3,731 research outputs found

    International Academic Symposium of Social Science 2022

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    This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate

    A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

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    The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.acceptedVersio

    The Dark Arts. A Future For Practitioners of Architecture.

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    Many practitioners experience dissonance between the potential of their field and the realities of practice as defined by status-quo conventions. The forces that shape practice create inefficiencies, barriers to opportunity, and amplify contingency across the built environment. This work aims to establish a new mode of practice that can flow around the status-quo, with the extended goal of accessing a means to impact problems on a systemic plane. This dissertation follows a practice-based design science research methodology. Beginning with a critical dissection of the architectural profession, it progresses via a series of representational and reflective tools that illustrate an emergent framework for the ‘creative project’: the conception, design, and implementation of a novel strategic design practice, called ‘Future Workshop’ (FW). This is developed in parallel with (and in contrast to) an existing architectural practice (DWA). The strategic design approach synthesizes new professional methods from architecture and other disciplines, allowing client organizations to target higher-order problems upstream of typical design engagements, focusing the impact of future design efforts on the most important goals and priorities. The research traverses the tensions between the pragmatic and intellectual hemispheres of practice and establishes metrics for considering these abstract problems through a particular series of diagrams and representational tokens, or ‘glyphs’. The contribution of this work is multivalent, including a novel way of operating a design practice (FW), and new means of inquiry, proposing situated methodologies for research within professional practice

    The Entrenched Political Limitations of Australian Refugee Policy: A Case Study of the Australian Labor Party (2007-2013)

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    This thesis deconstructs Australia’s asylum and refugee policy trajectory under the Labor government between 2007 and 2013. For a short time after the 2007 election, in accordance with its promise to abolish the LNP’s Pacific Solution, Labor began to unwind certain policy structures of externalisation and deterrence that had been in place since the introduction of mandatory detention in 1992. By 2013 however, the ALP had declared that asylum seekers arriving by boat had no prospect of resettlement in Australia. This thesis analyses the political strategy of the ALP in rhetoric, policy choices and policy justifications to derive lessons from Labor’s mitigated challenge to the deterrence/externalisation paradigm. Critical Discourse Analysis is used to examine the political strategies of lead actors, particularly the ALP and the LNP, and to reconcile these strategies with policy outcomes such as irregular arrivals, detention figures, deaths at sea and compliance with obligations under international law. A central argument of this thesis is that Labor’s attempt to sustainably depart from the dominant externalisation paradigm was impaired, not by a lack of commitment to its stated program of reform, but rather by entrenched political limitations of the Australian context. These limitations include the LNP’s rigid partisanship and lack of policy compromise, the deep-rooted nature of mandatory detention, and the Australian public’s historical and continued support for controlled migration. A precise and detailed analysis of the impact of these limitations on Labor’s proposed reform fills a gap in academic knowledge about the political influences on policy action in Australian asylum and refugee policy. I contend that these limitations must be effectively engaged with in any attempt to reform the Australian asylum and refugee policy space

    The role of the institutional environment as a barrier or an enabler to entrepreneurial and innovation activity; the case of the South African green economy industry

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    This thesis examines the relationship between the institutional environment and entrepreneurial and innovation activity within SMEs operating in South Africa's green economy, with a focus on the energy, agriculture, water and sanitation, and waste and recycling sectors. The aim is to investigate how entrepreneurs navigate the institutional environment by utilising entrepreneurial orientation and managerial discretion to achieve entrepreneurial output. By examining the implications of South Africa's post-apartheid legacy on present-day entrepreneurship in these sectors, the study yields valuable insights from the entrepreneurs' perspectives. The methodology adopted in this study is phenomenological, which utilises qualitative research methods, cross-validated with some quantitative evidence in the form of statistical analysis and case studies. The study includes 55 participants, comprising 47 entrepreneurs and 8 stakeholders from government departments, government agencies, NGOs, and incubators. The study highlights the regulatory mechanisms in place to promote small business participation in South Africa's economy and transition to a more environmentally conscious one. However, the outcomes suggest that these measures may not be achieving their intended objectives, and the institutional environment and cultural views may pose significant obstacles to entrepreneurship and the adoption of greener practices. The research emphasises the importance of addressing these issues to promote sustainable economic growth in South Africa. The study recommends a more coordinated effort by all stakeholders to target pertinent socio-economic challenges specific to South Africa's context

    Examining the Antecedents and Consequences of Employee Engagement on Temporary Agency Workers in a Partner-led Environment

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    The recent Covid-19 pandemic highlighted the increasing reliance of organisations on temporary agency workers (TAWs) to survive in business environments that are characterised as being temporal, unpredictable, and cyclical. Temporary workforces are growing in popularity as it offers flexibility and independence for both employees and the employer (i.e., The Client). At the same time, the recruitment industry has witnessed significant growth and heightened competition to source reliable, high-quality TAWs as this niche cohort of the workforce underpin the successful performance and outcomes of both agency and client.Despite the increasing number of TAWs and their significant contributions to sustaining competitive advantage and economic growth, extant literature on employee engagement of TAWs to date is rather limited. Also, what research does exist is rather limited as seminal research focused on employee engagement of full time employees, rather than any rigorous examination of engagement with TAWs who operate in turbulent and constantly changing ‘real world’ business environments.This study addresses this gap in knowledge by “examining employee engagement from the perspective of the TAWs to identify the influence and implications of job and organisation engagement”. This study draws on an exemplar case study of a well-known large UK retailer (i.e., The Client) that operates a distribution warehouse and employs TAWs who are sourced through three recruitment agencies.A review of seminal literature provides the theoretical base for the antecedents and proposed outcomes of employee engagement to inform the proposed research model to capture the perceptions of TAW engagement at The Client organisation. A self-completion questionnaire was completed by 277 TAWS and the research model was tested using partial least squares structural equation modelling (PLS-SEM) and SmartPLS v.4.The findings challenge two long-held assumptions about employee engagements First, job engagement and organisation engagement are two significantly distinct constructs that have implications for The Client organisation. Second, experiences of employee engagement for TAWs differ from that of traditional employees as they are heavily reliant on The Client organisation’s ability to support, value and embed them into the workforce and wider mission of the organisation

    New Approach for Market Intelligence Using Artificial and Computational Intelligence

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    Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.SmĂ„ och medelstora detaljhandlare Ă€r centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillvĂ€xten, men de möter ofta enorma utmaningar i att uppnĂ„ sin fulla potential. Finansiella svĂ„righeter, brist pĂ„ marknadstilltrĂ€de och svĂ„righeter att utnyttja teknologi har ofta hindrat dem frĂ„n att nĂ„ optimal produktivitet. Marknadsintelligens (MI) bestĂ„r av kunskap som samlats in frĂ„n olika interna externa kĂ€llor av data och som syftar till att erbjuda en helhetssyn av marknadslĂ€get samt möjliggöra beslutsfattande i realtid. Ett relaterat och vĂ€xande fenomen, samt ett viktigt tema inom marknadsföring Ă€r artificiell intelligens (AI) som stĂ€ller nya krav pĂ„ marknadsförarnas fĂ€rdigheter. Enorma mĂ€ngder kunskap finns sparade i databaser av transaktioner samlade frĂ„n detaljhandlarnas försĂ€ljningsplatser. ÄndĂ„ Ă€r formatet pĂ„ dessa data ofta sĂ„dant att det inte Ă€r lĂ€tt att tillgĂ„ och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försĂ€ljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsĂ€ljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fĂ„ngar upp kunskap frĂ„n de heterogena köpbeteendena i data och hjĂ€lper till att utreda hur effektiva marknadsföringsplaner Ă€r. Apriori, som rĂ€knar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), Ă€r den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gĂ€llande lĂ„g berĂ€kningshastighet och svag intelligens. NĂ€r antalet parallella databassökningar stiger, ökar ocksĂ„ berĂ€kningskostnaden, vilket har negativa effekter pĂ„ prestanda. Dessutom finns det brister i beslutstödet, speciellt gĂ€llande metoder att hitta sĂ€llan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumĂ€rken frĂ„n trenddata och utnyttja det innan det nĂ„r sin höjdpunkt. Eftersom mĂ„let för denna forskning Ă€r att hjĂ€lpa smĂ„ och medelstora detaljhandlare att vĂ€xa med hjĂ€lp av MI-strategier, demonstreras effekter av AI med hjĂ€lp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjĂ€lp av försĂ€ljningsdata frĂ„n en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjĂ€lper till att avslöja vĂ€rdefulla insikter för marknadsföring, framför allt gĂ€llande dynamiken i efterfrĂ„gan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning pĂ„ investering. Dessutom hjĂ€lper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier
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