If we implement it, will they come? User resistance in postacceptance usage behaviour within a business intelligence systems context

The aim of this article is to examine individual, corporate and technology-related factors that shape user resistance in business intelligence systems (BIS) post-acceptance usage behaviour. The author develops a conceptual framework and a series of propositions, grounded on previous studies of user resistance to information systems (IS) and post-acceptance usage. The framework proposes that three individual-level variables (loss of power, change in decision-making approach, change of job or job skills), four corporate-level variables (information culture, communication, user training, service quality) and a technology-related variable (system issues) can be attributed to fuel user resistance towards BIS post-acceptance usage stages. A series of propositions is offered that aims to stimulate empirical research in this topical subject. Despite wide acknowledgement of the importance of user resistance for IS implementation success, this area has been under-researched in the field of BIS. This article draws insights from theoretical and empirical studies to shed some light on this area. A framework is presented which transcends previous works on user resistance to IS by looking at the context of BIS use within the voluntary use environment

A machine learning approach

Castelli, M., Groznik, A., & Popovič, A. (2020). Forecasting electricity prices: A machine learning approach. Algorithms, 13(5), 1-16. [119]. https://doi.org/10.3390/A13050119The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique-namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.publishersversionpublishe

Conceptual Model of Business Value of Business Intelligence Systems

With advances in the business intelligence area, there is an increasing interest for the introduction of business intelligence systems into organizations. Although the opinion about business intelligence and its creation of business value is generally accepted, economic justification of investments into business intelligence systems is not always clear. Measuring the business value of business intelligence in practice is often not carried out due to the lack of measurement methods and resources. Even though the perceived benefits from business intelligence systems, in terms of better information quality or achievement of information quality improvement goals, are far from being neglected, these are only indirect business benefits or the business value of such systems. The true business value of business intelligence systems hides in improved business processes and thus in improved business performance. The aim of the paper is to propose a conceptual model to assess business value of business intelligence systems that was developed on extensive literature review, in-depth interviews, and case study analysis for researching business intelligence systems’ absorbability capabilities or key factors facilitating usage of quality information provided by such systems respectively

Benchmark of different classification algorithms on high-level image features from convolutional layers

Janke, J., Castelli, M., & Popovič, A. (2019). Analysis of the proficiency of fully connected neural networks in the process of classifying digital images: Benchmark of different classification algorithms on high-level image features from convolutional layers. Expert Systems with Applications, 135, 12-38. https://doi.org/10.1016/j.eswa.2019.05.058Over the course of research on convolutional neural network (CNN) architectures, few modifications have been made to the fully connected layers at the ends of the networks. In image classification, these neural network layers are responsible for creating the final classification results based on the output of the last layer of high-level image filters. Before the breakthrough of CNNs, these image filters were handcrafted, and any classification algorithm could be applied to their output. Because neural networks use gradient descent to learn their weights subject to the classification error, fully connected neural networks are a natural choice for CNNs. But a question arises: Are fully connected layers in a CNN superior to other classification algorithms? In this work, we benchmark different classification algorithms on CNNs by removing the existing fully connected classifiers. Thus, the flattened output from the last convolutional layer is used as the input for multiple benchmark classification algorithms. To ensure the generalisability of the findings, numerous CNNs are trained on CIFAR-10, CIFAR-100, and a subset of ILSVRC-2012 with 100 classes. The experimental results reveal that multiple classification algorithms, namely logistic regression, support vector machines, eXtreme gradient boosting, random forests and K-nearest neighbours, are capable of outperforming fully connected neural networks. Furthermore, the superiority of a particular classification algorithm depends on the underlying CNN structure and the nature of the classification problem. For classification problems with many classes or for CNNs that produce many high-level image features, other classification algorithms are likely to perform better than fully connected neural networks. It follows that it is advisable to benchmark multiple classification algorithms on high-level image features produced from the CNN layers to improve classification performance.authorsversionpublishe

Exploring the Role of Creativity in Business Analytics Use: A Business Analysts Perspective

Today’s businesses heavily rely on Business Analytics (BA) for informed decision-making, developing and sustaining competitive advantage, and growth. For achieving these goals, organizations aspire for creativity and creative solutions. Creativity is an important source of organizational innovation, competitive advantage, and growth, yet it remains an under-researched area in the information systems discipline. Intrigued by the influential role of creativity in organizational performance, we aim to explore its role in using BA. Through a qualitative approach, we aim to establish the importance of creativity for BA use and provide a deeper understanding of users’ (i.e., business analysts’) perceptions of creativity in the BA use process

SOA Adoption Phases - A Case Study

The paper argues that attitudes to SOA follow a typical hype cycle from Technological Trigger, Peak of Inflated Expectations, and a Trough of Disillusionment to the more recent realization that SOA is a concept that may offer certain benefits but has several limitations. Themain research question studies how the attitude to SOA changes in various phases of the hype cycle, how the SOA implementation cycle and an increase in business process maturity (BPMa) are interconnected and which factors influence the transition between the hype cycle phases. The paper shows that an organization’s success with implementing SOA depends on its ability to match the SOA implementation with an increase in BPMa. The dual purpose of implementing SOA is shown in the first framework: to assure the coherence of IT assets and to assure business/IT alignment. In the second framework, the interconnection of SOA and BPMa and its role in transiting through the hype cycle phases is outlined. The findings are analyzed using a longitudinal case study of a large Slovenian company

Design Thinking: The New Mindset for Competitive Intelligence? Impacts on the Competitive Intelligence Model

Competitive Intelligence (CI) is becoming of essence due to the need for improving firm performance in an increasingly volatile, uncertain, complex and ambiguous (V.U.C.A.) world. The CI model, however, has not evolved to address evolving intelligence needs, highlighting an opportunity for further research on how to fit for purpose the CI process itself. This study found that Design Thinking (DT) mindset and process has potential for the application to the CI model, improving efficiency both on the overall process, at each stage and in CI. This paper focus on researching the CI process and recognizing its main pitfalls, explaining how DT can help fix or improve on these, and propose a new process which incorporates the aforementioned results. The final part of the study analyses the implications for both CI practitioners and the CI discipline, while pointing to future research with the aim of validating this suggested framework

The case of Amazon.com, Inc.

Castelli, M., Manzoni, L., Vanneschi, L., & Popovič, A. (2017). An expert system for extracting knowledge from customers’ reviews: The case of Amazon.com, Inc. Expert Systems with Applications, 84(October), 117-126. https://doi.org/10.1016/j.eswa.2017.05.008E-commerce has proliferated in the daily activities of end-consumers and firms alike. For firms, consumer satisfaction is an important indicator of e-commerce success. Today, consumers’ reviews and feedback are increasingly shaping consumer intentions regarding new purchases and repeated purchases, while helping to attract new customers. In our work, we use an expert system to predict the sentiment of a product considering a subset of available customers’ reviews.authorsversionpublishe

Business Intelligence Capability: The Effect of Top Management and the Mediating Roles of User Participation and Analytical Decision Making Orientation

In this study, we draw on the structurational model of technology in an institutional setting to investigate how top management affects the development of a firm’s business intelligence (BI) capability. We propose a multiple mediator model in which organizational factors, such as user participation and analytical decision making orientation, act as mediating mechanisms that transmit the positive effects of top management championship to advance a firm’s BI capability. BI capability has two distinct aspects: information capability and BI system capability. Drawing on data collected from 486 firms from six different countries, we found support for the mediating effects of top management championship through user participation and analytical decision making orientation. These findings contribute to a nuanced understanding of how firms can develop BI capability. This study is one of the first to comprehensively investigate the antecedents of BI capability

THE ROLE OF MOBILE BI CAPABILITIES IN MOBILE BI SUCCESS

While IS success research has gained considerable attention within academia in the past, understanding its constitunts and the relationships among them has also emerged within professional fields. Since extant inquiries on the relationships among IS success dimensions have produced equivocal findings, the need to address IS success in the context of specific technologies has been regularly highlighted. Within business intelligence (BI) field one such pervasive technology gaining considerable attention is mobile BI (m-BI). \ \ Despite the recognized valu that m-BI brings to firms, our understanding of the success of m-BI is limited. To address this gap, we conducted a quantitative study of key informants using m-BI, employing multiple data collection methods to understand what the key m-BI capabilities are and other success dimensions being perceived as important by users, and how they can be assessed. \ \ Our contribution to the BI and IT business valu literature is twofold. First, our results highlight the users´ perceptions about m-BI capabilities that are deemed important for increasing satisfaction with m-BI and its use. Second, while these capabilities have a relatively high explanatory power for user satisfaction, their power to explain m-BI use is found to be rather low, suggesting there are other unobserved organizational characteristics importantly affecting m-BI usage behaviour, thus calling for further investigation