ICT for eco-sustainability: an assessment of the capability of the Australian ICT sector

Executive summary As eco-sustainability issues become increasingly important to most, if not all, Australian organisations, the Information and Communication Technology (ICT) industry is expected to provide solutions that reduce material consumption (dematerialise), emissions (decarbonise), and energy use and waste production (demobilise) in both the ICT infrastructure and the business processes and practices of industries. The term \u27Green ICT\u27 represents this eco- sustainability enabling role of the ICT industry. The School of Business Information Technology and Logistics, RMIT University in collaboration with the Australian Information Industries Association (AIIA) surveyed all members and affiliates of the AIIA at the beginning of 2010 to understand Australian ICT firms\u27 capability to enhance the eco-sustainability of other industries. Based on data collected from 133 ICT firms, this report constitutes the first comprehensive study that exclusively focuses on the Australian ICT industry

The Propensity To Continue Service Internationalization - A Model Of Planned Behavior

The present study investigates the propensity to continue internationalization by experienced international service providers. Enhancing the cognitive-behavioral root of internationalization process theory (Johanson and Vahlne 1977; 1990), this paper builds upon Ajzen’s (1991) theory of planned behavior. Consistent with the latter theory, the propensity to continue service internationalization is modelled as behavioral intention that results from (1) an overall attitude towards internationalization, (2) structural and strategic behavioral norms, and (3) inhibiting factors that exert behavioral control upon propensity. Using partial least squares analysis, a propensity model is tested on a sample of 193 international service providers. The results suggest that our model explicates and enhances the cognitive/affective character of internationalization process theory.management and organization theory ;

Strategic adoption of logistics and supply chain management

© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to develop a thorough understanding of the adoption of logistics and supply chain management (SCM) in practice, particularly at a strategic level, through an investigation of the four perspectives taxonomy of the relationship between logistics and SCM. Design/methodology/approach: Based on a comprehensive literature review, three specific research questions are proposed. The empirical work addresses these questions and comprised three phases: focussed interviews, a questionnaire survey and focus groups. Findings: The findings provide a usage profile of the four perspectives and indicate a divergence between the understanding and adoption of logistics and SCM principles and concepts at a strategic level in firms. The findings also identify the critical success factors (CSFs) and inhibitors to success in addressing this divergence. Research limitations/implications: The insights generated using the authors’ methodologically pluralist research design could be built upon to include case studies, grounded theory and action research. Replicating the research in other geographical areas could facilitate international comparisons. Practical implications: The findings allow practitioners to compare their perspectives on the relationship between logistics and SCM with those of their peers. The CSFs and inhibitors to success provide a rational basis for realising the strategic potential of logistics and SCM in practice. Originality/value: New insights are generated into practitioner perspectives vis-à-vis logistics vs SCM. A fresh understanding of those factors which drive and hinder the adoption of strategic SCM is also developed and presented

A Smart Modular Wireless System for Condition Monitoring Data Acquisition

Smart sensors, big data, the cloud and distributed data processing are some of the most interning changes in the way we collect, manage and treat data in recent years. These changes have not significantly influenced the common practices in condition monitoring for shipping. In part this is due to the reduced trust in data security, data ownership issues, lack of technological integration and obscurity of direct benefit. This paper presents a method of incorporating smart sensor techniques and distributed processing in data acquisition for condition monitoring to assist decision support for maintenance actions addressing these inhibitors

Business Intelligence and Analytics in Small and Medium-Sized Enterprises

This thesis presents a study of Business Intelligence and Analytics (BI&A) adoption in small and medium-sized enterprises (SMEs). Although the importance of BI&A is widely accepted, empirical research shows SMEs still lag in BI&A proliferation. Thus, it is crucial to understand the phenomenon of BI&A adoption in SMEs. This thesis will investigate and explore BI&A adoption in SMEs, addressing the main research question: How can we understand the phenomenon of BI&A adoption in SMEs? The adoption term in this thesis refers to all the IS adoption stages, including investment, implementation, utilization, and value creation. This research uses a combination of a literature review, a qualitive exploratory approach, and a ranking-type Delphi study with a grounded Delphi approach. The empirical part includes interviews with 38 experts and Delphi surveys with 39 experts from various Norwegian industries. The research strategy investigates the factors influencing BI&A adoption in SMEs. The study examined the investment, implementation, utilization, and value creation of BI&A technologies in SMEs. A thematic analysis was adopted to collate the qualitative expert interview data and search for potential themes. The Delphi survey findings were further examined using the grounded Delphi method. To better understand the study’s findings, three theoretical perspectives were applied: resource-based view theory, dynamic capabilities, and IS value process models. The thesis’ research findings are presented in five articles published in international conference proceedings and journals. This thesis summary will coherently integrate and discuss these results.publishedVersio

Ship product modelling

This paper is a fundamental review of ship product modeling techniques with a focus on determining the state of the art, to identify any shortcomings and propose future directions. The review addresses ship product data representations, product modeling techniques and integration issues, and life phase issues. The most significant development has been the construction of the ship Standard for the Exchange of Product Data (STEP) application protocols. However, difficulty has been observed with respect to the general uptake of the standards, in particular with the application to legacy systems, often resulting in embellishments to the standards and limiting the ability to further exchange the product data. The EXPRESS modeling language is increasingly being superseded by the extensible mark-up language (XML) as a method to map the STEP data, due to its wider support throughout the information technology industry and its more obvious structure and hierarchy. The associated XML files are, however, larger than those produced using the EXPRESS language and make further demands on the already considerable storage required for the ship product model. Seamless integration between legacy applications appears to be difficult to achieve using the current technologies, which often rely on manual interaction for the translation of files. The paper concludes with a discussion of future directions that aim to either solve or alleviate these issues

Critical Management Issues for Implementing RFID in Supply Chain Management

The benefits of radio frequency identification (RFID) technology in the supply chain are fairly compelling. It has the potential to revolutionise the efficiency, accuracy and security of the supply chain with significant impact on overall profitability. A number of companies are actively involved in testing and adopting this technology. It is estimated that the market for RFID products and services will increase significantly in the next few years. Despite this trend, there are major impediments to RFID adoption in supply chain. While RFID systems have been around for several decades, the technology for supply chain management is still emerging. We describe many of the challenges, setbacks and barriers facing RFID implementations in supply chains, discuss the critical issues for management and offer some suggestions. In the process, we take an in-depth look at cost, technology, standards, privacy and security and business process reengineering related issues surrounding RFID technology in supply chains

Model-driven engineering approach to design and implementation of robot control system

In this paper we apply a model-driven engineering approach to designing domain-specific solutions for robot control system development. We present a case study of the complete process, including identification of the domain meta-model, graphical notation definition and source code generation for subsumption architecture -- a well-known example of robot control architecture. Our goal is to show that both the definition of the robot-control architecture and its supporting tools fits well into the typical workflow of model-driven engineering development.Comment: Presented at DSLRob 2011 (arXiv:cs/1212.3308

Harnessing Artificial Intelligence Capabilities Through Cloud Services: a Case Study of Inhibitors and Success Factors

Industry and research have recognized the need to adopt and utilize artificial intelligence (AI) to automate and streamline business processes to gain competitive edges. However, developing and running AI algorithms requires a complex IT infrastructure, significant computing power, and sufficient IT expertise, making it unattainable for many organizations. Organizations attempting to build AI solutions in-house often opt to establish an AI center of excellence, accumulating huge costs and extremely long time to value. Fortunately, this deterrence is eliminated by the availability of AI delivered through cloud computing services. The cloud deployment models, Infrastructure as a Service, Platform as a Service, and Software as a Service provide various AI services. IaaS delivers virtualized computing resources over the internet and enables the raw computational power and specialized hardware for building and training AI algorithms. PaaS provides development tools and running environments that assist data scientists and developers in implementing code to bring out AI capabilities. Finally, SaaS offers off-the-shelf AI tools and pre-trained models provided to customers on a commercial basis. Due to the lack of customizability and control of pre-built AI solutions, this empirical investigation focuses merely on IaaS and PaaS-related AI services. The rationale is associated with the complexity of developing, managing and maintaining customized cloud infrastructures and AI solutions that meet a business's actual needs. By applying the Diffusion of Innovation (DOI) theory and the Critical Success Factor (CSF) method, this research explores and identifies the drivers and inhibitors for AI services adoption and critical success factors for harnessing AI capabilities through cloud services.Based on a comprehensive review of the existing literature and a series of nine systematic interviews, this study reveals ten factors that drive- and 17 factors that inhibit the adoption of AI developer tools and infrastructure services. To further aid practitioners and researchers in mitigating the challenges of harnessing AI capabilities, this study identifies four affinity groups of success factors: 1) organizational factors, 2) cloud management factors, 3) technical factors, and 4) the technology commercialization process. Within these categories, nine sub-affinity groups and 20 sets of CSFs are presented