The dual nature of information systems in enabling a new wave of hardware ventures: Towards a theory

Hardware ventures are emerging entrepreneurial firms that create new market offerings based on development of digital devices. These ventures are important elements in the global economy but have not yet received much attention in the literature. Our interest in examining hardware ventures is specifically in the role that information system (IS) resources play in enabling them. We ask how the role of IS resources for hardware ventures can be conceptualized and develop a framework for assessment. Our framework builds on the distinction of operand and operant resources and distinguishes between two key lifecycle stages of hardware ventures: start-up and growth. We show how this framework can be used to discuss the role, nature, and use of IS for hardware ventures and outline empirical research strategies that flow from it. Our work contributes to broadening and enriching the IS field by drawing attention to its role in significant and novel phenomena.

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Toward a design theory for green information systems

We report on ongoing research to develop a design theory for classes of information systems that allow for work practices that exhibit a minimal harmful impact on the natural environment. We call such information systems Green IS. In this paper we describe the building blocks of our Green IS design theory, which develops prescriptions for information systems that allow for: (1) belief formation, action formation and outcome measurement relating to (2) environmentally sustainable work practices and environmentally sustainable decisions on (3) a macro or micro level. For each element, we specify structural features, symbolic expressions, user abilities and goals required for the affordances to emerge. We also provide a set of testable propositions derived from our design theory and declare two principles of implementation.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

Quantitative approaches to content analysis : identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

On effective accounting information systems in a dynamic business environment : the role of complementing capabilities

The global business environment is witnessing tough times, and this situation has significant implications on how organizations manage their processes and resources. Accounting information system (AIS) plays a critical role in this situation to ensure appropriate processing of financial transactions and availability to relevant information for decision-making. We suggest the need for a dynamic AIS environment for today’s turbulent business environment. This environment is possible with a dynamic AIS, complementary business intelligence systems, and technical human capability. Data collected through a field survey suggests that the dynamic AIS environment contributes to an organization’s accounting functions of processing transactions, providing information for decision making, and ensuring an appropriate control environment. These accounting processes contribute to the firm-level performance of the organization. From these outcomes, one can infer that a dynamic AIS environment contributes to organizational performance in today’s challenging business environment.

Management information systems : user's guide for indentifying automatic data processing equipment (ADPE) requirements.

Issued Feb. 23, 1976.

Considerations in the selection of security measures for automatic data processing systems /

"Contributed to the Federal Information Processing Standards Task Group 15 - Computer Systems Security" -t.p.

Green IT : a matter of business and information systems engineering?

This panel discusses the impact of Green IT on information systems and how information systems can meet environmental challenges and ensure sustainability. We wish to highlight the role of green business processes, and specifically the contributions that the management of these processes can play in leveraging the transformative power of IS in order to create an environmentally sustainable society. The management of business processes has typically been thought of in terms of business improvement alongside the dimensions time, cost, quality, or flexibility – the so-called ‘devil’s quadrangle’. Contemporary organizations, however, increasingly become aware of the need to create more sustainable, IT-enabled business processes that are also successful in terms of their economic, ecological, as well as social impact. Exemplary ecological key performance indicators that increasingly find their way into the agenda of managers include carbon emissions, data center energy, or renewable energy consumption (SAP 2010). The key challenge, therefore, is to extend the devil’s quadrangle to a devil’s pentagon, including sustainability as an important fifth dimension in process change.

Specifying information systems for business process integration : a management perspective

Supply chain management and customer relationship management are concepts for optimizing the provision of goods to customers. Information sharing and information estimation are key tools used to implement these two concepts. The reduction of delivery times and stock levels can be seen as the main managerial objectives of an integrative supply chain and customer relationship management. To achieve this objective, business processes need to be integrated along the entire supply chain including the end consumer. Information systems form the backbone of any business process integration. The relevant information system architectures are generally well-understood, but the conceptual specification of information systems for business process integration from a management perspective, remains an open methodological problem. To address this problem, we will show how customer relationship management and supply chain management information can be integrated at the conceptual level in order to provide supply chain managers with relevant information. We will further outline how the conceptual management perspective of business process integration can be supported by deriving specifications for enabling information system from business objectives.

A framework for qualitative analysis of focus group data in information systems

Focus groups are a popular qualitative research method for information systems researchers. However, compared with the abundance of research articles and handbooks on planning and conducting focus groups, surprisingly, there is little research on how to analyse focus group data. Moreover, those few articles that specifically address focus group analysis are all in fields other than information systems, and offer little specific guidance for information systems researchers. Further, even the studies that exist in other fields do not provide a systematic and integrated procedure to analyse both focus group ‘content’ and ‘interaction’ data. As the focus group is a valuable method to answer the research questions of many IS studies (in the business, government and society contexts), we believe that more attention should be paid to this method in the IS research. This paper offers a systematic and integrated procedure for qualitative focus group data analysis in information systems research.