Business & Information Systems Engineering 

About: Business & Information Systems Engineering is an academic journal published by Springer Nature. The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 1867-0202. Over the lifetime, 165 publications have been published receiving 6142 citations. The journal is also known as: Business and information systems engineering & BISE.

Internet of Things - Technology and Value Added

Abstract: It has been next to impossible in the past months not to come across the term ‘‘Internet of Things’’ (IoT) one way or another. Especially the past year has seen a tremendous surge of interest in the Internet of Things. Consortia have been formed to define frameworks and standards for the IoT. Companies have started to introduce numerous IoTbased products and services. And a number of IoT-related acquisitions have been making the headlines, including, e.g., the prominent takeover of Nest by Google for $3.2 billion and the subsequent acquisitions of Dropcam by Nest and of SmartThings by Samsung. Politicians as well as practitioners increasingly acknowledge the Internet of Things as a real business opportunity, and estimates currently suggest that the IoT could grow into a market worth $7.1 trillion by 2020 (IDC 2014). While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses. The origins of the term date back more than 15 years and have been attributed to the work of the Auto-ID Labs at the Massachusetts Institute of Technology (MIT) on networked radio-frequency identification (RFID) infrastructures (Atzori et al. 2010; Mattern and Floerkemeier 2010). Since then, visions for the Internet of Things have been further developed and extended beyond the scope of RFID technologies. The International Telecommunication Union (ITU) for instance now defines the Internet of Things as ‘‘a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies’’ (ITU 2012). At the same time, a multitude of alternative definitions has been proposed. Some of these definitions exhibit an emphasis on the things which become connected in the IoT. Other definitions focus on Internet-related aspects of the IoT, such as Internet protocols and network technology. And a third type centers on semantic challenges in the IoT relating to, e.g., the storage, search and organization of large volumes of information (Atzori et al. 2010). The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday. The most prominent areas of application include, e.g., the smart industry, where the development of intelligent production systems and connected production sites is often discussed under the heading of Industry 4.0. In the smart home or building area, intelligent thermostats and security systems are receiving a lot of attention, while smart energy applications focus on smart electricity, gas and water meters. Smart transport solutions include, e.g., vehicle fleet tracking and mobile ticketing, while in the smart health area, topics such as patients’ surveillance and chronic disease management are being addressed. And in the context of Accepted after one revision by Prof. Dr. Sinz.

Multilevel Modeling: Toward a New Paradigm of Conceptual Modeling and Information Systems Design

Abstract: Domain-specific modeling languages (DSMLs) promise clear advantages over general-purpose modeling languages. However, their design poses a fundamental challenge. While economies of scale advocate the development of DSMLs that can be used in a wide range of cases, modeling productivity demands more specific language concepts tuned to individual requirements. Inspired by the actual use of technical languages (German: “Fachsprachen”), this paper presents a novel multilevel modeling approach to conceptual modeling and to the design of information systems. Unlike traditional language architectures such as Meta Object Facility (MOF), it features a recursive architecture that allows for an arbitrary number of classification levels and, hence, for the design of hierarchies of DSMLs ranging from reference DSMLs to “local” DSMLs. It can not only diminish the conflict inherent in designing DSMLs, but enables the reuse and integration of software artifacts in general. It also helps reduce modeling complexity by relaxing the rigid dichotomy between specialization and instantiation. Furthermore, it integrates a meta-modeling language with a metamodel of a reflective meta-programming language, thereby allowing for executable models. The specification of the language architecture is supplemented by the description of use scenarios that illustrate the potential of multilevel modeling and a critical discussion of its peculiarities.

Gamification - Design of IT-Based Enhancing Services for Motivational Support and Behavioral Change

Abstract: NikeFuel is the fuel of the Nike+ community. A fuel that has made two million users burn more than 68 bn. calories and that proliferates with each kilometer. The athletic performance of Nike+ users is measured via sensors in Nike sports shoes and an Apple iPod or iPhone, documented on the Nike+platform and converted into NikeFuel. In doing so, users may visualize their progress, compare their performance with others, and obtain different status levels that reflect their athletic potential. This approach derives from the domain of game design and is called gamification enriching products, services, and information systems with game-design elements in order to positively influence motivation, productivity, and behavior of users. In the consumer sector, various successful examples for gamication are gaining recognition. Gamication is a persuasive technology that attempts to influence user behavior by activating individual motives via game-design elements. As a consequence, this approach does not deal with designing games that can generally be defined as solving rule-based artificial conflicts or simulations. Thus, gamication needs to be contrasted to related concepts such as serious games and games with a purpose.

Business models: An information systems research agenda

Abstract: The business model concept, although a relatively new topic for research, has garnered growing attention over the past decade. Whilst it has been robustly defined, the concept has so far attracted very little substantive research. In the context of the wide-spread digitization of businesses and society at large, the logic inherent in a business model has become critical for business success and, hence, a focus for academic inquiry. The business model concept is identified as the missing link between business strategy, processes, and Information Technology (IT). The authors argue that the BISE community offers distinct and unique competencies (e.g., translating business strategies into IT systems, managing business and IT processes, etc.) that can be harnessed for significant research contributions to this field. Within this research gap three distinct streams are delineated, namely, business models in IT industries, IT enabled or digital business models, and IT support for developing and managing business models. For these streams, the current state of the art, suggest critical research questions, and suitable research methodologies are outlined.

Big Data : A Fashionable Topic with(out) Sustainable Relevance for Research and Practice?

Abstract: When looking at the words of Hal Varian, Google’s Chief Economist and professor emeritus at the University of California, Berkeley, thinking of Big Data seems natural. Big Data – a dictum which currently seems to be on everyone’s lips – has recently developed into one of the most discussed topics in research and practice. Looking at academic publications, we find that more than 70 % of all ranked papers which deal with Big Data were published within the last two years (Pospiech and Felden 2012) as well as nearly 12,000 hits for Big Data on GoogleScholar across various fields of research. In 2011, more than 530 academic Big Data related publications could be counted (Chen et al. 2012). We find more hits for “Big Data” than for “Development aid” in Google, and almost daily an IT-related business magazine publishes a Big Data special issue next to a myriad of Big Data business conferences. In Gartner’s current Hype Cycle for Emerging Technologies (Gartner 2012), Big Data is right on the peak of its hype phase, and according to this source a broad adoption is to be expected within the next five years. Big Data provokes excitement across various fields such as science, governments, and industries like media and telecommunications, health care engineering, or finance where organizations are facing a massive quantity of data and new technologies to store, process, and analyze those data. Despite the cherished expectations and hopes, the question is why we face such excitement around Big Data which at first view rather seems to be a fashionable hype than a revolutionary concept. Is Big Data really something new or is it just new wine in old bottles seeing that, e.g., data analytics is doing the same type of analysis since decades? Do more data, increased or faster analytics always imply better decisions, products, or services, or is Big Data just another buzzword to stimulate the IT providers’ sales? Taking the traditional financial service industry, which currently cherishes huge expectations in Big Data, as an example, the collection of massive amounts of data via multiple channels for a long time was part of the business model to customize prices, product offers, or to calculate credit ratings. However, improving financial services by exploiting these huge amounts of data implied constant updating efforts, media disruptions and expensive acquisition and processing of data. Hence, more data resulted in expensive data management, in higher prices for products or services as well as in inconvenient processes regarding the customers’ data entry. Hence, instead of the traditional universal banks that focused on a data-intensive business model, direct banks with a higher grade of standardization and IT support as well as a focus on (very few) key customer data often enough have become more successful. Focusing solely on pure IT-based data acquisition, processing and analysis to save costs on the other side is virtually impossible in industries such as banking due to an intense personal contact. Besides, neither in the financial service industry nor in other industries do more data automatically lead to better data, better business success, better services, better decisions, or (more) satisfied customers. Above all, Big Data brings a lot of still unresolved challenges regarding the volume, velocity, variety, and veracity of data, which should not be underestimated. Often enough, more data even lead to a certain amount of “data garbage” which usually is more easily and better recognized and managed by employees rather than by analytics software (veracity). Additionally, the management of various sources of data such as from, e.g., mobile applications, online social networks, or CRM systems is far from trivial (variety). The high data traffic brings along the challenge of archiving, retrieving, and analyzing huge amounts of data in real-time (volume and velocity). Unsurprisingly, nearly every second Big Data project is canceled before completion (Infochimps 2013). And as if these challenges were not enough, we additionally see a myriad of different legal privacy restrictions in different countries turning into one of Big Data’s most serious challenges.