Prior work on the technology acceptance model (TAM) is mainly devoted to the influence of TAM’s core motivational factors and their impact on behavioral intent toward IS acceptance. Relatively little research has focused on what specific system design characteristics motivate individuals toward IS acceptance. This article identified specific systems design factors and examined their impact on TAM’s motivational factors through the TAM. The findings will help designers to design and implement better user-accepted systems.

Transportation infrastructure projects are prone to cost and schedule overruns. At the time of contract award, a construction contingency budget is often used to accommodate for unplanned events such as scope changes. Recent empirical research has shown that rework during construction as a result of design changes, errors and omission are the major contributors of overruns in projects. The statistical characteristics of rework, and cost and schedule overruns that are experienced from a project's contract award for 58 Australian transportation infrastructure projects are analysed. Theoretical probability distributions are fitted to the rework, cost and schedule overrun data. Goodness of fit tests are used in conjunction with probability-probability (P-P) plots to compare the sample distribution from the known theoretical distribution. A Generalised Logistic probability density function is found to describe the behaviour of cost-overruns and provides the best overall distribution fit. The best fitting distribution for schedule overruns and rework data were the Four Parameter Burr and a Johnson SB distribution, respectively. The distributions are used to calculate the probability of rework, cost and schedule overruns being experienced. A case illustration is presented and discussed to demonstrate how the derived probabilities may be utilised in practice.

E-government services involve many stakeholders who have different objectives that can have an impact on success. Among these stakeholders, citizens are the primary stakeholders of government activities. Accordingly, their satisfaction plays an important role in e-government success. Although several models have been proposed to assess the success of e-government services through measuring users' satisfaction levels, they fail to provide a comprehensive evaluation model. This study provides an insight and critical analysis of the extant literature to identify the most critical factors and their manifested variables for user satisfaction in the provision of e-government services. The various manifested variables are then grouped into a new quantitative analysis framework consisting of four main constructs: cost; benefit; risk and opportunity (COBRA) by analogy to the well-known SWOT qualitative analysis framework. The COBRA measurement scale is developed, tested, refined and validated on a sample group of e-government service users in Turkey. A structured equation model is used to establish relationships among the identified constructs, associated variables and users' satisfaction. The results confirm that COBRA framework is a useful approach for evaluating the success of e-government services from citizens' perspective and it can be generalised to other perspectives and measurement contexts.

In recent years Government departments and public/private organisations are becoming increasingly transparent with their data to establish the whole new paradigm of big open data. Increasing research interest arises from the claimed usability of big open data in improving public sector reforms, facilitating innovation, improving supplier and distribution networks and creating resilient supply chains that help improve the efficiency of public services. Despite the advantages of big open data for supply chain and operations management, there is severe shortage of empirical analyses in this field, especially with regard to its acceptance. To address this gap, in this paper we use an extended technology acceptance model to empirically examine the factors affecting users’ behavioural intentions towards public sector big open data. We outline the importance of our model for operations and supply chain managers, the limitations of the study, and future research directions.

This paper provides a management perspective of organisational factors that contributes to the reduction of food waste through the application of design science principles to explore causal relationships between food distribution (organisational) and consumption (societal) factors. Qualitative data were collected with an organisational perspective from commercial food consumers along with large-scale food importers, distributors, and retailers. Cause-effect models are built and “what-if” simulations are conducted through the development and application of a Fuzzy Cognitive Map (FCM) approaches to elucidate dynamic interrelationships. The simulation models developed provide a practical insight into existing and emergent food losses scenarios, suggesting the need for big data sets to allow for generalizable findings to be extrapolated from a more detailed quantitative exercise. This research offers itself as evidence to support policy makers in the development of policies that facilitate interventions to reduce food losses. It also contributes to the literature through sustaining, impacting and potentially improving levels of food security, underpinned by empirically constructed policy models that identify potential behavioural changes. It is the extension of these simulation models set against a backdrop of a proposed big data framework for food security, where this study sets avenues for future research for others to design and construct big data research in food supply chains. This research has therefore sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions.

The importance of evaluation and optimization of electronic government (e-government) services is imperative if the government organisations are to have an effective impact on the success and take-up of the services offered. Transport For London's (TFL) London Congestion Charging (LCC) is one of the innovative electronic services (e-services) introduced by the United Kingdom (UK) government to the citizens. While some studies have addressed the impact of the introduction of the congestion charge there has been a dearth of research performed to address user (citizen) satisfaction of the online LCC system. Therefore, this research seeks to measure the citizen satisfaction of using the LCC online payment system offered by TFL. The citizen satisfaction in this context is measured using the four dimensions from the COBRA framework that comprise the cost, opportunity, benefits and risk assessment constructs. This paper presents the findings of a survey of 500 users of the TFL LCC online payment system. It also reports the qualitative feedback obtained from the participants that can be used to determine the areas that need further improvement in the current LCC e-service and potential influences on user satisfaction.

Working collaboratively with internal and external partners (suppliers, customers and internal stakeholders) has been at the epicentre of product design. Knowledge sharing has been well recognised in this context. However, there is limited research that has addressed the role of social media/Web 2.0 in facilitating knowledge sharing for sense- and decision-making within product design. To address this gap, this study draws on the resource-based view (RBV) of the firm and two vignettes that relate to ‘collaborative co-design’ and ‘collaborative design-to-order’. We illustrate the role of social media/Web 2.0 in building knowledge sharing capabilities for sense- and decision-making for internal and external partners during product design. Limitations and further research into the use of social media/Web 2.0 are also discussed.

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape.Given the significant nature of the BDand BDA, this paper presents a state-ofthe- art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/ employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 –What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.

Information systems (IS) facilitate organisations to increase responsiveness and reduce the costs of their supply chain. This paper seeks to make a contribution through exploring and visualising knowledge mapping from the perspective of IS investment evaluation. The evaluation of IS is regarded as a challenging and complex process, which becomes even more difficult with the increased complexity of IS. The intricacy of IS evaluation, however, is due to numerous interrelated factors (e.g. costs, benefits and risks) that have human or organisational dimensions. With this in mind, there appears to be an increasing need to assess investment decision-making processes, to better understand the often far-reaching implications associated with technology adoption and interrelated knowledge components (KC). Through the identification and extrapolation of key learning issues from the literature and empirical findings, organisations can better improve their business processes and thereby their effectiveness and efficiency, while preventing others from making costly oversights that may not necessarily be only financial. In seeking to enlighten the often obscure evaluation of IS investments, this paper attempts to inductively emphasise the dissemination of knowledge and learning through the application of a fuzzy Expert System (ES) based knowledge mapping technique (i.e. Fuzzy Cognitive Map [FCM]). The rationale for exploring knowledge and IS investment evaluation is that a knowledge map will materialise for others to exploit during their specific technology evaluation. This is realised through conceptualising the explicit and tacit investment drivers. Among the several findings drawn from this research, the key resulting knowledge mapping through FCM demonstrated the complex, multifaceted and emergent behaviour of causal relationships within the knowledge area. The principal relationships and knowledge within IS investment evaluation are illustrated as being determined by a blend of managerial and user perspectives.

Public–Private Partnerships (PPPs) have become a critical vehicle for delivering infrastructure worldwide. Yet, the use of such a procurement strategy has received considerable criticism, as they have been prone to experiencing time/cost overruns and during their operation poorly managed. A key issue contributing to the poor performance of PPPs is the paucity of an effective and comprehensive performance measurement system. There has been a tendency for the performance of PPPs to be measured based on their ex-post criteria of time, cost and quality. Such criteria do not accommodate the complexities and lifecycle of an asset. In addressing this problem, the methodology of sequential triangulation is used to develop and examine the effectiveness of a ‘Process Management Life Cycle Performance Measurement System’. The research provides public authorities and private-sector entities embarking on PPPs with a robust mechanism to effectively measure, control and manage their projects’ life cycle performances, ensuring the assets are ‘future proofed’.