Advancing Smart Building Upgrades Through an Innovative Decision Support Framework for Smart Readiness Indicator Implementation: A Greek Case Study

Advances in the field of technology enabled individuals and businesses to collect large amounts of data (structured and unstructured) from various sources like never before. Data from social media, user-generated, internet, health care, manufacturing, supply chain, financial institution, and sensors have grown exponentially. This paper’s objective is to review how big data drive and impact innovation, competitive advantage, productivity, and decision support. Methodology: A comprehensive literature review on big data and identifying the impact of big data analytics on innovation, competitive advantage, productivity, and decision support are studied. The reviewed literature created the foundation for studying, a model that was developed based on an extensive review of literature as well as case studies and future forecast by market leaders. Big data is the latest buzzword among businesses. A new model is suggested identifying big data and the correlation between innovation, competitive advantage, productivity, and decision support. Findings: A review of scholarly literature and existing case studies finds that there is a gap between existing frameworks and the integration of big data into various business and management functions and objectives. The findings are interesting that literature is rich with concepts and frameworks for achieving the end goal for business or management function along with framework but very little is available in the literature on the question of how to integrate big data analytics into those frameworks. This paper finds that a key question is missing i.e., what are the essential steps that businesses should perform to implement and integrate big data analytics into existing frameworks to fully exploit the big data potential. Research Limitations/Implications: The research was limited to a review of selective literature focused on in-depth understanding of big data. Additionally, it focuses on how big data leads to innovation, competitive advantage, productivity, and decision support. Although there are many other related fields of studies where big data impact can be studied but is not part of this study effort. Future studies can lead to more in-depth studies of other related areas of studies. Practical Implications: The review of the literature suggested that “Big Data” is playing an important role in innovations, creating competitive advantage, enhancing productivity, and assisting in data-driven decisions. Businesses are taking advantage of the customer insights that are innovating products and services which are very customer-centric, keeping the competition on the run, improving productivity at all levels, and making educated decisions every day. The future will be driven by smarter big data solutions and insights. Originality/Value: The study provides evidence that big data is the catalyst for innovation, creates competitive advantage, enhances productivity, and assists in decision making. The methodology is to review scholarly literature and case studies. It supports the need for developing new models, implementation frameworks for better insights, and patterns. The big data implementation methodologies, framework, and governance have been ignored in empirical research.

This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade and logistics sectors. In this study, the efficiency of logistics operations in 118 countries was evaluated using an integrated multi-criteria decision-making (MCDM) model objectively weighted by the Entropy method. Countries were ranked using the MCRAT, SAW, TOPSIS, and FUCA methods. According to the findings, large datasets provide more robust insights for sensitivity analyses, and wider weighting coefficient combinations make the data more meaningful. In addition, it is suggested to use low-compensation methods instead of classical additive methods for LPI. Unlike other studies in literature, this research applied an innovative sensitivity analysis to test the robustness of the model and comprehensively examined the effects of weighting techniques based on over 2500 different MCDM results. The findings suggest that the FUCA method should be recommended to decision-makers for calculating LPI rankings due to its simplicity, practicality, low compensatory power, and low sensitivity. This study offers methodological improvements when evaluating logistics performance and provides significant contributions to decision-making processes. The findings are expected to provide a valuable resource for policymakers and businesses in understanding a country’s position in global competition, as well as serving as a reference for researchers evaluating the logistics performance of countries.

Protecting environmental quality requires a comprehensive approach that heavily relies on our ability to confidently predict or forecast impacts, evaluate the ability of natural systems to absorb impacts, and determine the combination of options that will provide an acceptable level of protection and/or restoration. These forecasts, when accepted, can help stakeholders to understand the potential cost or implications of restoration options and guide the development of effective protection strategies that can mitigate and prevent adverse impacts. Forecasts, which are typically based on combinations of modeling and analysis, must be designed to be sensitive to changes in the watershed and to in-stream responses. The difficulty with forecasts is that they are just predictions based on what we know now and what we believe might happen in the future. That is why environmental protection studies emphasize rigorous science-based approaches that can build confidence in forecasts. Building on an extensive body of relevant studies in the Los Angeles region, including monitoring, model development, and regional model parameterization efforts, this study is designed to develop and apply a Best Management Practice Decision Support System (BMPDSS) for the evaluation of alternative storm water best management practices (BMPs) and Low Impact Development (LID) methods. The system allows managers to evaluate the ability of various BMP scenarios to provide necessary flow volume and pollutant load reductions for Total Maximum Daily Load (TMDL) implementation and optimizes the scenarios based on benefits and costs. The approach is designed to provide guidance for a public incentive plan for implementing onsite BMP/LID techniques. The framework developed in this study provides a technically defensible and repeatable basis for evaluating quantitative measures associated with alternative BMP implementation plans. The BMPDSS represents an advanced method for simulating BMP processes and analyzing benefits (e.g., load reductions). Results of this study provide an interesting glimpse into the most relevant and economical BMPs for typical land uses in the Los Angeles watershed.

Incorporating sustainable development considerations into energy sector decision-making: Malmö Flintränen district heating facility case study

Towards safer general aviation operations using a vision-based decision support system for weather threat avoidance

Since the beginning of the 1990s, systems of innovation have been used as a framework to explain differences in innovativeness between firms, industries and economies at local, regional, national and supranational levels. The main argument behind the discussions around systems of innovation is that firms are increasingly dependent on institutions in their direct environment for their innovativeness and thus competitiveness. In regional innovation systems, firms and other organisations are systematically engaged in interactive learning through an institutional milieu characterised by embeddedness. On the basis of these systems' governance infrastructure, a typology can be developed consisting of grassroots systems (with the highest level of regional embeddedness), integrated systems and dirigiste systems (with the lowest level of regional embeddedness). Based on empirical research on innovation support agencies in Kyongbuk-Taegu and Kyonggi, this paper shows that South Korea is characterised by dirigiste regional innovation support systems with a relatively low level of regional embeddedness. This dirigiste kind of system generates both excessively homogeneous innovation support agencies, which are not sufficiently focused on specific regional economic demand, and horizontal policy coordination problems due to the strong vertical dependencies of agencies in the regions to their sponsors in the central government.

Intermediaries and innovation support in the design for sustainability field: cases from the Netherlands, Turkey and the United Kingdom

Purpose– The purpose of this study is to empirically investigate impact of transformational leadership on team performance and the effects of support for innovation and psychological empowerment in this association. Over the past many decades, teams have become an increasingly popular way of organizing and coordinating work. This trend creates new challenges for leaders, as they are expected to motivate individual employees and to improve team performance simultaneously.Design/methodology/approach– This study proposes a conceptual framework for understanding motivation and support extended to individual employees which would influence team performance. The conceptual framework includes psychological empowerment and support for innovation as mediating variables in the effect of transformational leadership on team performance. The study also validates the proposed conceptual framework using a second-order confirmatory factor analysis technique, namely, partial least squares-structural equations modeling, with the data collected from 182 followers from 10 service organizations in the UAE.Findings– The findings suggest that transformational leadership has significant positive impact on team performance and, on the contrary, transactional leadership has significant negative effect on team performance. Two dimensions of psychological empowerment such as meaning and self-determination have partial mediation effect in the relationship between transformational leadership and team performance.Originality/value– This study contributes to the research that identifies team mechanisms influenced by transformational leadership that can affect team success and performance, particularly in the context of UAE-based organizations. Managerial implications and future research areas are further discussed.

Systematic approaches to the selection of innovative technologies for environmental protection are investigated. The features and directions of ensuring environmental safety are considered by means of a substantiated method of selecting innovative technologies. A method is used that substantiates the procedure for determining the relative importance of criteria and indicators of environmental protection technologies and methods using the program-analytical method. The basic and fundamental principles of project activities are well developed by specialists, but the practical orientation, application of aspects and algorithms for the implementation of these innovative decision support systems at the level of project activities at the regional level need further development. Decision support systems in the design of innovative environmental protection technologies will increase the complexity and effectiveness of solutions to improve environmental safety. According to the developed approach, criteria formulated as influencing groups of factors. Relevant experts, relying on various types of information on the specific purpose and innovative ecological solutions, give their own judgments regarding the priority of the advantages of the specified criteria. Expert judgments are processed according to the formal procedure of the MAI, which is implemented on a computer, and is the basis for decision-making when choosing the innovative ecological solutions in specific conditions. The criteria hierarchical structure of innovative ecological solutions is represented by a sequence of actions that include the following stages: development of elements of a methodical approach; verification of the consistency of the proposed hierarchical structure for determining priority technologies based on the input data of several objects of settlements; obtaining data on the priority of innovative ecological solutions for implementation on these objects. The advantages of the proposed methodical approach should include the possibility to link to a single algorithm for the justification of the required decision data that differ both in their content and in the form of presentation. Implementation of complex support systems for making innovative decisions in nature protection activities will increase the complexity and effectiveness of decisions to ensure environmental safety. Keywords: environmental safety, system analysis, innovative solutions, environmental protection tools and technologies, sustainable development.

The construction industry is embracing advanced digital technologies, such as the Internet of Things and automation systems, to enhance energy management and occupant comfort in smart buildings. Recognizing the need to assess the readiness of buildings to support energy-efficient and adaptive functionalities, the European Commission introduced the smart readiness indicator (SRI) in 2018. While the SRI provides a standardized framework, its adoption, limitations, and potential to drive the evolution of smart buildings remain underexplored. This study addresses these gaps through a systematic literature review, incorporating bibliometric and qualitative analyses to evaluate the state of research on the SRI. The bibliometric analysis reveals that research on smart readiness is growing rapidly, with a strong focus on energy efficiency and smart buildings. This literature primarily evaluates and promotes the adoption of the SRI within buildings, aligning with the need to explore the paths for the evolution of smart buildings. The qualitative review summarizes six understudied research topic required to drive the evolution of smart buildings in the literature: The applicability of the SRI to different contexts, including various building types and climatic conditions; the subjectivity in the framework; the alignment with other certificates and standards; the SRI as a tool for smart retrofit; expansion to the neighborhood and district levels; and the score correlation with energy performance. The findings show that, although the SRI was originally introduced for buildings, it has much wider applicability, at the more detailed building component level as well as at the broader neighborhood and district levels. Future research could focus on the role of the SRI in evaluating smart readiness at the neighborhood scale and determining the minimum acceptable SRI score.

The IT Service Management (ITSM) industry has defined processes as best practices in the widely-accepted IT Infrastructure Library (ITIL®) framework. However, there are few tools available to provide decision support to IT Service Managers who wish to improve service management processes. It is essential to assess the current capability of processes before process improvements are planned. Our research addressed the problems of the lack of transparency and the need for efficiency in ITSM process assessment. Using the Design Science Research methodology, we developed an innovative Software-mediated Process Assessment (SMPA) approach that automates assessment of ITSM processes and supports the decision-making of IT Service Managers. The SMPA approach includes process selection, an online survey to collect assessment data, measurement of process capability and specific recommendations for managers to commence process improvement. We implemented a decision support system (DSS) to automate the SMPA approach and evaluated it at two IT service providers. The evaluations indicated that the SMPA approach supports decision-making on process improvements. In the future, data analytics performed on the assessment data can help IT managers to analyse and visualise the ‘big data’ of process knowledge.

Although the role and importance of innovation support in South Africa is a well-recognised concept on the national level, provincial governments have only recently started to debate, investigate and explore instruments through which innovation can be leveraged and supported regionally. Extant theory and practice is vague on providing support on how to design precise solutions. In this paper we consider the Western Cape Department of Economic Development and Tourism (WCDEDAT) in South Africa. The goal of the paper is to design and populate a policy-learning framework on the provincial government level. We explore the key functions of the provincial government towards supporting innovation and propose a policy-learning framework extensively exploring possible systems instrument goals and actual instruments/ mechanisms towards performing such functions. We argue that this policy-learning framework could be useful to a wider audience and that it can also serve as a guide for regions at a similar level of development.

This study aims to improve team performance at PT. Insan Cerdas Indonesia (ICI), Ranotana Branch, through a psychological intervention in the form of the "Teamwork Excellence" training program. The research began with an assessment that identified psychological dysfunctions within the team, such as ineffective communication, unclear role division, and low psychological safety. The methodology employed was a combination of qualitative and quantitative approaches, including observation, structured interviews with the HR Manager and Branch Head, as well as psychological assessments using the Training Needs Analysis (TNA) and Team Climate Inventory (TCI). The intervention was conducted with seven staff members through a one-day experiential training. The results showed significant improvements in vision clarity, participative safety, task orientation, and support for innovation. Participants' average posttest scores increased by 18.57% compared to the pretest. Qualitative findings also indicated enhanced initiative, openness in communication, and team cohesion after the intervention. The study concludes that well targeted psychological interventions can effectively enhance team dynamics. It is recommended that the organization continue sustainable development programs to maintain and further improve team performance.

Studies have shown that clinical work in the real world involves considerable elements of local improvisations, use of tacit knowledge and experiences as well as patient-dependent and workplace-specific traditions of actions that are not easily captured by formal models of clinical workflows. We argue in this paper that decision support tools for emerging pervasive e-health need to take a more holistic view of clinical problem solving by considering the wide range of contextual, socio-cultural, dynamically situated factors that influence real world clinical practices. We discuss a set of design implications emerging from a user-centred study of clinical practices aimed to highlight the subtle differences in the ways clinicians often contextualise procedures and improvise practices in order to accommodate for peculiar workplace circumstances and patient-centred needs. Based on the result of the study, we propose a framework for cross-boundary awareness and practice-centred decision support in pervasive e-health.

Guiding organizations through transformational changes such as restructuring or adopting new technologies is a daunting task. Such changes generate workforce uncertainty, fear, and resistance, reducing morale, focus and performance. Conventional project management techniques fail to mitigate these disruptive effects, because social and individual changes are non-mechanistic, organic phenomena. CALM (for Change, Adaptation, Learning Model) is an innovative decision support system for enabling change based on CAS principles. CALM provides a low risk method for validating and refining change strategies that combines scenario planning techniques with “what-if” behavioral simulation. In essence, CALM “test drives” change strategies before rolling them out, allowing organizations to practice and learn from virtual rather than actual mistakes. This paper describes the CALM modeling methodology, including our metrics for measuring organizational readiness to respond to change and other major CALM scenario elements: prospective change strategies; alternate futures; and key situational dynamics. We then describe CALM’s simulation engine for projecting scenario outcomes and its associated analytics. CALM’s simulator unifies diverse behavioral simulation paradigms including: adaptive agents; system dynamics; Monte Carlo; event- and process-based techniques. CALM’s embodiment of CAS dynamics helps organizations reduce risk and improve confidence and consistency in critical strategies for enabling transformations.