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The primary purpose is to present empirical measures of variables relating to practices engaged in by site‐based teams, and then to use these variables to test a model predicting significant outcomes of site‐based decision making. The practice variables of site‐based management (SBM) teams are essential in promoting research within a distributed leadership framework.

A path model is computed to test the relationships between factors relating to the support received by site‐based teams; site team communication and decision‐making practice, and perceived outcomes of SBM. Measures are based on survey data collected from 367 team members in 50 schools from fifteen school districts in a northeastern state in the US.

Results show that different factors relating to the support provided to site‐based teams and practices employed by these teams emerge as statistically significant predictors of various outcomes. Results suggest that the resources provided to support site teams, e.g. the devolution of decision‐making power, results in enhanced stakeholder influence, but whether this influence results in better decisions or improvement in teaching and learning depends on the communication and decision‐making practices site teams employ within a distributed leadership framework.

This study sought to identify site team decision making and communication processes that reflect how site teams conduct their work, defining how members of site teams perceive the “rules of the game.” In doing so, it offers a new and different perspective on how such processes impact outcomes associated with shared decision‐making processes, and thus a better understanding of the complex dynamics of school‐site decision making and the distribution of leadership in schools.

As public–private partnerships (PPPs) have become preferred and veritable approach to deliver affordable housing, the seemingly lack of understanding of the significant factors that impact on success has become a notable setback. This study aims to delineate significant factors that can support decisions in affordable PPP public housing delivery.

Largely, a questionnaire survey was adopted to elicit insights from practitioners, policymakers and experts to develop an evaluative decision support model using an analytical hierarchy process and multi-attribute utility technique approach. Further, an expert illustration was conducted to evaluate and validate the results on the housing typologies.

The results revealed that energy efficiency and low-cost green building materials scored the highest weighting of all the criteria. Furthermore, multi-storey self-contained flats were found to be the most preferred housing typology and were significantly influenced by these factors. From the model evaluation, the scores on the factors of sustainability, affordability, cultural values and accountability were consistent across all typologies of housing whereas that of benchmarking, governance and transparency were varied.

The decision support factors captured varied dimensions of key factors that impact on affordable PPP housing that have not been considered in an integrated manner. These findings offer objective and systematic support to decision-making in affordable PPP housing delivery.

Industrialized construction (IC) is promoted to address some of the ills associated with the processes and products of the traditional construction approach. With several successful projects, IC is progressively becoming a preferred alternative construction approach and spurred the interest of contractors, developers and housing authorities in the technology. Increasingly, these stakeholders are keen to ascertain the compatibility and feasibility of using IC in their projects. This paper aims to develop a knowledge-based decision support framework for implementing industrialized construction projects (ICPs) that can facilitate better and informed decision-making when deciding to implement ICPs.

A comprehensive literature review was implemented to recruit 40 decision support factors (DSFs) and grouped into project requirements, location and site attribute, labour considerations and organizational factors. A 3-member expert panel validated the relevance of 35 DSFs, which became candidates for a structured questionnaire survey of experts in 18 countries. Statistical techniques are used to evaluate and prioritize the DSFs, leading to the development of a conceptual framework.

Statistical analysis revealed 33 significant DSFs. The top five most significant factors that could influence the decision to implement IC in a project include a stringent requirement for project quality control, suitability of the design for IC, organizational readiness and competencies in ICPs, client receptivity to IC and the need to minimize field construction time. A framework of project requirements, location and site attributes, labour considerations and organizational factors was proposed as decision support.

The proposed framework may help to inform decision-making regarding the implementation of IC in a project. It has wider applicability because it includes technical, managerial and operational aspects of and the required competencies for IC, which are shared between project types and territories. The prioritized DSFs could be used as a guide when implementing IC, especially in countries where bespoke decision support systems cannot be developed.

The paper delineated the most important DSFs that are shared between IC project types and territories and can be used to investigate the compatibility of using IC in a proposed project. This research constitutes the first exclusive attempt at delineating, quantifying and ranking the sets of decision-making factors, drawing on international data set and contributes to the empirical checklist of DSFs for ICPs.

Enterprise integration is considered to be of great strategic significance in the support of organisations to achieve a competitive advantage. Traditional approaches to integration such as electronic data interchange (EDI) have provided a wide range of benefits but have not managed to fully automate and integrate business processes and applications. In addressing many of the limitations of EDI to piece together disparate systems, enterprise resource planning (ERP) solutions introduced an alternative approach to integration. Although ERP systems overcome significant integration problems, they have failed to adequately support intra and inter‐organisational integration. There has been a great demand by organisations to overcome integration problems and become more competitive. In this respect, enterprise application integration (EAI) has emerged to address intra and inter‐organisational integration in a more flexible and maintainable way. The normative literature remains limited regarding this emerging area and there is consequently a need for further research and contribution in identifying influential factors for EAI adoption. In addressing this relative void in the literature, this paper proposes a model for the justification and evaluation of EAI adoptions. The proposed model can be used by organisations as a tool for decision making when considering the adoption of EAI.

The purpose of this paper is to explore three key aspects of managerial decision making, namely managerial skills and attitudes, information and performance measurement supporting decision making, and companies' approaches to the management of relationship with their stakeholders.

After giving a broad view of the management practice of the sample, the paper analyzes the differences (by company size, dominant ownership, and performance) of companies according to the routines and attitudes of decision making.

The findings of the research paper suggest that managerial capabilities and skills, and attitudes toward decision making, the information and performance measurement supporting decision making, and companies' approaches to the management of relationship with their stakeholders have a significant impact on the effectiveness of managerial decision making. All these factors play an important role in the competitiveness of the Hungarian companies.

This research was based on a questionnaire. Further investigations would be necessary to check the results by interviews and case studies.

Beyond summarizing the main experiences, the paper draws up some recommendations for the business community reflecting on the successful companies' practice.

The three factors presented by the paper can constitute a possible framework of managerial decision making in further researches.

The purpose of this paper is to select the main impact factors of environment change automatically and identify and analyze the potential environmental impact factors with time delay by computer simulation, analyzing the impact rate of environmental impact factors. Then, the environmental impact factors analysis decision support system based on self‐organizing data mining model is designed.

Applying data mining methods in the analysis and decision making of regional environmental impact factors will have broad perspective. Self‐organization data mining is a new modeling method of complex systems modeling with strong modeling capability. It was first presented by A.G. Ivakhnenko, based on the principle of self‐organization of biological cybernetics and Kolmogoorov‐Gabor polynomial function. In this paper, the impact factors of regional environment quality evolution based on self‐organization data mining method is studied, selecting the main impact factors of environment change automatically by computer simulation, analyzing the impact contribution rate of environmental impact factors.

The environmental impact factors analysis decision support system based on self‐organizing data mining model is designed.

Accessibility and availability of data are the main limitations affecting which model will be applied.

The paper has important theoretical and practical significance for the sustainable development of regional environment, resource, economy system and the constitution of environmental protection and management measures.

This paper not only exploits new application domains of self‐organization data mining, but also explores new ways for regional environment impact factors analysis.

The purpose of this paper is to examine empirically the link between the determinants of perceived strategic value (PSV) of e‐commerce and e‐commerce adoption among Ghanaian small and medium size enterprises (SMEs), defined as businesses that employ a maximum of 200 employees.

The authors randomly sampled SME owners/managers from the membership of the Ghana Club 100 (GC 100) and non‐traditional exporters (NTEs). Established databases are not the norm in Ghana, and GC 100 and NTEs had membership databases that were accessible to the local co‐author. Investigating e‐commerce adoption issues among these companies was warranted. The authors used a structured instrument developed and validated in prior studies to collect the data in a face to face interview. A pilot study was conducted to ascertain the clarity and reliability of the questionnaire. Of the SME owners/managers: 200 were contacted; 112 agreed to participate in the study; 107 responses were obtained – representing a 53.5 per cent response rate. Statistical analysis was performed using SPSS 14.

Factor analysis demonstrated convergent and discriminant validity and construct reliability. PSV construct resulted in four factors: Strategic Decision Support (SD), Information Management (IM), Organizational Support (OS) and Decision Aids (DA). This finding is both consistent and inconsistent with prior research. The adoption construct yielded five factors: Perceived Usefulness (PU), Ease of Use (EU), Compatibility (C), Organisational Readiness (OS) and External Pressure (EP). There was more congruence between our results and those of prior research.

Limitations stem from small sample size, the population and locale from which the sample was drawn.

The study has research and practical implications, and these are discussed fully in the paper.

The paper contributes to the knowledge of perceived strategic value and adoption of e‐commerce by SMEs in an under‐researched part of the world.

The purpose of this paper is to refine and expand technology adoption theory for a healthcare logistics setting by combining the technology–organization–environment framework with a business process management (BPM) perspective. The paper identifies and ranks factors impacting the decision to implement instances of technologies in healthcare logistics processes.

A multiple case study is carried out at five Danish hospitals to investigate the bed logistics process. A combined technology adoption and BPM lens is applied to gain an understanding of the reasoning behind technology adoption.

A set of 17 factors impacting the adoption of technologies within healthcare logistics was identified. The impact factors perceived as most important to the adoption of technologies in healthcare logistics processes relate to quality, employee work conditions and employee engagement.

This paper seeks to understand how managers can use knowledge about impact factors to improve processes through technology adoption. The findings of this study provide insights about the factors impacting the adoption of technologies in healthcare logistics processes. Differences in perceived importance of factors enable ranking of impact factors, and prioritization of changes to be implemented. The study is limited to five hospitals, but is expected to be representative of public hospitals in developed countries and applicable to similar processes.

The study contributes to the empirical research within the field of BPM and technology adoption in healthcare. Furthermore, the findings of this study enable managers to make an informed decision about technology adoption within a healthcare logistics setting.

This study aims to explore the critical factors for digital technologies (DT) adoption to develop a sustainable agri-food supply chain (AFSC). As the developing countries are struggling to survive during COVID-19, DT adoption in AFSC can bring resilience and minimizes the food security concerns.

The study has used Fuzzy Delphi and fuzzy decision-making trial and evaluation laboratory (DEMATEL) methods for identifying the critical success factors (CSFs) for DT adoption and inter-relationship among them to explore the crucial factors for food security across AFSC.

The research reveals that “Digital Technologies, Logistics and infrastructure” is the most crucial CSF for managing food security in developing economy during the COVID-19 situation. This factor supports the decision-makers to manage data for demand and supply management and helps to survive and sustain in the disruptive environment. The findings of the study will help farmers and supply chain partners to manage the smooth flow of food items from source to end-users during a disruptive environment. The sourcing, manufacturing and delivery methods are needed to be changed with DT inclusion and may support to redesign their internal systems for improvisation. This shorter AFSC will enhance the resilience in AFSCs.

The emergency situation raised by the COVID-19 pandemic has brought global food security concerns. Adoption of DT across AFSCs can strategically reduce food waste and optimize the demand and supply balance.

The study aims to build a comprehensive framework by identifying the CSFs to develop resilient and sustainable AFSC amidst COVID-19.

The purpose of this paper is to statistically assess the effects of the design factors including usage of data-driven decision support tool (DST), classification of patients (triage), prioritization based on vital scores of patients, number of ambulances and hospital selection rules, on the casualty transportation system’s performance in large-scale disasters. Besides, a data-driven DST for casualty transportation is proposed to enhance the casualty survival and ambulance transportation times during the disaster response stage.

In this study, the authors applied simulation and statistical analysis to evaluate the effects of usage of data-driven DST, classification of patients (triage), prioritization of the patients based on vital scores, number of ambulances and hospital selection rules, on the patient survival and transportation time of the casualty transportation system. An experimental design was made, and 16 scenarios were formulated. Simulation models were developed for all scenarios. The number of unrecoverable casualties and time-spent by the casualties until arriving at the hospital was observed. Then, a statistical analysis was applied to the simulation results, and significant factors were determined.

Utilization of the proposed DST was found to improve the casualty transportation and coordination performance. All main effects of the design factors were found statistically significant for the number of unrecoverable casualties. Besides, for the Time spent Until Arrival of T1-Type Casualty at the Hospital, all of the main factors are significant except the number of ambulances. Respiratory rate, pulse rate, motor response score priority and hospital selection rule based on available hospital capacities must be considered to reduce the number of unrecoverable casualties and time spent until arrival of the casualties at the hospitals.

In this study, the factors that significantly affect the performance of the casualty transportation system were revealed, by simulation and statistical analysis, based on an expected earthquake case, in a metropolitan city. Besides, it was shown that using a data-driven DST that tracks victims and intends to support disaster coordination centers and medical staff performing casualty transportation significantly improves survival rate of the victims and time to deliver the casualties. This research considers the whole systems’ components, contributes to developing the response stage operations by filling gaps between using the data-driven DST and casualty transportation processes.