Abstract Organisations spend much money on Information Technology (IT) development and maintenance activities with the intention that these activities will create results that enable benefits for the organisations. This paper seeks to understand potential associations between IT development and maintenance activities and the adoption of benefits management practices to realise value for the organization. The aim is also to uncover potential differences between public and private organisations. We surveyed 86 Norwegian public and private organisations, including data collected in similar surveys every five years since 1993. For the period between 1998 and 2018, we observe a stable pattern of IT work distribution. We found that organisations that managed benefits put more effort into advancing functionality for the end‐users than other organisations, and they realised more benefits. This advantage was particularly true for organisations that managed benefits beyond the early stages of the development lifecycle. Private organisations both managed and realised benefits to a larger extent than public organisations. Our findings can enable organisations to be evidence‐based when choosing management practices to achieve a higher return on investments in IT development and maintenance activities.

Although big data analytics have been claimed to revolutionize the way firms operate and do business, there is a striking lack of knowledge about how organizations should adopt and routinize such technologies to support their strategic objectives. The aim of this research is to explore how different inertial forces during deployments of big data analytics hinder the emergence of dynamic capabilities. To do so, we follow a multiple-case study design approach of 27 European firms and examine the different forms of inertia that materialize during big data analytics diffusion. The findings contribute to the growing body of knowledge on how big data analytics can be leveraged effectively to enable and strengthen a firm’s dynamic capabilities. By disaggregating dynamic capabilities into the underlying capabilities of sensing, seizing and transforming, findings indicate that different combinations of organizational inertia including economic, political, socio-cognitive, negative psychology, and socio-technical hamper the formation of each type of capability.

Abstract Considerable resources are wasted on software projects delivering less than the planned benefits. Herein, the objective is to synthesize empirical evidence of the adoption and impact of benefits management (BM) in software development, and to suggest directions for future research. A systematic review of the literature is performed and identified 4836 scientific papers of which the authors found 47 to include relevant research. While most organizations identify and structure benefits at the outset of a project, fewer organizations report implementing BM as a continuous process throughout the project lifecycle. Empirical evidence gives support for positive impact on project outcome from the following BM practices: identifying and structuring benefits, planning benefits realization, BM during project execution, benefits evaluation and the practice of having people responsible for benefits realization. The authors suggest four research directions to understand (1) why BM practices sometimes not are adopted, (2) BM in relation to other management practices, (3) BM in agile software development and (4) BM in the context of organizations' value creation logics.

For graduates in computer science and informatics to get employment as IT professionals, there is a need for their education to provide the competence sought after by employers. To become fully operational in the organization, the candidates typically also need to develop their competence there. For a university offering master’s degrees in computer science and informatics, it is important to know the employers’ view of the relevance of the study programs and what is possibly considered to be lacking. Also, it is essential to know whether the missing part needed to become fully operational should be provided by the university. In this paper, we investigate these questions by asking employers of master students in IT. A combination of in-depth interviews and a survey is used. Analysis of the findings shows that employers overall find the candidates’ competence to be adequate and that the time needed for in-house training is acceptable. We argue that collaboration between university and industry is essential to this success, pointing to a set of key steps in the process from entering a study program to becoming fully operational in work life.

This work-in-progress paper addresses how to include sustainability in higher IT-education. We consider the challenge of modifying a course by adding sustainability content to underpin the existing learning outcomes in the course. Based on a case from a user-centred design course we show that existing sustainability frameworks can be used to connect learning tasks and outcomes to sustainability competences and aspects. We conclude that further work is needed on how a similar approach can be used to include sustainability in other courses in the IT-curricula.

The potential of big data analytics in enabling improvements in business processes has urged researchers and practitioners to understand if, and under what combination of conditions, such novel technologies can support the enactment and management of business processes. While there is much discussion around how big data analytics can impact a firm’s incremental and radical process innovation capabilities, we still know very little about what big data analytics resources firms must invest in to drive such outcomes. To explore this topic, we ground this study on a theory-driven conceptualisation of big data analytics based on the resource-based view (RBV) of the firm. Based on this conceptualisation, we examine the fit between the big data analytics resources that underpin the notion, and their interplay with organisational contextual factors in driving a firm’s incremental and radical process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms are analysed by means of fuzzy set qualitative comparative analysis (fsQCA). Results show that under different combinations of contextual factors the significance of big data analytics resources varies, with specific configurations leading to high levels of incremental and radical process innovation capabilities.

The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it positively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of environmental uncertainty conditions in our model and find that information governance and BDACs have amplified effects under conditions of high environmental dynamism.

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.

With big data analytics constantly growing in importance for contemporary organizations so does the need for skilled professionals. Perhaps the most critical item noted in the age of data is the lack of people with the required skill-set to turn raw data into actionable insight. Building on this pressuring issue, the objective of this paper is to survey the status quo of technical and business-related data analytics skills in a range of different industries and identify the most important skills that will be needed in the next few years. To do so, this study builds on a sample of 202 survey responses from key executives from Norwegian firms. Our analysis reveals the level of skill-fulfilment in for technically and business-oriented employees in a number of key industries. In addition, we use survey data from an additional sample of 27 executives and interviews with 6 managers and provide a ranking of the perceived importance of data analytics-related skills according to respondents in three categories, technical skills, business and project management skills, and soft skills. Our study concludes with findings regarding the skill-gap that exists in the domain of data science as well as suggestions on how to fulfil these needs, indicating specific subject-areas that are of heightened importance.

Cities worldwide aim to reduce their greenhouse gas emissions and improve air quality for their citizens. Therefore, there is a need to implement smart city approaches to monitor, model, and understand local emissions to better guide these actions. We present our approach that deploys a number of low-cost sensors through a wireless Internet of Things (IoT) backbone and is thus capable of collecting high-granular data. Based on a flexible architecture, we built an ecosystem of data management and data analytics including processing, integration, analysis, and visualization as well as decision-support systems for cities to better understand their emissions. Our prototype system has so far been tested in two Scandinavian cities. We present this system and demonstrate how to collect, integrate, analyze, and visualize real-time air quality data.

It is widely recognized by public and private organizations, that the biggest challenge faced in light of the data revolution is finding people with the required set of skills to transform data into actionable insight. The growing interest on the role of the data scientist and the relating data analytics skills has seen an increasing amount of research on the importance of data analytics skills in the contemporary working environment. Yet, there is still limited understanding on the importance of data analytic skills, and even more, there is limited research on the discrepancies between the skills that are needed in the market and what graduates possess. To this end, this research uses a mixed-methods approach combining quantitative survey data from 113 IT executives, and qualitative interview data from 27 big data project managers to explore the significance, discrepancies, and aspects of data analytic skills. Our results show that data analytic skills significantly contribute firm performance, particularly for firms that are data-oriented. In addition, we find that the need for skills greatly exceeds those that graduates possess. Lastly, our analysis suggests that the data skills of the data scientist span multiple subject areas which are further discussed.

Mobile and wearable devices (e.g., smartphones, FitBit, and Apple Watch) are among the most transformative technologies that have created a rapid worldwide impact on almost every aspect of our soci...

In this paper, we consider the key dimensions of learning analytics applications in adaptive learning. We then review recent publications on the topic and map them to the dimensions of the reference model. Twenty one peer-reviewed articles are identified and analyzed. The findings of the review suggest that interesting work has been carried out during the last years on the topic. Yet, there is a clear lack of studies on school education and in topics outside STEM. In addition, there is a lack of studies that do not focus solely on the (self) reflection of students or tutors. Finally, the majority of the studies included look at student performance, as an adaptation parameter. Yet, a new trend of taking into account more complex student behaviors, like collaboration, is emerging.

This study aims to have a better understanding of business models of mobile ecosystems in China. On the basis of a literature review on the business model, we propose an analysis framework consisting of four major dimensions of business model concepts to study how a mobile ecosystem works, including value network, value architecture, value proposition, and value finance. Four research questions are presented in this study. To address this, a case study with the WeChat ecosystem is carried out. The key findings from the case study are presented in accordance with identified four dimensions of business model concepts.

Although Domain Analysis (DA) is important for Model Driven Development (MDD), traditional DA methods are demanding and not practical in many situations. When computer games are developed, game design (problem domain) is usually decided in a gradual way within iterations where software prototypes are constructed and playtest are performed. In such a case, it is not practical to fit a heavyweight DA in the highly iterative process. Researchers indicated that vocabularies were expected to automate game design. Such vocabularies can be reused in another form in DA tasks. In this research, the authors developed an ontology and a DA procedure based on it. To evaluate them, theoretical analysis, case studies, and a user acceptance survey were used. The results indicated that the ontology met the general requirement as a domain vocabulary, and it enhanced the DA process in an expected way. Most of external potential users (46 in total) considered the ontology useful and easy to use.

We propose an approach to context-aware advertising in which context is defined by the products currently used by a consumer. Unlike more traditional approaches, consumers are neither identified nor tracked; instead, products are tagged. An interesting use-case scenario for this model is a product-aware outdoor advertising system that dynamically selects a product to advertise based on the products identified for one person or a group of people nearby. For example, RFID tags integrated into clothing of someone passing by a digital billboard could allow for determining preferences regarding style, fashion and brands. This information would be used by a digital billboard with an RFID reader to recommend and advertise complementary and other products. There would be no inherent connection between product information and the identity of the consumer; and therefore the privacy of the consumer would not be violated. Tagging and tracking of consumer products provides opportunities for more personalized and engaging marketing experiences without introducing a privacy risk.

We have developed a mobile augmented reality application with historical photographs and information about a historical street. We follow a design science research methodology and use an extended version of the technology acceptance model (TAM) to study the acceptance of this application. A prototype has been developed in accordance with general principles for usability design, and two surveys have been conducted. A web survey with 200 participants that watched a short video demonstration of the application to validate the adapted acceptance model, and a street survey, where 42 participants got the opportunity to try the application in a live setting before answering a similar questionnaire and provide more concrete feedback. The results show that both perceived usefulness and perceived enjoyment has a direct impact on the intention to use mobile augmented reality applications with historical pictures and information. Further a number of practical recommendations for the development and deployment of such systems are provided.

This study is investigating the adoption of mobile tourism services in Norway. By expanding the Technology Acceptance Model (TAM), a research model, called mobile services acceptance model including seven research hypotheses is presented. The proposed research model and research hypotheses were empirically tested using data collected from a survey of 47 users of a mobile tourism service called extended Mobile Tourist Service Recommender (MTSR) in a city in Norway. The findings indicated that the fitness of the research model is good and strong support was found for the seven research hypotheses. Among all the factors, the personal initiatives and characteristics had the most significant influence on the intention to use MTSR.

A semiotic framework for evaluating the quality of conceptual models was proposed by (Lindland OI, Sindre G and Sølvberg A (1994) Understanding Quality in Conceptual Modelling, IEEE Software 11(2), 41–49) and has later been extended in several works. While the extensions have fixed some of the limitations of the initial framework, other limitations remain. In particular, the framework is too static in its view upon semantic quality, mainly considering models, not modelling activities, and comparing these models to a static domain rather than seeing the model as a facilitator for changing the domain. Also, the framework's definition of pragmatic quality is quite narrow, focusing on understanding, in line with the semiotics of Morris, while newer research in linguistics and semiotics has focused beyond mere understanding, on how the model is used and impact its interpreters. The need for a more dynamic view in the semiotic quality framework is particularly evident when considering process models, which themselves often prescribe or even enact actions in the problem domain, hence a change to the model may also change the problem domain directly. This paper discusses the quality framework in relation to active process models and suggests a revised framework based on this.

Abstract The large amount of work on information systems being taken up by maintenance activities has been one of the arguments of those announcing a ‘software crisis’. In this article we have investigated the applicability of this statement, and propose instead to look at the percentage of work being done on functional maintenance to assess the efficiency of the information system support in an organization, seen from the end‐user's point of view. A survey with results from 52 Norwegian organizations on development and maintenance of information systems has been performed, and some of the results are presented in the article. These are compared with results from similar investigations acting as calibration for our more novel results on functional development and maintenance. This is the first time to our knowledge that figures for functional maintenance have been investigated in a survey. From a functional point of view, the situation is better than previously perceived. Still, almost 45% of the work being done on computerized information systems in organizations is functional maintenance, i.e. not improving the information system support of the organization functionally. The amount of functional maintenance appears to be negatively correlated with the size measures for the organization, and with organizations having a well‐developed maintenance and development methodology, although further investigations should be performed to uncover the mechanisms of functional maintenance.

The main results from a survey investigation performed in Norwegian organizations within software maintenance are presented. The results are based on responses from 52 Norwegian organizations. The amount of work being reported on maintenance in the investigation is not statistically different from what is reported in other investigations. From a functional point of view, the situation is better than previously perceived. Still, almost 45% of the work being done on information systems is not improving the information system support of the organization functionally. A large effort is used for developing replacement systems, which are needed because companies are not able to maintain the systems to keep up with organizational and technical changes.>