The paper examines the complex part of business user information needs in terms of IT support and split of support functionality between IT and users. Several dimensions of information needs are combined in respect to possible potential of IT support, and compared to related approaches in other research. The relation between automated efficiency and human flexibility shows that in the process of user informing in complex situations, the “last mile” should be left to human information functions. Regarding this relation, possible ways of flexible satisfaction of complex information needs are discussed.
This paper describes our approach and experiences of applying techniques in complex event processing to fleet management systems. Current fleet management systems do not have support for complex event processing, hence they are limited to reacting to simple events such as door open, and vehicle reached waypoint. We argue that fleet management systems can benefit from having support for complex event processing. In this paper we present an implemented fleet management system that supports complex event processing.
Business analytics (BA) systems create value and provide competitive advantage for organisations. They involve technology and data infrastructure, BA capabilities, and business processes that provide useful insights and support decision-making. To provide value and competitive advantage, BA capabilities should be valuable, rare, inimitable and have organisational support (VRIO). In this paper, we develop and evaluate a prototype dashboard for the VRIO assessment of BA capabilities. The dashboard is intended to support the strategic management of BA capabilities. We discuss implications of the prototype dashboard for researchers and practitioners and suggest directions for future research.
Kristens Gudfinnsson, Mikael Berndtsson, Mattias Strand
265 - 276
Business intelligence (BI) has fundamentally changed how many companies conduct their business. In literature, focus has been on volume-operation companies that provide services to millions of customers. In contrast, complex-systems companies have fewer customers and pursue customer needs by providing more customized products and services. This paper presents the results at a case of a complex-systems company with the overall aim to see how a complex-systems company has taken advantage of BI. In addition, a framework was used to measure the BI maturity of the company. Literature also emphasis that complex-system companies may benefit from adopting BI applications from volume-operations companies, but the results indicate that there may be a difference in the importance of BI tools, which in turn may negatively influence such cross-category adoptions.
In this paper, we propose the concept: the BI Sweet Spot. The BI Sweet Spot ecosystem includes mobile computing, cloud computing and Big Data. We provide an overview for each of the key components and explain how these three components support the BI Sweet Spot. We also discuss best practices for managing these essential components. This study is the first-of-its-kind work in the BI research that considers the inter-relationships and the combined effect of mobile, cloud and Big Data.
The needs of non-profit organizations for accountability are no less than that of for-profit or public sector organizations, yet typically they allocate resources to front-line activities rather than organizational systems. A research and development project is being conducted that aims to develop a conceptual framework which would guide organizations in the non-profit sector in improving their decision-making and reporting performance through the adoption of business intelligence solutions. This paper uses and reflects on Action Design Research, a contemporary socio-technical approach that draws on action research and design research, as a means for framing the project's processes, products, and context. This paper contributes to the understanding of Action Design Research by reflecting on what it reveals about the case study and how related theories for ‘design and action’ such as small wins and user participation can be brought into the Action Design Research framework.
Mobile Business Intelligence (m-BI) enhances the capabilities of organizations to take decisions ‘on-the-move’. M-BI emerged as an extension of BI and many companies have or are considering implementing it. Being an innovation, m-BI reflects an organizing vision, created by a broad community, which is prominent during the comprehension process of this innovation. Little is known how the organizing vision affects the decision to adopt a certain innovation, and moreover these studies are completely lacking within the m-BI area. A qualitative approach has been embraced and interviews with different organizations that have already decided to adopt m-BI have been conducted. This study revealed that industrial research reports, vendors marketing campaigns and success stories produced by the collaboration among vendors, analysts, journalist and early adopters have a strong influence on the early phases of the organizational decision-making process to adopt m-BI. Increasing internal and external legitimacy, reducing the risk to be left behind, gaining competitive advantage, following the footsteps of high-performing companies, increasing their prestige and reputation, are the main reasons for why organizations may start the decision-making process to adopt m-BI; all this under the umbrella of the organizing vision.
This paper proposes a Decision-Guided Group Package Recommender (DG-GPR) framework, which provides recommendations on dynamically defined packages of products and services. This framework is based on: (1) defining the space of alternatives; (2) eliciting the utility function for each individual decision maker; (3) estimating the group utility function; (4) using the group utility function to find an optimal recommendation alternative; (5) constructing a set of diverse recommendations which contains the optimal recommendation alternative; and (6) applying a Hybrid Condorcet-Instant Runoff Voting method from social choice theories, to refine the recommendations. A preliminary experimental study is conducted which shows that the proposed framework significantly outperforms its previous extension which in turn had previously outperformed three popular aggregation strategies.
Hospitals often retain inadequate systems because they are concerned about the organisational and financial cost of IT investments that do not clearly lead to increased revenue. Previous work has not carefully examined the health care, organisational and business process issues caused by widespread underinvestment in Health Information Systems, leading to compromised decision making.
Methods: This paper describes the processes involved in a common data management problem in hospitals by investigating the use of a legacy semi-integrated system. It combines a number of methods in a novel way – interviews, surveys and a simulation – to show how data loss affects the length of service time for patients.
Findings: The paper finds that data delay or loss inclines medical consultants to make decisions about patient care without full access to all requested data, leading to repeat patient scanning, risks to patients and costs to the system.
Contribution: It examines how the processes leading to unreliable data delivery influence the decision-making behaviour of actors in a system. It shows how problematic processes lead to doctors making decisions even if data is absent and illustrates how inertia in adopting integrated health IS, providing improved decision support, can have an impact on patient care.
In this paper a collaborative WEB platform is proposed to implement the collaborative manufacturing environment. This is to offer maximum interoperability between all the distributed participants of a Collaborative Manufacturing Network (CMN) and their management information systems. Furthermore, this paper develops a simple framework to help to understand the collaboration that is afforded by Web 2.0 applications. The proposed framework is used to examine how production of agents can create, share and exchange experiences on diagnosis and resources failures with each other to have new ideas or useful information for the decision-making. The agent-based approach provides a collaborative WEB interface to facilitate the remote interaction with human users involved in the risk management process. The global collaborative decision model is applied to the spunlace nonwovens production industry.
Over the past five decades, the area of decision support systems has made a significant progress toward becoming a solid academic discipline. A number of prior studies have been conducted to assess the extent of progress within these stages in the DSS area. Among them, a study of Eom  has provided biblio-metric evidence that the decision support system has made meaningful progress over the past four and a half decades (1969–2004). The primary data for this study were gathered from a total of 498 citing articles in the BI area over the past eight years (2005–2012). This study uses author cocitation analysis (ACA). ACA is a technique of bibliometrics that applies quantitative methods to various media of communication such as books, journals, conference proceedings, and so on. Indeed, the results of this study showed the substantial changes in the intellectual structure of BI research. First, many research subspecialties appeared in the previous investigations up to 2004 have disappeared. They are individual differences/user interfaces, model management, and evaluation research during the period of 1969–2004. Second, through this research, we also noticed that the group support systems and foundation research are weakening in terms of the number of researchers and therefore the substantiality of these research topics are diminishing. On the other hand, the focus of business intelligence research is shifting to knowledge management and data mining.
Firms are increasingly organizing virtual new product development (NPD) initiatives to solicit ideas and suggestions from external parties such as customers. While virtual NPD provides firms with unprecedented access to external knowledge, the diversity of input and participants also poses challenges to decision making. This study develops the concept of decision openness and examines whether making decisions with respect to the opinion of external participants influences product sales growth over time. Decision openness in product development activities is distinguished from that in commercialization activities to examine their differential impacts.
This study presents an empirical research, which examines how scientific social networking sites can support researchers' work. In particular, the study investigates knowledge sharing behaviors of users of the scientific platform ResearchGate. A questionnaire was distributed to members of the social network. One hundred and fifteen responses were obtained. The results of this study provide a profile of scientific social networks users. Users consider these platforms mainly as a means to communicate their results and their knowledge. The level of utilization of knowledge obtained from these social networks is still low.
Innovating is a prerequisite for competing also for retail sector and has attracted huge interest concerning the role of technology management for successfully adopting technology-based innovations. In this competitive scenario, the design of new and attractive retail environments able to better influence consumer decision-making process plays a critical role, with emphasis on stores characterized by large size (i.e. department stores, huge anchor stores, etc.). Despite the wide deal of research in involving consumers in the design process, identifying the best practices for achieving consumers' contribution to the co-design process has proven difficult. This study extents previous knowledge on consumers' involvement in collaborative innovation by focusing on retail environments, starting from the case study of a new collaborative platform devoted the design of a new store in Italy and the experimental design based on the response of 150 potential consumers. Empirical findings suggest a set of users' motives to participate to the development of the new store, by providing insights for defining novel tools for achieving more consumers' contribution with benefits for innovation and decision-making process.
Big Data is not the first and most definitely not the last new term that the IT industry is going to coin in order to drive interest and investment in new technology. Moreover, with these new terms, an opportunity is afforded for the research community to objectively understand the impact (or lack thereof) on organizations and decision makers. This paper provides a high-level framework to guide researchers in the area of Big Data through a conceptualization of the Information Supply Chain. The Information Supply Chain can be used as a scoping device for researchers in positioning their work but also as a tool to enable stronger objectivity and prevent an automatic resistance or acceptance of the new term/trend.
The term ‘big data’ is used to describe data that are beyond the capabilities of an organization to store, analyze and use for accurate and timely decision making. They have alternately been described in terms of characteristics of volume, velocity, variety, veracity. We propose another characteristic of volatility for big data that should be considered in their use for decision making. We utilize a case study approach with a mid-sized company in the hospitality industry to elucidate challenges that an organization faces in developing a big data strategy and highlight research needed in this domain. Challenges identified were technical (inconsistent and unstandardized data, implementation and use of new analytics platforms, obtaining a global view of data, visualization of data, integrating mobile data), organizational (finding people with the right skills, users' desire for customization), and strategic management (finding return on investment in big data, alignment of business and analytics strategies, leadership of analytics initiatives and thought). Decision questions to be addressed in using big data for a marketing decision are illustrated. We demonstrate the use of the decision framework and illustrate challenges identified in the case study with sentiment analysis of Twitter data.
Software as a service, cloud computing and predictive analytics are examples of new phenomena that can have a profound impact on the pervasiveness and accessibility of organizational decision making. The power of propensity scoring based on historic data delivered as a service via cloud computing heralds the availability of capabilities that previously required teams of quantitative specialist to deliver. This paper outlines the development of a predictive analytics cloud service for customer retention and attempts to position the outcomes of this implementation in the context of decision support, knowledge management and work practice evolution.
Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).
Danilo Gambelli, Francesco Solfanelli, Raffaele Zanoli
441 - 446
The aim of this paper is to provide a DSS to improve the effectiveness of the inspection procedures in organic certification. The analysis is based on data from the archives of the largest Italian organic control body. Bayesian Networks are used to develop a DSS combining available data and expert information, with the aim to support inspectors in the design of inspection schemes that could lead to a high likelihood of non-compliance detection. Results show that there is scope to increase the effectiveness of inspections, and in particular by organizing the sampling at key times during the year.
Noor Maizura Mohamad Noor, Ahmad Faiz Ghazali, Md Yazid Mohd Saman, Mohd Nazli Mohd Nor
447 - 458
Decision-making processes involve conscious and unconscious consideration that can be estimated qualitatively or quantitatively. It can be divided into several phases, including before or after making decisions. Risk concept can be integrated into decision-making processes in order to provide more evaluation besides merely depends on criteria and alternatives. Risk impact, risk averse, risk acceptance, and risk residual are some of the parts from risk assessment that can be included in computerized decision-making using decision support system (DSS). This DSS can then be implemented in either many organizations' strategic plan, any private companies, public universities or law enforcements.