Co-Diffusion Effects in Software Sourcing Arrangements

Introduction

Software sourcing is a multi-faceted phenomenon that enables enterprises to obtain software solutions in a cost-effective manner (Naik, 2016). Software sourcing has evolved from developing software within a country and a within a firm through insourcing and onshoring to innovative sourcing models such as outsourcing and offshoring (Dayyala, et al., 2017). These sourcing models can be broadly classified based on geography: onshoring and offshoring and based on the extent of externalization: insource and outsource (Tate & Bals, 2017). Onshoring is when software development activity is performed in a domestic country and offshoring is when software development activity is performed in a foreign country. While the insourcing can be conceptualized as the allocation or reallocation of resources internally within an organization to develop software, outsourcing can be defined as allocating or reallocating the software development activities internal to an organization, to an external source (Schniederjans, et al., 2015). Several other sourcing options have emerged based on the combination of geography and externalization of software development activities as shown in Figure 1 (Contractor, et al., 2010; Foerstl, et al., 2016). Each software sourcing strategy has its own adoption pattern over time depending on the benefits and drawbacks as perceived by the adopters. Angst, et al. (2017) note that very little is known about software sourcing trends and the rationale for pursuing one sourcing strategy over the other. Inconsistencies in adoption trends can be better understood with diffusion studies that incorporate temporal effects (Manning, et al., 2018). Specifically, diffusion studies explain the channels through which an innovation spreads over time (Rogers, 2010). Bucklin and Sengupta (2003) note that the introduction and adoption of innovations may not only deliver the envisioned benefits but might affect the diffusion of other existing or potential innovation adoptions due to interactions between them. The authors called this phenomenon “the co-diffusion effect”. Given the co-existence of several sourcing phenomena we expect that the innovative sourcing strategies may exert co-diffusive interactions on the adoption patterns of traditional sourcing strategies impacting the adoption pattern over time. This study analyzes the diffusion and co-diffusion effects between 1) onshoring and offshoring and 2) insourcing and outsourcing.

Figure 1.

Software sourcing models

Software sourcing research is well documented in the literature, with researchers performing several cross-sectional studies to understand the motivation of adoptions, sourcing decisions, outcomes, and risks (Liang, et al., 2016). However, the study of the diffusion of software sourcing analyzing the spread of software sourcing over time is limited (Lewin & Volberda, 2011; Mann, et al., 2015, Angst, et al. 2017). Scholars indicate the need for Information Systems (IS) sourcing research to move beyond snapshot studies and incorporate temporal effects to understand the evolution and development of software sourcing options (Alsudairi & Dwivedi, 2010; Madsen, 2017; Manning, et. al., 2018). Despite the call for diffusion studies in IS literature, there are very few existing studies that study the time-based spread of the software sourcing phenomenon. Some noteworthy studies that applied innovation diffusion models to understand the temporal spread of software sourcing are done by Chakrabarty (2006); Chaudhury & Bharati, (2008); Hu, Saunders, & Gebelt (1997); Loh & Venkatraman (1992); Mann, et al., (2011); and Angst, et al., (2017). These studies analyzed the factors influencing the adoption of information systems outsourcing by identifying it as an innovation. Nevertheless, the application of diffusion studies to analyze the co-diffusive interactions between sourcing options do not exist.