Taxonomy of Technological IT Outsourcing Risks: Support for Risk Identification and Quantification

The past decade has seen an increasing interest in IT outsourcing as it promises companies many economic benefits. In recent years, IT paradigms, such as Software-as-a-Service or Cloud Computing using third-party services, are increasingly adopted. Current studies show that IT security and data privacy are the dominant factors affecting the perceived risk of IT outsourcing. Therefore, we explicitly focus on determining the technological risks related to IT security and quality of service characteristics associated with IT outsourcing. We conducted an extensive literature review, and thoroughly document the process in order to reach high validity and reliability. 149 papers have been evaluated based on a review of the whole content and out of the finally relevant 68 papers, we extracted 757 risk items. Using a successive refinement approach, which involved reduction of similar items and iterative re-grouping, we establish a taxonomy with nine risk categories for the final 70 technological risk items. Moreover, we describe how the taxonomy can be used to support the first two phases of the IT risk management process: risk identification and quantification. Therefore, for each item, we give parameters relevant for using them in an existing mathematical risk quantification model

Big Data and Information Processing in Organizational Decision Processes - A Multiple Case Study

Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing mechanisms and their complementary relationship to data-centric mechanisms. Third, it demonstrates how information processing theory can be applied to assess the dynamics of mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed

The Role of Collaboration between Incumbent Firms and Start-ups on Customers\u27 Adoption of Digital Innovation

Due to growing hyper-competition, firms need to create digital innovation in order to remain competitive in the digital era. While start-ups are known as a major source of creativity because they use new technologies to develop digital innovations, incumbent firms are beginning to address the opportunities and challenges of digitalization. Against this backdrop, incumbent firms have become interested in collaborating with start-ups in order to create digital innovation in co-development and offer it to customers. However, insights into costumers\u27 subjective stance towards adoption regarding digital innovation that is marketed by incumbent firms and start-ups are absent in the existing research. In light of this, we have analyzed this field based on a qualitative study with 16 interviews with customers. With our results, we contribute to the literature and provide practitioners with valuable insights into how collaboration between incumbent firms and start-ups should be presented to customers of digital innovations

The Role of Collaboration between Incumbent Firms and Start-ups on Customers' Adoption of Digital Innovation

Due to growing hyper-competition, firms need to create digital innovation in order to remain competitive in the digital era. While start-ups are known as a major source of creativity because they use new technologies to develop digital innovations, incumbent firms are beginning to address the opportunities and challenges of digitalization. Against this backdrop, incumbent firms have become interested in collaborating with start-ups in order to create digital innovation in co-development and offer it to customers. However, insights into costumers' subjective stance towards adoption regarding digital innovation that is marketed by incumbent firms and start-ups are absent in the existing research. In light of this, we have analyzed this field based on a qualitative study with 16 interviews with customers. With our results, we contribute to the literature and provide practitioners with valuable insights into how collaboration between incumbent firms and start-ups should be presented to customers of digital innovations

THE IMPORTANCE OF GOVERNANCE STRUCTURES IN IT PROJECT PORTFOLIO MANAGEMENT

Although recently a lot of attention has been devoted to IT project portfolio management in theory as well as in practice, research in this area is particularly focused on approaches for project selection. Related tasks and especially the organizational environment in which IT project portfolio management is embedded are often excluded. This paper relates existing findings from the field of IT governance to the field of IT project portfolio management. Based on a qualitative study, different fields of activities in IT project portfolio management are identified. Furthermore, governance issues in IT project portfolio management are illustrated and a category schema for the assessment of governance structures in the different fields of activities is introduced. In contrast to existing publications in this field of research, which usually employ a maturity level perspective, the paper focuses on the advantages and disadvantages of centralized, decentralized and federal structures in different fields of activities of IT project portfolio management. The paper is intended to highlight why different degrees of centralization in IT project portfolio management can be observed in practice

Internet social networking - Distinguishing the phenomenon from its manifestations in web sites

The Service-Oriented Architecture (SOA) paradigm promises to facilitate the integration of software services provided by different vendors and thus enables users to benefit from Best-of-Breed solutions. In order to support software architects we present the Multilayer Standardization Problem (MSP) to analyze the trade-off between possibly enhanced utility versus higher assembling costs of Best-ofBreed SOA solutions. We implemented a software prototype to support decision makers during the data input and the subsequent analysis of the solution’s robustness. The MSP for the SOA-case is formulated as a linear 0–1 optimization model and extends the established Standardization Problem (SP) by modeling the user preferences and considering varying granularity as well as integration relationships in addition to communication relationships. These characteristics are common to numerous systems – thus the general MSP can serve as a basis for further research in this fiel

Quantifying Risks in Service Networks: Using Probability Distributions for the Evaluation of Optimal Security Levels

The increasing costs and frequency of security incidents require organizations to apply proper IT risk management. At the same time, the expanding usage of Service-oriented Architectures fosters software systems composed of cross-linked services. Therefore, it is important to develop risk management methods for these composite systems. In this paper, we present a straightforward model that can be used to quantify the risks related to service networks. Based on the probability distribution of the costs which are related to risks, it is possible to make proper investment choices using individual risk preferences. The attractiveness of investment alternatives and different levels of security can be measured with various characteristics like the expected value of the costs, the Value-at-Risk or more complex utility functions. Through performance evaluations we show that our model can be used to calculate the costs’ probability density function for large scale networks in a very efficient way. Furthermore, we demonstrate the application of the model and the algorithms with the help of a concrete application scenario. As a result, we improve IT risk management by proposing a model which supports decision makers in comparing alternative service scenarios and alternative security investments in order to find the optimal level of IT security

Pricing Strategies of Software Vendors

Due to the economic characteristics specific to the software industry, pricing concepts existing in other industries cannot be transferred without adaptation. Therefore, this article provides an overview of pricing models for software. In this context we discuss the six parameters formation of prices, structure of payment flow, assessment base, price discrimination, price bundling, and dynamic pricing strategies. Furthermore, we refer to recent software delivery models, such as Software as a service. The results are based on literature research and empirical studies

Business Analytics for Sales Pipeline Management in the Software Industry: A Machine Learning Perspective

This study proposes a model designed to help sales representatives in the software industry to manage the complex sales pipeline. By integrating business analytics in the form of machine learning into lead and opportunity management, data-driven qualification support reduces the high degree of arbitrariness caused by professional expertise and experiences. Through the case study of a software provider, we developed an artifact consisting of three models to map the end-to-end sales pipeline process using real business data from the company’s CRM system. The results show a superiority of the CatBoost and Random Forest algorithm over other supervised classifiers such as Support Vector Machine, XGBoost, and Decision Tree as the baseline. The study also reveals that the probability of either winning or losing a sales deal in the early lead stage is more difficult to predict than analyzing the lead and opportunity phases separately. Furthermore, an explanation functionality for individual predictions is provided