Towards an operational understanding of wellness

Despite the increased focus on wellness and wellness programs there is still no consensus as to what wellness is. This is in part because programs do not define wellness and in part because studies and programs employ vastly different outcome measures that arguably reflect other constructs such as health, well-being, and quality of life. In this paper, we suggest an operational understanding of wellness and show how wellness differs from health, quality of life and well-being. Academic literature on the subject of health, wellness, well-being and quality of life reveals confusion, as theorists and researchers frequently describe each of these constructs in a very similar manner. We argue that elements such as the context and target population in which the term wellness is used are critical for our understanding of the construct. While it is inevitable that cross-over exists between similar constructs, wellness does have distinctly identifiable features. These include: being both holistic and multidimensional, being focused on lifestyle behaviours, being about actions or processes, recognising the inter-relatedness between person and environment, and being unique by way of goal and context.

Health big data analytics : current perspectives, challenges and potential solutions

Modern health information systems can generate several exabytes of patient data, the so called "Health Big Data", per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of Health Big Data as well as the challenges and solutions for health Big Data Analytics (BDA) – the process of extracting knowledge from sets of Health Big Data – and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA.

A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

Social Media Analytics : Ein interdisziplinärer Ansatz und seine Implikationen für die Wirtschaftsinformatik [Social Media Analytics - An Interdisciplinary Approach and Its Implications for Information Systems]

Social Media Analytics ist ein neuer Forschungsbereich, in dem interdisziplinäre Methoden kombiniert, erweitert und angepasst werden, um Social-Media-Daten auszuwerten. Neben der Beantwortung von Forschungsfragen ist es ebenfalls ein Ziel, Architekturentwürfe für die Entwicklung neuer Informationssysteme und Anwendungen bereitzustellen, die auf sozialen Medien basieren. Der Beitrag stellt die wichtigsten Aspekte des Bereichs Social Media Analytics vor und verweist auf die Notwendigkeit einer fächerübergreifenden Forschungsagenda, für deren Erstellung und Bearbeitung der Wirtschaftsinformatik eine wichtige Rolle zukommt.

Social media analytics: an interdisciplinary approach and its implications for information systems

Social Media Analytics is an emerging interdisciplinary research field that aims on combining, extending, and adapting methods for analysis of social media data. On the one hand it can support IS and other research disciplines to answer their research questions and on the other hand it helps to provide architectural designs as well as solution frameworks for new social media-based applications and information systems. The authors suggest that IS should contribute to this field and help to develop and process an interdisciplinary research agenda.

Identity, addressing, authenticity and audit requirements for trust in ERP, analytics and big/open data in a “Cloud” computing environment : a review and proposal

Enterprises, both public and private, have rapidly commenced using the benefits of enterprise resource planning (ERP) combined with business analytics and “open data sets” which are often outside the control of the enterprise to gain further efficiencies, build new service operations and increase business activity. In many cases, these business activities are based around relevant software systems hosted in a “cloud computing” environment. “Garbage in, garbage out”, or “GIGO”, is a term long used to describe problems in unqualified dependency on information systems, dating from the 1960s. However, a more pertinent variation arose sometime later, namely “garbage in, gospel out” signifying that with large scale information systems, such as ERP and usage of open datasets in a cloud environment, the ability to verify the authenticity of those data sets used may be almost impossible, resulting in dependence upon questionable results. Illicit data set “impersonation” becomes a reality. At the same time the ability to audit such results may be an important requirement, particularly in the public sector. This paper discusses the need for enhancement of identity, reliability, authenticity and audit services, including naming and addressing services, in this emerging environment and analyses some current technologies that are offered and which may be appropriate. However, severe limitations to addressing these requirements have been identified and the paper proposes further research work in the area.

Framework of a process laboratory for the operational control of service processes

Service processes such as financial advice, booking a business trip or conducting a consulting project have emerged as units of analysis of high interest for the business process and service management communities in practice and academia. While the transactional nature of production processes is relatively well understood and deployed, the less predictable and highly interactive nature of service processes still lacks in many areas appropriate methodological grounding. This paper proposes a framework of a process laboratory as a new IT artefact in order to facilitate the holistic analysis and simulation of such service processes. Using financial services as an example, it will be shown how such a process laboratory can be used to reduce the complexity of service process analysis and facilitate operational service process control.

Measuring Online user engagement : the limitations of Web analytics

The ability to identify and assess user engagement with transmedia productions is vital to the success of individual projects and the sustainability of this mode of media production as a whole. It is essential that industry players have access to tools and methodologies that offer the most complete and accurate picture of how audiences/users engage with their productions and which assets generate the most valuable returns of investment. Drawing upon research conducted with Hoodlum Entertainment, a Brisbane-based transmedia producer, this chapter outlines an initial assessment of the way engagement tends to be understood, why standard web analytics tools are ill-suited to measuring it, how a customised tool could offer solutions, and why this question of measuring engagement is so vital to the future of transmedia as a sustainable industry.

Twitter data analytics – or : the pleasures and perils of studying Twitter

It might still sound strange to dedicate an entire journal issue exclusively to a single internet platform. But it is not the company Twitter Inc. that draws our attention; this issue is not about a platform and its features and services. It is about its users and the ways in which they interact with one another via the platform, about the situations that motivate people to share their thoughts publicly, using Twitter as a means to reach out to one another. And it is about the digital traces people leave behind when interacting with Twitter, and most of all about the ways in which these traces – as a new type of research data – can also enable new types of research questions and insights.

Exploring subscription renewal intention of operational cloud enterprise systems-a socio-technical approach

Despite the fact that customer retention is crucial for providers of cloud enterprise systems, only little attention has been directed towards investigating the antecedents of subscription renewal in an organizational context. This is even more surprising, as cloud services are usually offered as subscription-based pricing models with the (theoretical) possibility of immediate service cancellation, strongly opposing classical long-term IT-Outsourcing contracts or license-based payment plans of on premise enterprise systems. To close this research gap an empirical study was undertaken. Firstly, a conceptual model was drawn from theories of social psychology, organizational system continuance and IS success. The model was subsequently tested using survey responses of senior management within companies which adopted cloud enterprise systems. Gathered data was then analysed using PLS. The results indicate that subscription renewal intention is influenced by both – social-related and technology-specific factors – which are able to explain 50.4% of the variance in the dependent variable. Beneath the cloud enterprise systems specific contributions, the work advances knowledge in the area of organizational system continuance, as well as IS success.

Participatory Data Analytics: Collaborative interfaces for data composition and visualisation

This research proposes the development of interfaces to support collaborative, community-driven inquiry into data, which we refer to as Participatory Data Analytics. Since the investigation is led by local communities, it is not possible to anticipate which data will be relevant and what questions are going to be asked. Therefore, users have to be able to construct and tailor visualisations to their own needs. The poster presents early work towards defining a suitable compositional model, which will allow users to mix, match, and manipulate data sets to obtain visual representations with little-to-no programming knowledge. Following a user-centred design process, we are subsequently planning to identify appropriate interaction techniques and metaphors for generating such visual specifications on wall-sized, multi-touch displays.

Socio-emotional and operational demands on service employees

Enacting appropriate behaviors often requires service employees to suppress genuine emotions and/or express other emotions, genuine or contrived. Managing emotions to act in a socially appropriate manner constitutes a form of labor: emotional labor. If labor demands exceed the resources of the employee, burnout arises, with negative consequences for overall psychological well-being and job performance. Similarly, task related activities engender role stress, which can also lead to burnout. Both task related role demands and socio-emotional demands are likely to be omnipresent in interpersonal interactions in service settings. Accordingly, this study sets out to investigate the simultaneous impact of these job demands on burnout in front line service professionals. Based on survey data collected from allied health service workers, the study findings strongly suggest that both socio-emotional demands and task related role demands are significant determinants of workplace stress and that their simultaneous effects on employee burnout can be large.

A robust methodology for developing an operational systems view of mine water interactions

The Minerals Council of Australia’s (MCA) Water Accounting Framework (WAF) is an industry lead initiative to enable cross company communication and comparisons of water management performance. The WAF consists of two models, the Input-Output Model that represents water interactions between an operation and its surrounding environment and the Operational Model that represents water interactions within an operation. Recently, MCA member companies have agreed to use the Input-Output Model to report on their external water interactions in Australian operations, with some adopting it globally. The next step will be to adopt the Operational Model. This will expand the functionality of the WAF from corporate reporting to allowing widespread identification of inefficiencies and to connect internal and external interactions. Implementing the WAF, particularly the Operational Model, is non-trivial. It can be particularly difficult for operations that are unfamiliar with the WAF definitions and methodology, lack information pertaining to flow volumes or contain unusual configurations. Therefore, there is a need to help industry with its implementation. This work presents a step-by-step guide to producing the Operational Model. It begins by describing a methodology for implementing the Operational Model by describing the identification of pertinent objects (stores, tasks and treatments), quantification of flows, aggregation of objects and production of reports. It then discusses how the Operational Model can represent a series of challenging scenarios and how it can be connected with Input-Output Model to improve water management.

Evaluating operational cloud enterprise systems success : an organizational perspective

This paper presents a formative measurement index to assess cloud enterprise systems success. The scale development procedure is based on Moore and Benbasat (1991), including newer scale development elements which focus on the creation and assessment of formative constructs. The data is analysed using SmartPLS with a sample of 103 IT decision makers. The results show that the perception of net benefits is shaped not only by enterprise-system-specific factors like productivity improvements and higher quality of business processes, but also by factors which are specifically attributed to cloud systems, such as higher strategic flexibility. Reliability, user requirements and customization contribute most to the overall perception of system quality. Information quality shows no cloud-specific facets and is robust in the context of cloud enterprise systems.