DO WEB SERVICES FOSTER SPECIALIZATION? AN ANALYSIS OF WEB SERVICE DIRECTORIES

Web service technologies are expected to foster the creation of networks of specialists which expose their digital services over the internet for the dynamic discovery of services by other organizations. Although the idea of a global Web service directory, which was considered a key enabler of e-commerce in the dot.com era, has failed with the shutdown of the Universal Business Registry in 2006, the vision of an open market for Web services has regained popularity lately in the context of the SOA and Web 2.0 concepts. Given these latest developments, the interesting question is whether there is empirical evidence of an emerging market for Web services. Based on a longitudinal study of Web services directories, this paper aims at analyzing the evolving offering and market structure of B2B Web services. The study suggests that commercial Web services which enable companies to out-task discrete, repetitive tasks to specialized service providers continue to be relatively scarce. However, Web services specialists emerge in specific domains, such as compliance, online validation and alerting. In addition, Web services directories are extending their scope beyond service discovery and evolve into either ‘real’ electronic marketplaces or infomediaries

Understanding the manifold forms of B2B integration - A transaction cost perspective

The need for collaboration within value chains is rapidly increasing and drives enterprise to align and electronically integrate their business processes with their business partners. As technologies evolve, manifold forms of B2B integration have emerged – from e-mail communication to customer or supplier portals, the exchange of EDIFACT- to XML documents, and Web Services. Although serviceoriented architectures (SOA) are considered the future of inter-organizational linkages, no empirical studies have been found which surveyed the impact of SOA on B2B integration costs and benefits. From a research perspective, we still lack a systematic analysis that explains how a specific B2B integration technology impacts the effectiveness of B2B integration. Building on transaction cost theory, this research analyzes the different forms of B2B integration with regard to their impact on connectivity and coordination costs. Based on a field study from the automotive industry, it demonstrates that there is economic rationale for preferring supplier portals to machine-to-machine integration based on EDIFACT or XML messages. Compared to prior technologies, SOA reduces the costs of external integration by eliminating separate B2B integration infrastructures and improving connectivity of internal applications. However, we find that prior literature tends to overestimate the impact of open Internet and Web service technologies on connectivity costs

Internal markets as a sourcing option for the delivery of IS services: Improving outsourcing and insourcing

The need for collaboration within value chains is rapidly increasing and drives enterprise to align and electronically integrate their business processes with their business partners. As technologies evolve, manifold forms of B2B integration have emerged – from e-mail communication to customer or supplier portals, the exchange of EDIFACT- to XML documents, and Web Services. Although serviceoriented architectures (SOA) are considered the future of inter-organizational linkages, no empirical studies have been found which surveyed the impact of SOA on B2B integration costs and benefits. From a research perspective, we still lack a systematic analysis that explains how a specific B2B integration technology impacts the effectiveness of B2B integration. Building on transaction cost theory, this research analyzes the different forms of B2B integration with regard to their impact on connectivity and coordination costs. Based on a field study from the automotive industry, it demonstrates that there is economic rationale for preferring supplier portals to machine-to-machine integration based on EDIFACT or XML messages. Compared to prior technologies, SOA reduces the costs of external integration by eliminating separate B2B integration infrastructures and improving connectivity of internal applications. However, we find that prior literature tends to overestimate the impact of open Internet and Web service technologies on connectivity costs

Building data management capabilities to address data protection regulations: Learnings from EU-GDPR

The European Union’s General Data Protection Regulation (EU-GDPR) has initiated a paradigm shift in data protection toward greater choice and sovereignty for individuals and more accountability for organizations. Its strict rules have inspired data protection regulations in other parts of the world. However, many organizations are facing difficulty complying with the EU-GDPR: these new types of data protection regulations cannot be addressed by an adaptation of contractual frameworks, but require a fundamental reconceptualization of how companies store and process personal data on an enterprise-wide level. In this paper, we introduce the resource-based view as a theoretical lens to explain the lengthy trajectories towards compliance and argue that these regulations require companies to build dedicated, enterprise-wide data management capabilities. Following a design science research approach, we propose a theoretically and empirically grounded capability model for the EU-GDPR that integrates the interpretation of legal texts, findings from EU-GDPR-related publications, and practical insights from focus groups with experts from 22 companies and four EU-GDPR projects. Our study advances interdisciplinary research at the intersection between IS and law: First, the proposed capability model adds to the regulatory compliance management literature by connecting abstract compliance requirements to three groups of capabilities and the resources required for their implementation, and second, it provides an enterprise-wide perspective that integrates and extends the fragmented body of research on EU-GDPR. Practitioners may use the capability model to assess their current status and set up systematic approaches toward compliance with an increasing number of data protection regulations

Leveraging Market Research Techniques in IS – A Review of Conjoint Analysis in IS Research

With the increasing importance of mass-market information systems (IS), understanding individual user preferences for IS design and adoption is essential. However, this has been a challenging task due to the complexity of balancing functional, non-functional, and economic requirements. Conjoint analysis (CA), a marketing research technique, estimates user preferences by measuring tradeoffs between products attributes. Although the number of studies applying CA in IS has increased in the past years, we still lack fundamental discussion on its use in our discipline. We review the existing CA studies in IS with regard to the application areas and methodological choices along the CA procedure. Based on this review, we develop a reference framework for application areas in IS that serves as foundation for future studies. We argue that CA can be leveraged in requirements management, business model design, and systems evaluation. As future research opportunities, we see domain-specific adaptations e.g., user preference models

Who Owns Data In The Enterprise? Rethinking Data Ownership in Time of Big Data and Analytics

Today, a myriad of data is generated via connected devices and digital applications. With recent ad- vances in artificial intelligence (AI), companies are seeking new opportunities to monetize data. This goes along with improving their capabilities to manage big data and analytics (BDA). A critical factor that is often cited concerning the ‘soft’ aspects of BDA is data ownership, i.e. clarifying the funda- mental rights and responsibilities for data. Scholars have investigated data ownership for operational systems and data warehouses, where the purpose of data processing is known. In the BDA context, defining accountabilities for data ownership is more challenging, because data is stored in data lakes and used for new, previously unknown purposes. Based on insights from three case studies with exten- sive experience in BDA, we identify ownership principles and three data ownership types: data, data platform, and data product. By redefining the concept of data ownership, our research answers fun- damental questions about how data management changes with BDA, extending existing concepts on data ownership and contributing to the data governance literature

Introducing a Data Perspective to Sustainability: How Companies Develop Data Sourcing Practices for Sustainability Initiatives

Many companies use the UN Sustainable Development Goals as a point of reference for their sustainability initiatives and actions. Reporting on these goals requires collecting, processing, and interpreting substantial amounts of data (e.g., on emissions or recycled materials) that were previously neither captured nor analyzed. Although prior studies have occasionally highlighted the issues of data availability, data access, and data quality, a research void prevails on the data perspective in the sustainability context. This article aims at developing this perspective by shedding light on data sourcing practices for the reliable reporting of sustainability initiatives and goals. We make a two-fold contribution to sustainability and Green IS research: First, as a theoretical contribution, we propose a framework based on institutional theory to explain how companies develop their data sourcing practices in response to regulatory, normative, and cultural-cognitive pressures. Second, our empirical contributions include insights into five case studies that represent key initiatives in the field of environmental sustainability that touch on, first, understanding the ecological footprint, and, second, obtaining labels or complying with regulations, both on product and packaging levels. Based on five case studies, we identify three data sourcing practices: sense-making, data collection, and data reconciliation. Thereby, our research lays the foundation for an academic conceptualization of data sourcing in the context of sustainability

A Method to Screen, Assess, and Prepare Open Data for Use

Open data's value-creating capabilities and innovation potential are widely recognized, resulting in a notable increase in the number of published open data sources. A crucial challenge for companies intending to leverage open data is to identify suitable open datasets that support specific business scenarios and prepare these datasets for use. Researchers have developed several open data assessment techniques, but those are restricted in scope, do not consider the use context, and are not embedded in the complete set of activities required for open data consumption in enterprises. Therefore, our research aims to develop prescriptive knowledge in the form of a meaningful method to screen, assess, and prepare open data for use in an enterprise setting. Our findings complement existing open data assessment techniques by providing methodological guidance to prepare open data of uncertain quality for use in a value-adding and demand-oriented manner, enabled by knowledge graphs and linked data concepts. From an academic perspective, our research conceptualizes open data preparation as a purposeful and value-creating process

Decoding Data Products through the Lens of Work System Theory

Data products are seen as important levers in repurposing the large volumes of data that are captured in organizations and in satisfying the increasing demand for analytics. Despite the increasing popularity of the data product concept, it remains unclear how data can be productized. This article proposes work system theory to study the implications data products have for how organizations manage their data. Adopting a multimethod approach involving case studies and focus groups, we identify three types of data products and analyze how organizations coordinate their resources to build work systems around each data product type. Our findings contribute to the ongoing discourse on scaling data and analytics capabilities to repurpose and consume data effectively