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Researching the field of business intelligence and analytics (BI & A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI & A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI & A research of the current decade, focusing on the third wave of BI & A. By this means, the paper’s contribution is fourfold. First, a systematically developed taxonomy for BI & A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI & A 3.0. Third, the results foster discussions on the predicted and observed developments in BI & A research of the past decade. Fourth, research gaps of the third wave of BI & A research are disclosed and concluded in a research agenda.
In the past decade, many IS researchers focused on researching the phenomenon of Big Data. At the same time, the relevance of data protection gets more attention than ever before. In particular, since the enactment of the European General Data Protection Regulation in May 2018 Information Systems research should provide answers for protecting personal data. The article at hand presents a structuring framework for Big Data research outcome and the consideration of data protection. IS Researchers might use the framework in order to structure Big Data literature and to identify research gaps that should be addressed in the future.
Names of individuals
(2017)
Small Claims Regulation
(2017)
Given the strong increase in regulatory requirements for business processes the management of business process compliance becomes a more and more regarded field in IS research. Several methods have been developed to support compliance checking of conceptual models. However, their focus on distinct modeling languages and mostly linear (i.e., predecessor-successor related) compliance rules may hinder widespread adoption and application in practice. Furthermore, hardly any of them has been evaluated in a real-world setting. We address this issue by applying a generic pattern matching approach for conceptual models to business process compliance checking in the financial sector. It consists of a model query language, a search algorithm and a corresponding modelling tool prototype. It is (1) applicable for all graph-based conceptual modeling languages and (2) for different kinds of compliance rules. Furthermore, based on an applicability check, we (3) evaluate the approach in a financial industry project setting against its relevance for decision support of audit and compliance management tasks.
Divided government is often thought of as causing legislative deadlock. I investigate the link between divided government and economic reforms using a novel data set on welfare reforms in US states between 1978 and 2010. Panel data regressions show that, under divided government, a US state is around 25% more likely to adopt a welfare reform than under unified government. Several robustness checks confirm this counter-intuitive finding. Case study evidence suggests an explanation based on policy competition between governor, senate, and house.
We introduce a new way to measure the forecast effort that analysts devote to their earnings forecasts by measuring the analyst's general effort for all covered firms. While the commonly applied effort measure is based on analyst behaviour for one firm, our measure considers analyst behaviour for all covered firms. Our general effort measure captures additional information about analyst effort and thus can identify accurate forecasts. We emphasise the importance of investigating analyst behaviour in a larger context and argue that analysts who generally devote substantial forecast effort are also likely to devote substantial effort to a specific firm, even if this effort might not be captured by a firm-specific measure. Empirical results reveal that analysts who devote higher general forecast effort issue more accurate forecasts. Additional investigations show that analysts' career prospects improve with higher general forecast effort. Our measure improves on existing methods as it has higher explanatory power regarding differences in forecast accuracy than the commonly applied effort measure. Additionally, it can address research questions that cannot be examined with a firm-specific measure. It provides a simple but comprehensive way to identify accurate analysts.
Knowledge-based productivity in “low-tech” industries: evidence from firms in developing countries
(2014)
Using firm-level data from five developing countries—Brazil, Ecuador, South Africa, Tanzania, and Bangladesh—and three industries—food processing, textiles, and the garments and leather products—this article examines the importance of various sources of knowledge for explaining productivity and formally tests whether sector- or country-specific characteristics dominate these relationships. Knowledge sources driving productivity appear mainly sector specific. Also differences in the level of development affect the effectiveness of knowledge sources. In the food processing sector, firms with higher educated managers are more productive, and in least-developed countries, additionally those with technology licenses and imported machinery and equipment. In the capital-intensive textiles sector, productivity is higher in firms that conduct R&D. In the garments and leather products sector, higher education of the managers, licensing, and R&D raise productivity.
This paper investigates the extent to which corporate governance affects the cost of debt and equity capital of German exchange-listed companies. I examine corporate governance along three dimensions: financial information quality, ownership structure and board structure. The results suggest that firms with high levels of financial transparency and bonus compensations face lower cost of equity. In addition, block ownership is negatively related to firms' cost of equity when the blockholders are other firms, managers or founding-family members. Consistent with the conjecture that agency costs increase with firm size, I find significant cost of debt effects only in the largest German companies. Here, the creditors demand lower cost of debt from firms with block ownerships held by corporations or banks. My findings demonstrate that a uniform set of governance attributes is unlikely to satisfy suppliers of debt and equity capital equally.