among the dimensions.

Overlapping analysis determined indicators significant for economic, environmental and social performance. Environmental protection and reporting (pollution and emissions) were significant for all three performance dimensions. The most significant indicators are economic performance (adequate competence of key project staff), environmental performance (environmental protection and reporting – pollution and emissions) and social performance (adequate sustainability expenditure by construction organisations). Significant differences due to dimensions existed for adequate competence of key project staff, sustainable construction and eco-design, adequate governance and organisational excellence of construction projects and satisfactory workers’ morale.

Determining overlapping indicators enables prioritised implementation that ensures sustainable construction. Excluding construction workers was a significant limitation for a holistic interrogation.

To the best of the authors’ knowledge, this is the first study to determine overlapping indicators for sustainable construction performance in Zimbabwe.

The purpose of this study is to successfully apply ultrasonic waves for the quick extraction of flax seed gum from flaxseed hull or whole seed and compare it to the standard technique of extraction.

The effect of the heating source, extracted time, temperature and pH of extracted solution on the extraction was studied. The obtained gum is subsequently used for silk screen printing on cotton, linen and viscous fabrics. Rheological properties and viscosity of the printing paste were scrutinized in the current study to get a better insight into this important polysaccharide. The output of this effort aimed to specify the parameters of the processes for printing textiles to serve in women’s fashion clothes by applying innovated handmade combinations of Islamic art motives using a quick and affordable method. Seven designs are executed, and inspiring from them, seven fashion designs of ladies’ clothes were designed virtually by Clo 3D software.

The result recorded that the new gum has excellent printing properties. In addition, they have better rheological properties, viscosity, chromatic strength and fastness qualities, all of which could help them in commercial production.

Flaxseed and three different fabric types (Cotton, Linen and Viscous) were used.

Synthesis of a new biodegradable thickener from a natural resource, namely, flaxseed, by applying new technology to save time, water and energy.

Synthesis of eco-friendly biodegradable thickener and used in textile printing alternative to the synthetic thickener.

This study aims to get rid of non-degradable polyvinyl chloride (PVC) waste as well as sunflower seed cake (SSC) waste by preparing eco-friendly composites from both in different proportions to reach good mechanical and insulating properties for antimicrobial and antistatic applications.

Eco-friendly composite films based on waste polyvinylchloride (WPVC) and SSC of concentrations (0, 10, 20, 30 and 40 Wt.%) were prepared using solution casting method. Further, the effect of sunflower seed oil (SSO) on the biophysical properties of the prepared composites is also investigated. Fourier transform infrared spectroscopy, X-ray diffraction (XRD), scanning electron microscope, mechanical, thermal, dielectric properties were assessed. Besides, the antimicrobial and biodegradation tests were also studied.

The crystallinity increases by rising SSC concentration as revealed by XRD results. Additionally, the permittivity (ε′) increases by increasing SSC filler and SSO as well. A remarkable increase in dc conductivity was attained after the addition of SSO. While raw WPVC has very low bacterial activity. The composite films are found to be very effective against staphylococcus epidermidis, staphylococcus aureus bacteria and against candida albicans as well. On the other hand, the weight loss of WPVC increases by adding of SSC and SSO, as disclosed by biodegradation studies.

The study aims to reach the optimum method for safe and beneficial disposal of PVC waste as well as SSC for antistatic and antimicrobial application.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

The study deploys Anthias' intersectional framework of social spaces and her concept of translocational positionality to explore the barriers to entrepreneurship for refugee entrepreneurs in the United Kingdom (UK). In particular, the study aims to assess how migrant identities require a specific form of business support.

A total of 32 semi-structured interviews with 14 refugee entrepreneurs and 18 business support agents were conducted between April and October 2022 and, together with field notes, were combined for thematic analysis in NVivo 12.

Organisational, representational, intersubjective and experiential barriers combined to create practical and psychological deterrents to entrepreneurship for refugees. However, an explicitly humanistic and de-centred approach to business support was (partially) able to counter such barriers.

Policymakers and business support agencies should consider intersectional characteristics and the importance of a compassionate and individual approach when designing business support programmes for refugee entrepreneurs.

Two intersectional concepts of social spaces and translocational positionality are brought into conversation with each other, creating a novel approach to framing the barriers to entrepreneurship for refugees.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

The purpose of this article is to develop an integrative framework of accelerator design to answer the question of what activities accelerators perform and how they function within a structured framework. Research on the functioning of accelerators as a mechanism for startup engagement produced multiple empirical results. However, the comparability of relevant research is strongly limited, currently hindering theoretical developments. Existing accelerator design models often differ and only partially overlap, which leaves extant literature with a fragmented and discordant conceptual understanding.

Based on a meta-synthesis method using qualitative analysis of 36 accelerator design articles, an integrative framework is developed. After identification of relevant literature, a renowned method for extracting, coding and synthesizing data on individual and cross-study level is applied to identify accelerator design constructs. Eventually, identified accelerator design constructs are integrated into a framework resting on the activity system lens of business model design.

The article reconciles fragmented knowledge on accelerator design and shows how accelerator design can be holistically conceptualized by 32 key activities clustered in eight design dimensions. The framework is complemented by an initial guideline for measurement. The findings further highlight formerly disregarded aspects of governance and community formation from a processual and structural perspective.

This article is the first to present a comprehensive picture of accelerator design integrating multiple empirical findings of prior research into a single coherent framework. This framework offers a shared foundation for future research exploring the delineations, functioning and impact of accelerators. From a practical perspective, the article provides managers of accelerators a guide to design, review and improve programs according to their value creation goals.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.