Products, Platforms, and Open Innovation: Three Essays on Technology Innovation

High technology industries, where IT artifacts are core to the business model of a firm, are marked by a high level of market competition and uncertainty. Firms within these industries are constantly evolving at a swift pace. Products and services developed in these industries have the shortest life cycle from product development to maturity, compared to those developed in other industries. According to a 2015 KPMG report, products and services in the high technology industry have an average maturity life cycle of 0.5 - 5 years, which is the shortest among all sectors (KPMG, 2015). Value generation and capture from these products and services must happen in a shorter duration compared to those from other industries. Imitation of products and services in these industries is also rampant, diminishing opportunities to generate value from innovative products and services. According to extant research, imitation among vendors in the IT sector is widespread, and firms mimic direct competitors in the introduction and withdrawal of products and services (Ruckman et al., 2015; Rhee et al., 2006). While the inherent nature of products developed in the IT industry and the associated incremental innovation leads to better performance gains, these gains erode quickly via imitation from firms competing in the same domain (Ethiraj et al., 2008). For many firms, these issues lead to a shift in their revenue generation model. Rather than appropriating the value from direct sales of products and services, firms have slowly started opting for innovation strategies that allow rent-seeking through opening up the business and revenue models of the firm. These strategies may include but are not limited to, adopting open standards for their products and services, establishing platform business models and engaging in open innovation. In this thesis, I assess these three innovation strategies and their value to a firm in terms of product and services and related value performance. In the first essay of this thesis, I start by examining the lifecycle of products in information technology-intensive firms, which is deemed to be shorter compared to other industries. I call these products complex assembled digital products (CADP). In the product innovation literature, the emergence of a dominant design configuration in a product category is seen as the start of a technological lifecycle that allows winners of the industry to appropriate long-term returns through incremental innovation. In the context of a complex assembled digital product, a dominant design will manifest itself as a single dominant design configuration or a narrow set of configurations that represent a majority of the products manufactured in a product category (Tushman & Murmann, 1998; Cecere et al., 2015). However, in technology-intensive firms, two challenges need further exploration. Firstly, due to the pace of innovation in technology-intensive industries, it is highly likely that a dominant design configuration never emerges (Srinivasan et al., 2006). Secondly, due to the modular nature of the products, even if a dominant design is achieved, it is achieved at the configurational level. It manifests itself as the set of components that achieves dominance in a product configuration (Murmann & Frenken, 2006). In the first essay, I examine the evolutionary attributes of the components of a CADP, which enable the components to become and remain part of the dominant design configuration of the product for a longer duration. I model the entry and survival of a component in a dominant design configuration using three evolutionary attributes: (1) pleiotropy of the component, (2) openness of the standard supporting the component, and (3) innovation source of the component. Pleiotropy as a construct is adapted from evolutionary biology and defined as the number of functionalities supported by a component. The standard supporting a component can be open or proprietary. The innovation source can be internal to the industry or external. I empirically test my hypotheses using a rich, longitudinal dataset of TV models spanning 15 years (2002-2016). The results show that components that have higher pleiotropy and that are supported by open standards not only have a higher chance of being selected into the dominant design configuration of TVs but also remain in the TV market for a longer time. However, while components developed through endogenous innovation efforts were nearly four times more likely to enter the dominant design configuration of TVs, their longevity was not significantly different from that of the components sourced exogenously. In the first essay, I look at how adopting components with specific sets of attributes allows firms to win a product market and appropriate value for a long duration from product development. In the second essay, I shift my focus from a product-based business model to a platform business model as an innovation strategy to achieve a competitive advantage. In recent years we have observed the emergence of platform businesses across domains of information technology-intensive industries (van Alystyne and Parker 2016). Firms are either completely shifting to platform business models or starting to include platform business models as part of their business strategy portfolios. Newer firms in these industries are more likely to adopt a platform business model as the core model for value generation and value capture. Seven of the ten most valuable companies in the world have opted for a platform business model as part of their overall business strategy (Cusumano et al., 2019). However, not all firms adopting the platform business model succeed in dominating the market. An exploratory study examined the success of platform businesses in terms of the number of years the firm remained in business. Taking a 20 years dataset of the firms in US markets, it was observed that only 43 out of 252 platform firms flourished are still active (Yoffie et al., 2019). Most of the surviving firms have to spend a considerable amount of resources in incentivizing the stakeholders of the platform, R&D, and marketing activities to stay relevant in the market (Cusumano, 2020). In Essay Two, I investigate the effect of a platform innovation on a firm’s performance under competitive threats. As argued earlier, technology-intensive firms operate in an ever-changing environment where competition is continuously evolving and mimicking the products of the focal firm. This constantly evolving product market competition is inherent in high technology industries. While product market competition encourages the overall pace of innovation as seen in technology-intensive industries, we are not aware of its effect on value generated by the firms operating in those industries. In the second Essay, I model the effect of product market competition on a firm’s performance. I look at how adopting a platform business model mitigates the effect of product market competition on a firm’s value generation. I use a machine learning-based firm classification method to measure the business model adopted by a firm. I extracted data from 10-K annual reports of the sample firms and classified the firms as platform or non-platform based on the supervised classification of 10-K annual reports of the firm. Using a 20-year panel of the firm’s financial data and their business classification, I explore the effect of a platform business model on a firm’s performance under high product market competition. My results suggest that adopting a platform business model can be an effective business strategy in delivering better value in general and under high market competition in particular. A third innovation strategy that has found favor with firms in recent years to build a competitive advantage over rivals is engaging in open innovation. Open innovation is defined as “a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology” (Chesbrough, 2003). In the context of information technology-intensive firms, open innovation manifests itself in many ways. In recent years, for-profit firms have started engaging with open-source communities to develop products and services on social coding platforms like GitHub. According to my investigation, 41 of the top 100 firms by market valuation have a direct presence on GitHub and actively develop their products with support from open-source developer communities. Opening up open software products and services for the world is another way that allows for faster development and propagation of products across user and developer communities (Khan, 2018). Firms also sponsor open source community developed products and regularly sponsor summer coding schools and hackathons (Mitchell, 2012). These open innovation events have shown promise in the collaborative development of products and services (Tereweisch and Xu, 2008). Firms appropriate rents by selling complementary services for the products they are developing as open-source. In his famous 1997 book, “The Cathedral and the Bazaar,” Eric Raymond coined the term “Cathedral” model of software development to represent the closed sourced, hierarchical and proprietary model of software development and “Bazaar” to represent the open-source, free and equality based software development model (Raymond, 1997). However, there is limited empirical evidence to suggest that firms create and capture value on open innovation platforms like GitHub (West et al., 2014). We do know that firms have started selective revealing of their accumulated knowledge and started engaging with open source communities (Fosfuri et al., 2008; Henkel et al., 2014; Alexy et al., 2018). In the third Essay, I investigate the effect of open-source engagement on the economic outcomes of a firm. More specifically, I look at how engagement on the open-source platform and intensity of that engagement influence the financial performance of a firm. To investigate the influence of open-source innovation on a firm’s financial performance, I created a data set containing all continuous open-source engagements of firms in high technology sectors. I collected this data from multiple sources, including GitHub, 10-K reports, and a search of innovation contests organized by firms. I then matched this data set with the financial information of the firms. I employed the generalized synthetic control method (GSynth) to estimate the model. I estimated the dynamic panel data regression model to measure the influence of open-source engagement intensity on financial performance. Additionally, I also investigated the heterogeneity in the effect of open-source engagement on the financial performance of the firm using the random causal forest. My results suggest that open-source engagement and its intensity positively influence the financial performance of a firm. The effects are heterogeneous and based on the absorptive capacity of the firm, market competition, and other environmental factors. I explore and discuss the implications of my findings on open-source engagement choices by firms. Finally, I conclude this dissertation with the findings of my essays and their implications on information technology-intensive firms. I provide additional details about my studies in the Appendices. The Appendices also highlight the additional analysis done during the research to test the robustness of the results. Overall, this dissertation has broader implications for research and practice alike. There are opportunities for future research and investigation into various innovation strategies adopted by firms in high technology industries. This research also provides directions for applying novel research methods, like the generalized synthetic control method and machine learning algorithms, in IS research

An Empirical analysis of Open Source Software Defects data through Software Reliability Growth Models

The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) using Software Reliability Growth Models (SRGM). This study uses defects data of twenty five different releases of five OSS projects. For each release of the selected projects two types of datasets have been created; datasets developed with respect to defect creation date (created date DS) and datasets developed with respect to defect updated date (updated date DS). These defects datasets are modelled by eight SRGMs; Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model. These models are chosen due to their widespread use in the literature. The SRGMs are fitted to both types of defects datasets of each project and the their fitting and prediction capabilities are analysed in order to study the OSS reliability growth with respect to defects creation and defects updating time because defect analysis can be used as a constructive reliability predictor. Results show that SRGMs fitting capabilities and prediction qualities directly increase when defects creation date is used for developing OSS defect datasets to characterize the reliability growth of OSS. Hence OSS reliability growth can be characterized with SRGM in a better way if the defect creation date is taken instead of defects updating (fixing) date while developing OSS defects datasets in their reliability modellin

Myths and Realities about Online Forums in Open Source Software Development: An Empirical Study

The use of free and open source software (OSS) is gaining momentum due to the ever increasing availability and use of the Internet. Organizations are also now adopting open source software, despite some reservations, in particular regarding the provision and availability of support. Some of the biggest concerns about free and open source software are post release software defects and their rectification, management of dynamic requirements and support to the users. A common belief is that there is no appropriate support available for this class of software. A contradictory argument is that due to the active involvement of Internet users in online forums, there is in fact a large resource available that communicates and manages the provision of support. The research model of this empirical investigation examines the evidence available to assess whether this commonly held belief is based on facts given the current developments in OSS or simply a myth, which has developed around OSS development. We analyzed a dataset consisting of 1880 open source software projects covering a broad range of categories in this investigation. The results show that online forums play a significant role in managing software defects, implementation of new requirements and providing support to the users in open source software and have become a major source of assistance in maintenance of the open source projects

Reliability in open source software

Open Source Software is a component or an application whose source code is freely accessible and changeable by the users, subject to constraints expressed in a number of licensing modes. It implies a global alliance for developing quality software with quick bug fixing along with quick evolution of the software features. In the recent year tendency toward adoption of OSS in industrial projects has swiftly increased. Many commercial products use OSS in various fields such as embedded systems, web management systems, and mobile software’s. In addition to these, many OSSs are modified and adopted in software products. According to Netcarf survey more than 58% web servers are using an open source web server, Apache. The swift increase in the taking on of the open source technology is due to its availability, and affordability. Recent empirical research published by Forrester highlighted that although many European software companies have a clear OSS adoption strategy; there are fears and questions about the adoption. All these fears and concerns can be traced back to the quality and reliability of OSS. Reliability is one of the more important characteristics of software quality when considered for commercial use. It is defined as the probability of failure free operation of software for a specified period of time in a specified environment (IEEE Std. 1633-2008). While open source projects routinely provide information about community activity, number of developers and the number of users or downloads, this is not enough to convey information about reliability. Software reliability growth models (SRGM) are frequently used in the literature for the characterization of reliability in industrial software. These models assume that reliability grows after a defect has been detected and fixed. SRGM is a prominent class of software reliability models (SRM). SRM is a mathematical expression that specifies the general form of the software failure process as a function of factors such as fault introduction, fault removal, and the operational environment. Due to defect identification and removal the failure rate (failures per unit of time) of a software system generally decreases over time. Software reliability modeling is done to estimate the form of the curve of the failure rate by statistically estimating the parameters associated with the selected model. The purpose of this measure is twofold: 1) to estimate the extra test time required to meet a specified reliability objective and 2) to identify the expected reliability of the software after release (IEEE Std. 1633-2008). SRGM can be applied to guide the test board in their decision of whether to stop or continue the testing. These models are grouped into concave and S-Shaped models on the basis of assumption about cumulative failure occurrence pattern. The S-Shaped models assume that the occurrence pattern of cumulative number of failures is S-Shaped: initially the testers are not familiar with the product, then they become more familiar and hence there is a slow increase in fault removing. As the testers’ skills improve the rate of uncovering defects increases quickly and then levels off as the residual errors become more difficult to remove. In the concave shaped models the increase in failure intensity reaches a peak before a decrease in failure pattern is observed. Therefore the concave models indicate that the failure intensity is expected to decrease exponentially after a peak was reached. From exhaustive study of the literature I come across three research gaps: SRGM have widely been used for reliability characterization of closed source software (CSS), but 1) there is no universally applicable model that can be applied in all cases, 2) applicability of SRGM for OSS is unclear and 3) there is no agreement on how to select the best model among several alternative models, and no specific empirical methodologies have been proposed, especially for OSS. My PhD work mainly focuses on these three research gaps. In first step, focusing on the first research gap, I analyzed comparatively eight SRGM, including Musa Okumoto, Inflection S-Shaped, Geol Okumoto, Delayed S-Shaped, Logistic, Gompertz and Generalized Geol, in term of their fitting and prediction capabilities. These models have selected due to their wide spread use and they are the most representative in their category. For this study 38 failure datasets of 38 projects have been used. Among 38 projects, 6 were OSS and 32 were CSS. In 32 CSS datasets 22 were from testing phase and remaining 10 were from operational phase (i.e. field). The outcomes show that Musa Okumoto remains the best for CSS projects while Inflection S-Shaped and Gompertz remain best for OSS projects. Apart from that we observe that concave models outperform for CSS and S-Shaped outperform for OSS projects. In the second step, focusing on the second research gap, reliability growth of OSS projects was compared with that of CSS projects. For this purpose 25 OSS and 22 CSS projects were selected with related defect data. Eight SRGM were fitted to the defect data of selected projects and the reliability growth was analyzed with respect to fitted models. I found that the entire selected models fitted to OSS projects defect data in the same manner as that of CSS projects and hence it confirms that OSS projects reliability grows similarly to that of CSS projects. However, I observed that for OSS S-Shaped models outperform and for CSS concave shaped models outperform. To overcome the third research gap I proposed a method that selects the best SRGM among several alternative models for predicting the residuals of an OSS. The method helps the practitioners in deciding whether to adopt an OSS component, or not in a project. We test the method empirically by applying it to twenty one different releases of seven OSS projects. From the validation results it is clear that the method selects the best model 17 times out of 21. In the remaining four it selects the second best model

Reliability modeling of a 1-out-of-2 system: Research with diverse Off-the-shelf SQL database servers

Fault tolerance via design diversity is often the only viable way of achieving sufficient dependability levels when using off-the-shelf components. We have reported previously on studies with bug reports of four open-source and commercial off-the-shelf database servers and later release of two of them. The results were very promising for designers of fault-tolerant solutions that wish to employ diverse servers: very few bugs caused failures in more than one server and none caused failure in more than two. In this paper we offer details of two approaches we have studied to construct reliability growth models for a 1-out-of-2 fault-tolerant server which utilize the bug reports. The models presented are of practical significance to system designers wishing to employ diversity with off-the-shelf components since often the bug reports are the only direct dependability evidence available to them

A theory-grounded framework of Open Source Software adoption in SMEs

This is a post-peer-review, pre-copyedit version of an article published in European Journal of Information Systems. The definitive publisher-authenticated version Macredie, RD and Mijinyawa, K (2011), "A theory-grounded framework of Open Source Software adoption in SMEs", European Journal of Informations Systems, 20(2), 237-250 is available online at: http://www.palgrave-journals.com/ejis/journal/v20/n2/abs/ejis201060a.html.The increasing popularity and use of Open Source Software (OSS) has led to significant interest from research communities and enterprise practitioners, notably in the small business sector where this type of software offers particular benefits given the financial and human capital constraints faced. However, there has been little focus on developing valid frameworks that enable critical evaluation and common understanding of factors influencing OSS adoption. This paper seeks to address this shortcoming by presenting a theory-grounded framework for exploring these factors and explaining their influence on OSS adoption, with the context of study being small- to medium-sized Information Technology (IT) businesses in the U.K. The framework has implications for this type of business – and, we will suggest, more widely – as a frame of reference for understanding, and as tool for evaluating benefits and challenges in, OSS adoption. It also offers researchers a structured way of investigating adoption issues and a base from which to develop models of OSS adoption. The study reported in this paper used the Decomposed Theory of Planned Behaviour (DTPB) as a basis for the research propositions, with the aim of: (i) developing a framework of empirical factors that influence OSS adoption; and (ii) appraising it through case study evaluation with 10 U.K. Small- to medium-sized enterprises in the IT sector. The demonstration of the capabilities of the framework suggests that it is able to provide a reliable explanation of the complex and subjective factors that influence attitudes, subjective norms and control over the use of OSS. The paper further argues that the DTPB proved useful in this research area and that it can provide a variety of situation-specific insights related to factors that influence the adoption of OSS

Using a Combination of Measurement Tools to Extract Metrics from Open Source Projects

Software measurement can play a major role in ensuring the quality and reliability of software products. The measurement activities require appropriate tools to collect relevant metric data. Currently, there are several such tools available for software measurement. The main objective of this paper is to provide some guidelines in using a combination of multiple measurement tools especially for products built using object-oriented techniques and languages. In this paper, we highlight three tools for collecting metric data, in our case from several Java-based open source projects. Our research is currently based on the work of Card and Glass, who argue that design complexity measures (data complexity and structural complexity) are indicators/predictors of procedural/cyclomatic complexity (decision counts) and errors (discovered from system tests). Their work was centered on structured design and our work is with object-oriented designs and the metrics we use parallel those of Card and Glass, being, Henry and Kafura's Information Flow Metrics, McCabe's Cyclomatic Complexity, and Chidamber and Kemerer Object-oriented Metrics

OSS integration issues and community support: an integrator perspective

The reuse and integration of Open Source Software (OSS) components provided by OSS communities is becoming an economical and strategic need for today’s organizations. The integration of OSS components provides many benefits, but also risks and challenges. One of the most important risks is the lack of effective and timely OSS community support for dealing with possible integration problems. For gaining an understanding of the common problems that organizations face when integrating OSS components, and the role played by OSS communities, we performed an exploratory study on 25 OSS integration projects from different European organizations. The results show that the main way of reducing integration problems was the use of OSS components from well-established communities; therefore very few integration problems were identified. In most of the cases these problems were successfully solved with the support from the OSS community and/or colleagues. In addition, contrary to the common belief that understanding code from someone else is a hard and undesirable task, some integrators consider OSS code even more understandable than their own code.Peer ReviewedPostprint (author's final draft