Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research

An Intelligent Customer Relationship Management (I-CRM) Framework and its Analytical Approaches to the Logistics Industry

This thesis develops a new Intelligent Customer Relationship Management (i-CRM) framework, incorporating an i-CRM analytical methodology including text-mining, type mapping, liner, non-liner and neuron-fuzzy approaches to handle customer complaints, identify key customers in the context of business values, define problem significance and issues impact factors, coupled with i-CRM recommendations to help organizations to achieve customer satisfaction through transformation of the customer complaints to organizational opportunities and business development strategies

The importance of BIM capability assessment: An evaluation of post-selection performance of organisations on construction projects

Purpose: The emergence of building information modelling (BIM) has led to the need for pre-qualification and selection of organisations capable of working within a BIM environment. Several criteria have been proposed for the assessment of an organisation’s BIM capability during the pre-qualification and selection phase of projects. However, no studies have sought to empirically establish whether organisations selected on the basis of such criteria have actually been the most successful at delivering BIM on projects. The purpose of this paper is to address the aforementioned gap through a comparison of predicted BIM capability and post-selection performance. Design/methodology/approach: BIM capability of firms in a case study was predicted using 28 BIM pre-qualification and selection criteria, prioritised based on their perceived contribution to BIM delivery success from a survey of practitioners on BIM-enabled projects. The comparison of predicted BIM capability and post-selection performance was, on the other hand, achieved through the application of the Technique to Order Preference by Similarity to Ideal Solution and fuzzy sets theory (Fuzzy-TOPSIS). Findings: Findings underscore the reliability of the 28 BIM pre-qualification and selection criteria as well as the priority weightings proposed for their use in predicting BIM capability and likelihood of performance. The findings have highlighted the importance of criteria related as previous BIM use experience as well as information processing maturity as critical indicators of the capability of organisations, particularly design firms. Originality/value: Overall, the findings highlight the need for prioritisation of BIM pre-qualification and selection criteria on the basis of their actual contribution to delivery success from post-selection evaluation of performance

A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets

The Success Factors in Measuring the Millennial Generation’s Energy-Saving Behavior Toward the Smart Campus

The millennial generation has a pivotal role in leading the industrial digital revolution. Energy-saving behavior and millennials’ awareness of energy consumption for educational context become crucial in performing a smart campus. This study tries to identify the success factors in measuring the millennial generation’s energy-saving Behavior toward the smart campus. The measurement model considers two significant constructs, including energy-saving attitudes with energy-saving education (organizational saving climate); energy-saving education and environment knowledge (personal saving climate); and energy-saving information publicity as sub-indicators, and construct energy-saving Behavior viz sub-indicators Behavior regarding energy and behavior control. In order to determine the preference level of each indicator and sub-indicator, the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach was executed by disseminating the questionnaire to 100 respondents from energy practitioners, students, and academicians in Indonesia. The calculation reveals that the energy-saving behavior construct has a higher priority value (0.94) than the energy-saving attitude (0.06). Meanwhile, energy-saving education and environment knowledge (personal saving climate) have been analyzed at the cutting-edge sub-indicator, followed by energy-saving information publicity and education (organizational saving climate). In addition, the sub-indicator for behaviors regarding energy becomes more demanding compared to behavioral control. As a novelty, the priority analysis of this Model aids the management of the campus and government in developing smart campus policies and governance. This Model can be used as a guideline for the management level to execute the smart campus practices. Thus, the effectiveness and optimization of smart campus transformation can be cultivated and accelerated. Besides, the potential coming of risks can be avoidable

Understanding Agile Innovation Management Adoption for SMEs

Research interest in agile innovation management (AIM) has increased due to its growing popularity. However, little is known about adaptability by small and medium enterprises (SMEs). This study examines the factors affecting intention to adopt AIM in SMEs. A conceptual framework is developed by combining internal and external environmental factors that influence adoption intention. Responses were received from 276 SMEs and analyzed using covariance-based structural equation modeling (SEM). The results confirmed that external environmental factors of mimetic isomorphism and normative isomorphism have a positive association with the intention to adopt AIM. Moreover, the internal environmental factors of top management championship, adhocracy culture, clan culture, and organizational readiness were confirmed to be positively associated with AIM adoption. This study provides one of the first empirical evidence of AIM for SMEs. In doing so, the study contributes both theoretically and practically toward understanding strategies that would enhance adoption by SMEs

A view of MCDM application in education

The effectiveness of the teaching and learning process by educators plays a significant role for countries to prepare students' potential in the forthcoming new industrial revolution (IR). However, the current COVID-19 pandemic and dynamic changes in the curriculum have created a significant shift of emphasis to educators. Hence, the teaching and learning process problems nowadays, including selecting appropriate effectiveness learning, have become a tough decision for educators. It can be solved using multi-criteria decision-making (MCDM) methods. The MCDM technique is widely applied and accepted in various fields but less in the teaching and learning context. This paper reviews and analyses the type of decision problems that were paid most attention to MCDM approaches, the adopted fuzzy set theory as well as inadequacies of those approaches. The purpose is to analyse and identify the literature review related to the applications of MCDM in education so new attributes and appropriate MCDM models for decision making can be suggested. The process involved comparing and analysing the MCDM application and fuzzy set theory in education by reviewing related articles in international scientific journals and well-known international conferences. Some improvements and more future works are recommended based on the inadequacies. The reviewed result may create an interest to the Ministry of Education (MoE) as it proposes teaching and learning process improvement, which will help to achieve greater satisfaction among educators and students

Development of a decision support framework to aid selection of construction supply chain organisations for BIM-enabled projects

With the emergence of Building Information Modelling (BIM), a critical criterion for the qualification of a suitable Construction Supply Chain (CSC) for projects is the ability of individual organisations to deliver through the use of BIM. Despite emerging research on BIM capability assessment, there are very few studies which look specifically at the qualification (pre-qualification and selection) of CSC organisations for projects. Furthermore, there is a general dearth of knowledge about the links between often pre-emptive qualification criteria and actual delivery success, particularly, in the BIM or CSC context. This research identifies the most relevant BIM qualification criteria for CSC organisations, as well as investigates their relative importance and influence on various aspects of BIM delivery success. A sequential exploratory mixed method research strategy was adopted in a three-phase design. The first phase explored BIM expert views on appropriate BIM qualification criteria in the UK, through interviews with BIM specialists (n=8). The next phase consisted of two rounds of a Delphi study with experienced construction practitioners (n=30 and n=25) to ascertain the most critical among the BIM qualification criteria derived from the first phase. This was achieved through statistical determination of Delphi participant consensus with the inter-rater agreement (rwg) test. The final phase involved a survey of practitioners on BIM-enabled projects in the UK (n=64) in order to empirically establish the relationship between the critical BIM qualification criteria and various dimensions of BIM delivery success in practice. This was achieved through survey respondents’ independent appraisal of CSC organisations on recent projects in relation to quality of BIM deliverables, delivery of BIM within schedule and on budget, plus collaboration, coordination and integration of project CSC through BIM. Various multivariate statistical analysis techniques including correlation analysis, mean weighted contribution analysis, multiple regressions modelling and analysis of variance (ANOVA) were engaged to identify qualification criteria influence on success. A decision support framework (DSF) was developed and proposed, based on the coefficients and weightings computed from the inferential statistical analysis of survey data. The research findings and DSF were validated through convergence analysis, as well as elicitation of expert respondent feedback to ensure adequacy, suitability and relevance in practice.The findings highlight the multi-dimensional nature of the relationship between BIM capability and various elements of delivery success. It is surmised that individual BIM capability attributes influence various aspects of BIM delivery success to different extents and this must be taken into consideration when selecting CSC candidates. BIM ‘staff experience’ and the ‘suitability of proposed methodology’ prior to BIM project commencement were identified as the most influential criteria on BIM modelling success (quality of BIM models, delivery of BIM within schedule and on budget). Individual competencies were found to be most influential on modelling quality and delivery of BIM within budget while execution planning adequacy influenced ability to deliver BIM on time. On the other hand, the ‘administrative and strategic’ level capacities were found as the most influential in relation to leveraging BIM to achieve project CSC objectives namely, collaboration, coordination or integration on projects. From a consolidation of the findings, a DSF is proposed for prioritisation of CSC organisations based on their propensity to succeed in the delivery of BIM. The work also provides an enhanced guidance on the relationship between various dimensions of BIM capability and delivery success, as well as how this knowledge enhances the prediction of CSC candidate propensity to succeed at the pre-qualification and selection phase of construction projects

Driving Sustainability through Engineering Management and Systems Engineering

Despite the ongoing impact of the COVID-19 pandemic, the challenge of realizing sustainability across the triple bottom line of social, environmental, and economic development remains an urgent priority. If anything, it is now imperative that we work towards achieving the United Nations Sustainable Development Goals (SDGs). However, the global challenges are significant. Many of the societal challenges represent complex problems that require multifaceted solutions drawing on multidisciplinary approaches. Engineering management involves the management of people and projects related to technological or engineering systems—this includes project management, engineering economy, and technology management, as well as the management and leadership of teams. Systems engineering involves the design, integration, and management of complex systems over the full life cycle—this includes requirements capture, integrated system design, as well as modelling and simulation. In addition to the theoretical underpinnings of both disciplines, they also provide a range of tools and techniques that can be used to address technological and organisational complexity. The disciplines of engineering management and systems engineering are therefore ideally suited to help tackle both the challenges and opportunities associated with realising a sustainable future for all. This book provides new insights on how engineering management and systems engineering can be utilised as part of the journey towards sustainability. The book includes discussion of a broad range of different approaches to investigate sustainability through utilising quantitative, qualitative and conceptual methodologies. The book will be of interest to researchers and students focused on the field of sustainability as well as practitioners concerned with devising strategies for sustainable development