Business Information Systems

High-frequency traders (HFTs) account for a considerable component of equity trading but we know little about the source of their trading profits and how those are affected by such attributes as ultra-low latency or news processing power. Given a fairly modest amount of empirical evidence on the subject, we study the relation between the computational speed and HFTs’ profits through an experimental artificial agent-based equity market. Our approach relies on an ecological modelling inspired from the r/K selection theory, and is designed to assess the relative financial performance of two classes of aggressive HFT agents endowed with dissimilar computational capabilities. We use a discrete-event news simulation system to capture the information processing disparity and order transfer delay, and simulate the dynamics of the market at a millisecond level. Through Monte Carlo simulation we obtain in our empirical setting robust estimates of the expected outcome.

The paper presents a methodology for identification, assessment and selection of internet data sources that shall be used to supplement existing internal data in a continuous manner. Several criteria are specified to help in the selection process. The proposed method is described based on an example of the system for the maritime surveillance purposes, originally developed within the SIMMO research project. As a result, we also present a ranking of concrete data sources. The presented methodology is universal and can be applied to other domains, where internet sources can offer additional data.

In today’s increasingly digital business ecosystem, big data offers numerous opportunities. Although research on big data receives a lot of attention, research on the business value of big data is scarce. The research project presented in this article aims at advancing the research in this area, focusing on the identification of opportunities towards determining the business value of big data. The goal of the research project pursued is to develop a framework that supports decision makers to identify opportunities for attaining business value form big data in the financial services industry. The proposed framework was constructed based on information collected by performing an in-depth literature review and interviews with experts in the area of big data and financial services industry, and it was empirically validated via a questionnaire sent to experts. A comparative analysis was also performed, emphasizing the strengths of the proposed framework over existing approaches.

Recommender systems help consumers to find products online. But because many content-based systems work with insufficient data, recent research has focused on enhancing item feature information with data from the Linked Open Data cloud. Linked Data recommender systems are usually bound to a predefined set of item features and offer limited opportunities to tune the recommendation model to individual needs. The paper addresses this research gap by introducing the prototype SKOS Recommender (SKOSRec), which produces scalable on-the-fly recommendations through SPARQL-like queries from Linked Data repositories. The SKOSRec query language enables users to obtain constraint-based, aggregation-based and cross-domain recommendations, such that results can be adapted to specific business or customer requirements.

Sustainable use of energy is one of the guiding principles of today’s society. But there is a lack of comprehensive analysis solutions for the energy consumption of private households to provide real insights. In order to provide useful information, feedback systems may be the answer. Numerous studies about feedback systems have been conducted so far and each individual component of such a system has been tested. The combination of these components leads to a dashboard for decision support of private households. Within this study the individual components were combined in several configurations and implemented as a prototype dashboard. A Conjoint measurement is used for evaluation and observation of user preferences collected in over 1,000 questionnaires. The result, an evaluated dashboard, combines several effective feedback elements based on user preferences and helps to save energy based on decision support and transparency.

Fraud is a widespread international problem for enterprises. Organizations increasingly use self-learning classifiers to detect fraud. Such classifiers need training data to successfully distinguish normal from fraudulent behavior. However, data containing authentic fraud scenarios is often not available for researchers. Therefore, we have implemented a data generation tool, which simulates fraudulent and non-fraudulent user behavior within the purchase-to-pay business process of an ERP system. We identified fraud scenarios from literature and implemented them as automated routines using SAP’s programming language ABAP. The data generated can be used to train fraud detection classifiers as well as to benchmark existing ones.

In the paper we investigate new approaches to quantitative art market research, such as statistical analysis and building of market indices. An ontology has been designed to describe art market data in a unified way. To ensure the quality of information in the knowledge base of the ontology, data enrichment techniques such as named entity recognition (NER) or data linking are also involved. By using techniques from computer vision and machine learning, we predict a style of a painting. This paper comes with a case study example being a detailed validation of our approach.

“Search engine (optimization) visibility indices” or also called “SEO visibility indices” are a widespread and important key performance indicator in the SEO-Communities. SEO visibility indices show the overall visibility of a website regarding the search engine result page (SERP). Although search engine visibility indices are widespread as an important KPI, they are highly controversial regarding the aspect of a correlation between real website visitor traffic and search engine visibility indices. Furthermore, only a few online-publications examine this controversial aspect. Therefore, we designed a study, analyzing the correlation between organic visitor traffic and search engine visibility indices, the correlation amongst the indices themselves and the impact of Google Updates on the indices. The study is based on 32 websites of German enterprises from various business branches. Key findings imply that there is no high correlation between organic visitor traffic and search engine visibility indices, but a high correlation between the indices themselves. Furthermore, there is no identifiable pattern relating to the expected effect that Google Updates influence the search engine visibility indices.

The evaluation of credit applications is among processes that should be conducted in an efficient manner in order to prevent incorrect decisions that may lead to a loss even for the bank or for the credit applicant. Several approaches have been proposed in this context in order to ensure the enhancement of the credit evaluation process by using various artificial intelligence approaches. Even if the proposed schemes have shown their efficiency, the provided decision regarding a credit is not correct in most cases due to the lack of information for a provided criteria, incorrect defined weights for credit criteria, and a missing information regarding a credit applicant. In this paper, we propose a hybrid neural network that ensures the enhancement of the decision for credit applicants data based on a credit scoring by considering the big data related to the context associated to credit criterion which is collected through a period of time. The proposed model ensures the evaluation of credit by using a set of collectors that are deployed through interconnected networks. The efficiency of the proposed model is illustrated through a conducted simulation based on a set of credit applicant’s data.

The paper presents an innovative architecture for push-based Context-aware Recommendation Systems (CARS) that integrates different description and reasoning approaches. Complex Event Processing (CEP) is applied on live data to provide situation awareness. Ontologies and semantic rules are used to define domain expertise that allow individualized and domain-specific recommendations. A case study of a museum serves as a proof of concept of the approach.

The main goal of this paper is the development of a platform which can insure the effectiveness and the simplification of the loan granting process performed by financial credit institutions and banks oriented to small and medium enterprises. The factors considered include employee’s education, experience, philosophy, self-beliefs and self-understanding of the bank’s target and values and his self-commitment to the bank’s objectives. This paper proposes a platform which implements a statistical model, containing financial indicators. The model is flexible, being able to include, besides financial indicators, some emotional ones, considered as model corrections pertaining to the decision maker. The latter indicators are important in borderline decisions. Our platform has been validated on samples containing financial data for Romanian small and medium sized enterprises.

In this paper, we present an empirical study on the factors that influence companies in their decision regarding the adoption of Cloud Computing services. While this issue has been subject to a number of studies in the past, most of these approaches lack in the application of quantitative empirical methods or the appreciation of the inherent risk. With our study we focus on the factors that promote and inhibit the adoption of cloud services with a particular consideration of application risks. Our findings show that decision makers are significantly influenced by the risk of data loss in the first place but also by the risk that comes along with the service provider or technical issues that might occur during the use of the service. On the other side, the attractiveness could be identified as an important driver for the adoption of cloud services.

The proposition is connected with the research of the security or threats referring to message decryption, user dishonesty, a non-fresh nonce, uncontrolled information jurisdiction, etc. (that means security properties - attributes), in network communication processes [3]. Encrypted messages are usually sent in the form of protocol operations. Protocols may be mutually interleaving, creating the so called runs, and their operations can appear as mutual parallel processes. The investigation regards both particular security attributes and their compositions referring to more general factors, such as: concrete users, protocols, public keys, secrets, messages, etc. Probabilistic timed automata (PTA) and Petri nets characterize the token set and the complex form of conditions which have to be fulfilled for the realization of transition [5]. The abovementioned situation forms a conception pertaining to the parallel strategy realized with the help of the Petri net that includes the set of security tokens (attributes) in each node.

In many industries, companies are currently testing and adopting internet of things (IoT) technology. By adopting IoT, they seek to improve efficiency or to develop and offer new services. In current projects, a variety of IoT systems is used and gets interconnected with existing or newly developed application systems. Due to the integration of IoT and the related cloud systems, existing enterprise architecture (EA) models have to be extended. By drawing on the example of the Hamburg smartPORT initiative, we analyze the consequences of IoT projects on the enterprise architecture. As a result, we present an EA meta-model extension, which includes (1) sensor, physical object, smart brick, and fog system types, (2) a smart brick management database and (3) data streams, cloud systems and service applications. Furthermore, we discuss implications regarding a to-be architecture.

The integration of online and offline channels is a key challenge for retailers pursuing an omni-channel strategy to improve consumer experience. The prevalence of smartphones offers an opportunity to connect the physical and digital world. Bluetooth Low Energy beacons are small devices, which send out a signal that can be detected by consumer’s smartphones to enable location-based services. However, there are very few documented cases of beacon usage in Germany, whereas they seem to have a much higher adoption in the US. In this paper, we investigate the challenges associated with the use of beacons in retail. Using a survey, we aim to understand the attitude towards beacons-based services from a sample of consumers in Germany.

Batch processing is a means to synchronize the execution of multiple process instances for certain activities to improve process performance. Current batch processing concepts for business processes focus only on single process models whereas in practice large process model repositories exist with repeating activities. In this paper, we introduce a concept to specify batch processing requirements in centrally given object life cycles, which describe allowed data manipulations in order to identify candidates for batch processing during run-time across multiple processes and propose them to the user. We evaluate the applicability of this concept by implementation for an open source BPM platform.

Implications of market and environmental changes have always influenced the industrial world. Combined with new technologies, the current changes are summarized under the term Industrie 4.0. Since the first announcement, Industrie 4.0 is one of the most discussed topics in research and industry. However, for companies in the industrial sector, it is a challenge to assess the implications of Industrie 4.0 for their organizations, and to decide whether and how to respond. Therefore, a methodology to transform an organization towards Industrie 4.0 is required. This paper provides a metamodel for the transformation of organizations towards Industrie 4.0 as well as the first technique of this method, a framework, to structure the implications of Industrie 4.0 and to identify Industrie 4.0 action fields as a first step towards Industrie 4.0 transformation. Furthermore, it provides an outlook how to implement the identified action fields systematically in existing process change management.

Companies in dynamic environments have to react to certain market events. Reactions can be short-term and influence the behavior of running process instances or they can be mid-term or long-term and cause the redesign of the process. In both situations, insights into the process flow are necessary and provided by Process Data Warehouse Systems. This paper proposes to derive the data warehouse structures from the meta model of the BPMN (Business Process Model and Notation), the actual de-facto standard of workflow languages. The resulting data structure is generic in order to be portable between application domains and to be stable in case of changing workflows.

Enterprise business process management is directly affected by how effectively it designs and coordinates decision making. To ensure optimal process executions, decision management should incorporate decision logic documentation and implementation. To achieve the separation of concerns principle, the OMG group proposes to use Decision Model and Notation (DMN) in combination with Business Process Model and Notation (BPMN). However, often in practice, decision logic is either explicitly encoded in process models through control flow structures, or it is implicitly contained in process execution logs. Our work proposes an approach of semi-automatic derivation of DMN decision models from process event logs with the help of decision tree classification. The approach is demonstrated by an example of a loan application in a bank.

The IT unit is not the only provider of information technology (IT) used in business processes. Aiming for increased work performance, many business workgroups autonomously implement IT resources not covered by their organizational IT service management. This is called shadow IT. Associated risks and inefficiencies challenge organizations. This study proposes design principles for a method to control identified shadow IT following action design research in four organizational settings. The procedure results in an allocation of task responsibilities between the business and the IT units following risk considerations and transaction cost economics. This contributes to governance research regarding business-located IT activities.

Multiagent (MA) organizations can be regarded as a functional part in business information systems, in which software agents negotiate conditions for participation in the organization. How the strategic behavior of self-interested agents and MA-Organizations affects the formation process, however, is still not known. This research is concerned with the specification of MA-Organizations in business information systems and the design of negotiation protocols for determining the agents participation conditions. We draw on mechanism design to model the participation decision of the agent and the organization as a bilateral trading game. In a simulation experiment we find that a rather simple manipulation scheme provides a suitable approximation for the equilibrium strategies employed by the agents.

An Enterprise Architecture (EA) provides a holistic view about the domains to support planning and management tasks. The creation can be made more efficient if present domain models and measures are integrated. But these sources often lack coordination and thus are rather isolated. The paper proposes an approach based on Semantic Web technologies to combine these sources. A key aspect is the indirect connection through bridging elements to reduce the effort to establish an EA. These elements and a small EA vocabulary are the basis for an integrated data pool being a “mash up” instead of a new data silo.

Artifact-centric initiatives have been used in business processes whose data management is complex, being the simple activity-centric workflow description inadequate. Several artifact-centric initiatives pursue the verification of the structural and data perspectives of the models, but unfortunately uncovering security aspects. Security has become a crucial priority from the business and customer perspectives, and a complete verification procedure should also fulfill it. We propose an extension of artifact-centric process models based on the Usage Control Model which introduces mechanisms to specify security policies. An automatic transformation is provided to enable the verification of enriched artifact-centric models using existing verification correctness algorithms.

A profitable and renowned business model no longer guarantees a strong position against competitors in the future, as disruptive technologies and new businesses gain momentum. The society, politics and industry consortia shape the market further and their claims are way beyond the economic interests of current industries, but inhere environmental and societal desires as well. This work takes the new balance of forces into account by illustrating the obstacles of a sustainable business model (SBM) innovation and demonstrating how to overcome them by making use of the toolset of an open innovation approach.

Enterprise architecture aligns business and information technology through the management of different elements and domains. Performing an integrated analysis of EA models using automated techniques is necessary when EA model representations grow in complexity, in order to support, for example, benchmarking of business processes or assessing compliance with requirements. Moreover, heterogeneity challenges arise from the frequent usage of multiple modelling languages, each based on a specific meta-model that cross-cuts distinct architectural domains. The motivation of this paper is, therefore, to investigate to what extent ontology matching techniques can be used as a means to improve the execution of automated analysis of EA model representations, based on the syntax, structure and semantic heterogeneities of these models. For that, we used AgreementMakerLight, an ontology matching system, to evaluate the matching of EA models based on the ArchiMate and BPMN languages.

Self-customization of services is an approach, where customers configure a service to their individual preferences by assistance of a system for self-customization. This paper concentrates on the self-customization of business services in a multi actor environment, where different service providers as part of a service network provide service-modules, which are selected and combined by the customer. Existing concepts as well as the exemplar of a service value adding system as given by a Fourth Party Logistics provider are used to define requirements on the functionality of a service self-customization system. The determined functionality is merged and presented in a model.

This paper proposes a risk-aware B2B service pricing approach. The approach characterizes relevant cost positions according to their quantity and adaptiveness towards changes in the quantities sold. Based on the achieved transparency about the cost structure, cost niveau and cost adaptiveness, the approach allows to configure a risk-aware pricing scheme with an arbitrary number of different price components. It also allows for several different pricing schemes and provides analysis functionality for comparing these schemes with respect to risk and return criteria. The approach contributes to the domain of service science, which historically has not been discussing risk-based pricing approach in-depth.

Prior research has provided a number of approaches for the specification and analysis of service processes. However, little is known about their level of maturity regarding considered dimensions and characteristics. The present study represents a first step towards filling this gap. Drawing upon recent formalizations and delineations of service, a model for assessing the maturity of service modeling and analysis tools is derived. As part of a systematic literature review, it is applied to a set of 47 service blueprinting techniques to determine their maturity. The study’s findings indicate a high level of maturity regarding control flow and resource integration for most of the identified approaches. However, there are several shortcomings with respect to the input and output dimensions of the service process. The study proposes a set of research questions to stimulate future research and address the identified shortcomings.

This paper provides an introduction to the topic of Enterprise Social Networks (ESN) and illustrates possible applications, potentials, and challenges for future research. It outlines an analysis of research papers containing a literature overview in the field of ESN. Subsequently, single relevant research papers are analysed and further research potentials derived therefrom. This yields seven promising areas for further research: (1) user behaviour; (2) effects of ESN usage; (3) management, leadership, and governance; (4) value assessment and success measurement; (5) cultural effects, (6) architecture and design of ESN; and (7) theories, research designs and methods. This paper characterises these areas and articulates further research directions.

Popularity in social networks is a significant indicator of social success in western societies. The social capital within social networks has become an important element of social status. Therefore, the paper investigates why some users on Facebook receive more likes than others. The authors created eight hypotheses to test the influence of determinants on the popularity on the social media platform Facebook. The results show that especially gender, age, written posts and uploaded pictures or videos as well as adding new friends influences the popularity on Facebook.

This study attempts to identify the persuasive design principles of car-related smartphone apps that assist users in driving or managing their vehicles. We developed a guideline by experts for evaluating persuasive design principles of car apps and recruited four evaluators who were trained to apply the guideline and given 35 car apps for evaluation. The value of Fleiss’ Kappa was 0.782, over the excellent criterion of 0.75, which means the inter-rater reliability of persuasive design guideline was reliable. We collected 697 car apps from Apple iTunes Store and Google Play and examined which design principles were implemented by these car apps. The result shows that nine persuasive design principles are found, such as reduction, trustworthiness, real-world feel, self-monitoring, personalization, reminder, suggestion, expertise, and verifiability. The results from this study would suggest some implications for car app developers and automakers to develop better car apps in the future.

The need for training employees in new skills in an organization generally arises due to the changing skill requirements coming from the introduction of new products, technology and customers. Efficient assignment of employees to trainings so that the overall training cost is minimized while considering the career goals of employees is a challenging problem and to the best of our knowledge there is no existing work in literature that solves this problem. This paper presents TRaining AssigNment Service (TRANS) that minimizes an organization’s overall training costs while assigning employees to trainings that match their learning ability and career goals. TRANS uses an ORGanization and Skills ontology (ORGS) to calculate the cost for training each available employee for a potential role taking into account constructivist learning theory. TRANS uses TRaining assIgnMent algorithm (TRIM), based on Hungarian method for bipartite matching, for assigning employees to trainings. In our experiments with real-world data, proposed allocation algorithm performs better than the existing strategy of the organization.

We investigate two different portfolio construction methods for two different sets of algorithmic trading strategies that trade global futures. The problem becomes complex if we consider the out-of-sample performance. The Comgen method blindly optimizes the Sharpe ratio, and Comsha does the same but gives priority to strategies that individually have the better Sharpe ratio. It has been shown in the past that high Sharpe ratio strategies tend to perform better in out-of-sample periods. As the benchmark method, we use an equally weighted (1/N, naïve) portfolio. The analysis is performed on two years of out-of-sample data using a walk forward approach in 24 independent periods. We use the mean reversion and trend following datasets consisting of 22,702 and 36,466 trading models (time series), respectively. We conclude that Comsha produces better results with trend-following methods, and Comsha performs the same as Comgen with other type of strategies.

Supply Chain Management aims at optimizing the flow of goods and services from the producer to the consumer. Closely interconnected enterprises that align their production, logistics and procurement with one another thus enjoy a competitive advantage in the market. To achieve a close alignment, an instant, robust and efficient information flow along the supply chain between and within enterprises is required. However, less efficient human communication is often used instead of automatic systems because of the great diversity of enterprise systems and models. This paper describes an approach and its implementation SCM Intelligence App, which enables the configuration of individual supply chains together with the execution of industry accepted performance metrics. Based on machine-processable supply chain data model (the SCORVoc RDF vocabulary implementing the SCOR standard) and W3C standardized protocols such as SPARQL, the approach represents an alternative to closed software systems, which lack support for inter-organizational supply chain analysis. Finally, we demonstrate the practicality of our approach using a prototypical implementation and a test scenario.