In the digital age, organizations face the challenge of adapting IS sourcing practices to numerous major changes (Dibbern et al. 2020). First, digital technologies increasingly permeate the processes, products, and services of companies (Venkatraman 2017). These include IS services and products offered by a vibrant and increasingly complex ecosystem of providers such as consulting companies, standard software providers, specialized development firms, and digital platforms. Second, the digital transformation entails a number of concurrent technological shifts such as the rise of AI and new architectural paradigms (e.g., microservices, low-code platforms, and serverless computing) that fundamentally change the nature of the task that is being sourced. Examples include relying on intelligent software agents rather than human actors (Rutschi and Dibbern 2020; Willcocks et al. 2016), reconfigure firm boundaries, and add further complexity to the already confusing number of alternative sourcing arrangements that include multi-sourcing (Oshri et al. 2019), cloud-services (Hoffmann et al. 2020; Gozman and Willcocks 2018) and governance mechanisms (Benaroch et al. 2016; Gregory et al. 2013; Huber et al. 2013; Kotlarsky et al. 2020; Wiener et al. 2016). Furthermore, with the growing popularity of data-driven business models issues associated with data sourcing are becoming more prevalent (Wiener et al. 2020). Perhaps even more drastic changes lie ahead in the outsourcing of information services, amidst emerging technologies such as “big data,” blockchains, social media, cloud computing, and artificial intelligence (Sabherwal 2020). To respond to these changes, sourcing professionals will have to adapt their decision and governance practices—offering unique opportunities for researchers to advance understanding of the evolution and socio-technical underpinnings of sourcing practices (Sarker et al. 2019).

Track Chairs
Dorit Nevo, Rensselaer Polytechnic Institute,
Julia Kotlarsky, The University of Auckland,
Rajiv Sabherwal, University of Arkansas,

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Thursday, August 10th
12:00 AM

A Meta-Analysis of Funder’s intention on Crowdfunding Platforms

Sandeep Goyal, The Institute for Competitiveness
Sumedha Chauhan, Jindal Global Business School, O.P. Jindal Global University, Sonipat, India
Parul Gupta, Management Development Institute
Luvai F. Motiwalla, U MASS Lowell

12:00 AM

Selecting the Optimal Number of Crowd Workers for Forecasting Tasks

Arthur Carvalho, Miami University
Majid Karimi, California State University San Marcos

12:00 AM

Socialize Less Pay More: The Link Between Virtual Network Embeddedness and User Contributions

Kanghyun cho, Temple University
Kihwan Nam, Dongguk University

12:00 AM