Exploring technological process innovation from a lifecycle perspective
– Technological process innovation (TPI) is a distinctive organizational phenomenon characterized by a firm-internal locus and underlying components such as mutual adaptation of new technology and existing organization, technological change, organizational change, and systemic impact. The purpose of this paper is to investigate the management of these components at different stages of the innovation lifecycle (ILC) in large manufacturing companies.
– The authors adopt an exploratory case-based research design and conduct a multiple case study of five large successful manufacturing companies operating in different industries in Germany. The authors build the study on 55 semi-structured interviews, which yielded 91.5 hours of recorded interview data. The authors apply cross-case synthesis and replication logic to identify patterns of how companies address process innovation components at different ILC stages.
– The study uncovers the content of four central TPI components across the ILC and identifies differences between the development of core and non-core processes. Based on the findings the authors describe asymmetric adaptation as a theoretical construct and propose that companies seek different levels of process standardization depending on the type of process they develop, which in turn affects whether there is a greater extent of technological or organizational change.
– Awareness of existing structures, processes, and technologies, as well as their value in relation to the company’s core and non-core operations is imperative to determining the adequate structure of mutual adaptation.
– The authors provide detailed insight on the management of mutual adaptation, technological, and organizational change, as well as systemic impact at the different stages of the ILC. The authors extend prior research by adopting an ILC perspective for the investigation of these four TPI components and by proposing a construct of asymmetric adaptation to capture key mechanisms of process development and implementation.
The Evolution of Organisational Forms in the Digital Games Industry
The digital games industry - along with the music, film and book industries - is commonly referred to as part of the creative industry. However, although they can all be grouped under the same label, the digital game industry is the only one that is natively digital. During the past decade, the industry experienced a phenomenal growth in terms of social and economic significance. However, the impact of the industry, in socioeconomic terms, has remained unexplored in academic literature. Our project, NEMOG, leverages on the impact that the high degree of innovation has on all the stakeholders along the industry value chain, it addresses the changes enabled by technology in terms of business models and industrial organizational structure. To do so, the first year of research has focused on three research issues: the analysis of the impact of technology on business models innovation in the digital game industry, the mapping of the evolutionary trajectory of the industry’s business model innovation process, the innovation mechanisms that fuelled this particular path and growing areas of potential uses of digital games outside of purely entertainment purposes.
A Phylogenetic Classification of the Video-game Industry's Business Model Ecosystem
Since 1990, Business Models emerged as a new unit of interest among both academics and practitioners. An emerging theme in the growing academic literature is focused on developing a system that employs business models as a focal point of enterprise classification. In this paper we attempt a historical analysis of the video game industry business model evolution and examine the process through the prism of two-sided market economics. Based on the biological school of phylogenetic classification, we develop a cladogram that captures the evolution process and classifies the industry’s business models. The classification system is regarded as a first attempt to provide an exploratory and descriptive research of the video game industry, before attempting an explanatory and predictive analysis, and introduces a system that is not governed by the industry’s specific characteristics and can be universally applied, providing a map for researchers and practitioners to test organisational differences and contribute further to the business model knowledge.
Game engagement, as one of the most fundamental objec- tives for game designers to achieve, has become an attractive industrial and academic topic. An important direction in this area is to construct a model to predict how long a player could be engaged with a game. This paper introduces a pure data driven method to foresee whether a player will quit the game given their previous activity within the game, by constructing decision trees from historical gameplay data of previous players. The method will be assessed on two popular commercial online games: I Am Playr and Lyroke. The former is a football game while the latter is a music game. The results indicate that the decision tree built by our method is valuable to predict the players’ disengagement and that its human-readable form allow us to search out further reasons about what in-game events made them quit.
Game Intelligence is knowledge gained by the player or by analysing the data players generate by playing digital games. Serious games for education, raising public awarenesss or changing the players' behaviour are well established and have provided Game Intelligence for decades. However, more recently a trend has begun, inspired by the success of FoldIt, of developing games for scientific discovery. These games lower the barrier of entry to complex scientific topics, allowing garners to contribute to cutting edge research. We argue that this approach is currently underutilized and explore a vision where these games have wider impact. Furthermore, we will discuss the potential of extracting Game Intelligence from games designed originally for entertainment, potentially making all games into scientific discovery games.
Diffusion of multi-generational high-technology products
Previous multi-generational product diffusion (MGPD) models were developed based on the diffusion patterns at that time, but may not be adopted in today's cases. By incorporating the effect of customers' forward-looking behaviour, this paper offers a parsimonious and original model that captures the dynamics of MGPD in current high-technology markets. We empirically examine the feasibility of using previous MGPD models and our suggested model to explain the market growth of new products from high-technology industries. The results show that the new model exhibits better curve fitting and forecasting performance than the prior MGPD models in the cases of this study. For marketing researchers, our model and its results suggest customers' forward looking behaviour is perhaps one of the key sales affecting factors that are missing in previous MGPD models in explaining nowadays' cases. For marketing practitioners, this study offers a valuable tool for marketing strategies in high-tech industries.