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The purpose of this paper is to examine the usefulness of fusion as a means of improving the precision of automated opinion detection.

Five system fusion methods are proposed and tested using runs submitted by the Text REtrieval Conference (TREC) Blog06 participants as input. The methods include a voting method, an inverse rank method (IRM), a linear‐normalised score method and two weighted methods that use a weighted IRM score to rank the document.

Mean average precision (MAP) is used as an indicator of the performance of the runs in this study. The best system fusion method achieves a 55.5 percent higher MAP result compared with the highest MAP result of any individual run submitted by the Blog06 participants. This equates to an increase in detection of 2,398 relevant opinion documents (21 percent).

System fusion can be used to improve upon the results achieved by existing individual opinion detection systems. On the other hand, multiple opinion detection approaches can be combined into one system and fusion used to combine the results to build in diversity. Diversity within fusion inputs can increase the improvements achieved by fusion methods. The improved output from a diverse opinion detection system will then contain a higher number of relevant documents and reduce the incidence of high‐ranking non‐relevant documents and low‐ranking relevant documents.

The fusion methods proposed in this study demonstrate that simple fusion of opinion detection systems can improve performance.

This work affords a practical checklist that specifies the civil engineering trades-related hazards and offers a safety indicator to identify the safety level of a construction project concerning the hazards of the civil engineering trades.

The methodology depends on reviewing the archival works, visiting the construction sites, utilizing the direct observation and preliminary hazard analysis methods and conducting semistructured interviews to pinpoint and validate the checklist of the civil engineering trades-related hazards. Additionally, a questionnaire-based survey with the rank sum weight technique has been employed to assemble and analyze the data required to build the safety indicator.

Relying upon the used methodology, 70 hazards under the trades of general environment, earth, demolition, excavation, concrete, dewatering, waterproofing insulation and scaffolding have been pinpointed and validated. This is in addition to the safety level indicator of the civil engineering trades-related hazards (SLICETH), which indicates high viability during its validation in five national and international projects.

The value of this work lies in its ability to tackle the gap existing in the safety management knowledge regarding the notion of the hazards of the civil engineering trades and their influences on the safety performance of the construction projects. As a result, it offers a complete knowledge to the academics and the practitioners for confronting the negative impacts of the civil engineering trades-related hazards. Consequently, it helps in enhancing the safety performance level in the sites of the construction projects.

This study aims to develop a risk prioritization process using failure mode and effect analysis (FMEA) in association with composition of probabilistic preferences (CPP) and weighting the risk analysis criteria. It seeks to develop decision-making considering the fast response necessary to achieve project objectives in complex scenarios, such as the pandemic of COrona VIrus Disease 19 (COVID-19).

After identifying the risks, the prioritization process was applied to a project in the oil and gas area, in which a focus group assessed these risks. This evaluation took place employing traditional FMEA, FMEA with CPP by axes considering four points of view and FMEA with CPP by weighted sum with the use of a multicriteria method to weight the criteria. These approaches were compared to understand their differences and benefits, with a flow chart being developed, consolidating the procedure.

The methodologies that showed the greatest benefits were FMEA with CPP by axes PO (progressive-optimistic) and by weighted sum. Essentially, this was mainly related to the interrelationship between risks and to the importance of prioritization.

This procedure can consider company's views on what is critical and the interrelationship between risks. It provides a clear segmentation of what should and should not be prioritized. It was also developed in a practical case, showing a possible alternative to support fast responses in decision-making.

Many of the financial instruments used in classic investment analysis do not apply to typical information technology investment decision making because of the multi‐criteria and multi‐objective nature of the problem. This is particularly true when integrating strategic, tactical, and operations planning objectives in the decision. One approach for making IT investment decisions is a multi‐objective goal programming (GP) model proposed by Talluri in 2000. The purpose of our paper is twofold: to demonstrate how a simple ranking/scoring method can be used in place of the more involved Talluri GP modeling approach if only a solution is required; and, in decision situations where solution justification is desired, to explain how GP extension methodologies can be incorporated into the analysis to generate information to determine a solution’s reliability and identify economic tradeoffs that can be used to improve an existing solution.

Although the factors that affect the decision of outsourcing are variant from one industry to another and from one application to another, this paper aims to investigate the factors that influence the decision of outsourcing using a systematic literature review (SLR) approach to fill the gap of the literature and focus on maintenance activities in terms of industrial firms. Furthermore, the analytical hierarchy process (AHP) method will be developed to prioritize the important factors according to the experts' judgments and reveal the factors' weights to support decision-makers in thermal power plants to realize the factors that have a high impact on outsourcing maintenance decisions and enhance their decision-making to increase power plant competitive advantages over quality, availability and safety requirements.

The purpose of this research is to prioritize the factors that influence outsourcing maintenance decisions to be considered by decision-makers in power plants before making a decision of outsourcing maintenance activities. In this research, an SLR approach was applied to identify the factors in terms of industry and rank them accordingly using factors' frequency process. A questionnaire survey was designed to seek the experts' opinions to prioritize the factors that influence the decision of outsourcing maintenance activities in power companies using AHP. The research found that “Improve service quality”, “acquire new skills or technical knowledge” and “Function difficult to manage” have the first priority before making the decision of outsourcing maintenance activities in the context of power plants.

The decision of outsourcing regarding maintenance activities in the context of industry, as well as determining the most critical factors among the extracted factors from the literature and rank them accordingly using factors' frequency process. Next, a questionnaire survey was designed and conducted to seek the experts' opinions to prioritize the factors that influence the decision of outsourcing concerning maintenance activities in thermal power companies using AHP method. According to the SLR, industrial companies are mainly pursuing outsourcing maintenance services to focus on core activities, save overall costs and improve service quality.

This is an original research paper out of an MSc thesis submitted at the Industrial Engineering Department, University of Jordan.

Even with the recognized impact organizational leaders have on the outcome of digital transformation (DT), a comprehensive scholarly understanding of the competencies that leaders must possess to lead a DT to success is lacking.

To derive and list the competencies considered by experts as necessary for managing DT, the authors recruited 18 international senior managers with relevant experience and applied the Delphi method to survey the managers. Upon the completion of three survey rounds and the authors modifying the response list until consensus was reached, 39 items were shortlisted as constituting key competencies for managing DT. Furthermore, the authors engaged in inductive theorizing to derive propositional statements using these findings.

The practitioners agreed on visionary thinking, agility, understanding the value of data, data-driven decision-making, knowledge of strategy and accepting change as the most important requirements for managing DT. Through inductive theorizing, the authors further derived that the seven discovered clusters fell into two broader competencies – behavioral and strategic – and that each behavioral competency would have varying importance depending on the country and industry that the organization operates in.

As is typical for Delphi studies that involve multiple survey rounds, the study participant response rate was moderate. The implications of this study, in finding that a variety of leadership competencies are needed to ensure successful DT, validate prior research that people, not technology, drive DT.

This study helps mitigate assumptions that successful DT processes are only possible by hiring technological experts, as doing so highlights the importance of behavioral leadership competencies.

The study is one of the first to interlink digital leadership with DT by inductively theorizing behavioral and strategic competencies. The authors also establish that contexts are vital in determining which aspects of leadership competencies are deemed most important in driving DT.

In the Indian manufacturing SMEs context, supply chains have a complex structure having multiple echelons, multiple partners and multiple locations. Due to these complexities, most of the Indian manufacturing SMEs face several types of supply chain risks. This paper aims to identify the dominant risk variables and to develop the interrelationship among these risk variables.

Based on the literature review and experts’ opinion, nine dominant risk variables faced by an Indian manufacturing SMEs have been identified. An interpretive structural modelling (ISM) approach has been adopted to establish the interrelationship among the risk variables. These risk variables have been classified by using MICMAC analysis. Based on ISM-MICMAC approach, a case study on three Indian manufacturing SMEs has been carried out.

This study would help the supply chain managers to understand and prioritize the significant risk variables. Nine significant risks variables of Indian manufacturing small and medium enterprises (SMEs) have been studied. External risk, information technology risk and financial risk have identified as most influencing risk variables, while delay risk and market risk have emerged as the most dependent risk variables. These results will provide a guideline to supply chain managers for implementation of supply chain risk management (SCRM).

In this study, an ISM-based model is developed based on the opinion of experts from a group of Indian manufacturing SMEs; as such, this model may be biased and limited to a selected company. This framework can be extended further by adding more risk variables and sub-risk variables from the other sectors/organizations.

Many SCRM models are available in past literature, but no model has been proposed for the Indian Manufacturing SMEs. This research finding can be useful for managers to understand the characteristics and interrelationships among the risk variables for building a robust supply chain. These results will also help the supply chain managers in making proactive plans for SCRM, especially in the Indian SMEs context.

The role of clusters in the development and growth of local and national economies has been extensively studied and discussed in global literature. Different methodologies are used for analysing the impact these have in national and regional economies, such as the input–output (IO) and gravity models. This paper aims to detail the methodologies present in the literature and propose a new robust theoretical framework, which facilitates the evaluation and comparison among maritime clusters in terms of attractiveness assisting stakeholders to devise strategies, which will attract companies.

An index is created composed of five key categories, namely, infrastructure, financing, governance, manpower and institution/legislation. For the analysis of the index, multi-attribute utility theory (MAUT) is used as a tool to evaluate the importance and performance of the different attributes using both quantitative and qualitative criteria. The methodology has been tested via the use the Piraeus maritime cluster.

The framework has been tested on its robustness and friendliness to the user providing useful insights to the stakeholders. Among the results has been the importance of the finance, manpower and infrastructure attributes, which appear to promote the cluster’s attractiveness. In addition, legislation and institutional partnerships, along with Government support, need to take place improve the performance of the cluster.

A key limitation is the fact that the methodology has been tested in a single case. Applying the methodological framework in a wider sample of clusters will significantly improve the present work.

The proposed model takes further existing research in the field via adopting the philosophy of the World Bank’s Logistics Performance Index. Among the benefits of the proposed index is that it offers the flexibility and robustness to compare among different maritime clusters globally and can be readily used as a benchmarking policy tool at national, regional and global levels at any given point in time and attribute dimension.

Agility is the ability of an organization to adjust its supply chain tactics and operations to respond quickly against altering business environments such as fluctuating demand pattern, supply chain disruption and global competition. An agile organization must possess a promising capability of swiftly responding to dynamic conditions while being cost-effective without compromising the efficiency. Such high-performance adaptability necessitates the role of supply chain managers to maximize the agility of the supply chain through the efficient use of input resources. Therefore, the purpose of this study to reveal a new decision support tool that would allow the key decision-makers to maximize the agility of the supply chain while deploying the input resources more effectively.

In present study, an integrated approach of popular analytic hierarchy process (AHP) and goal programming (GP) has been adopted as a potential solution methodology. AHP has been implemented to allocate the local and global weights to decision variables, whereas GP incorporates the AHP weights into the desired model.

It was found that the proposed decision support tool restricts the value of the decision variables for maximizing the agility and optimizing the usage of input resources. The results obtained from the model validate the objective of achieving targeted agility level within the available resource limitations.

The decision support tool developed in the proposed study offers a systematic and effectively simple approach to supply chain managers with a goal of identifying the degree of focus under each decision variable in the respective manufacturing organizations.

A novel decision support tool has been developed known as an agility control system), which helps the decision-maker to achieve the required agility in the supply chain by controlling the decision variables.

The purpose of this paper is to introduce the integrated model of the Preference Selection Index (PSI) and the prospect theory as new means to appraise the impact of supply chain sustainability risks based on five pillars of sustainability. Research has shown that sustainability risk assessment has a strong positive impact on improving the performance of enterprises.

This study adopts a new decision support model for assessing supply chain sustainability risk based on additional failure mode and effect analysis parameters and its integration with PSI methodology and prospect theory. A case example of the supply chain small and medium enterprise (SME) producing fashion have been used in this study.

The result of this study reveals some critical supply chain sustainability risks affecting the sustainability of enterprises under study.

The use of a limited sample is often associated as a limitation in the research studies and this study is based on findings from SMEs in the fashion retail supply chain. This preliminary study provides academics and practitioners an exemplar of supply chain sustainability risk assessment using integration of the PSI method and prospect theory.

The result of this study is beneficial for practitioners, particularly owner–managers of SMEs who can use this study as guidance on how to consider risk behavior to identify and select the critical sustainability risks and plan mitigating strategies accordingly.

Scientific studies on using the PSI and its integration with prospect theory as means to assess the criticality of supply chain sustainability risks is very rare. To the best of the authors’ knowledge, this is the first paper that presents the integrated model of the PSI and prospect theory to rank supply chain sustainability risks based on five pillars of sustainability.