@article{63d6f19184304dd89553351c2c4f578e,
title = "The logic of innovative value proposition: A schema for characterizing and predicting business model evolution",
abstract = "Innovations in business models involve a dynamic process that makes business values resilient against market and technology changes. We propose a schema that supports predicting the effect of innovation on business model values. The schema redefines the value proposition logic in business model innovations with five primary variables: business dependence structure, business value dominance, innovation dynamics, innovation domains, and innovation-resources-agility. Based on the schema, we have tested a set of hypotheses for 474 cases of business model innovations in four textile processing technology markets. We have estimated and modeled the data using two techniques: Cox modeling and temporal qualitative comparative analysis. The former predicts the business model that is destructed by innovation over time and the latter assesses the configurative conditions for innovation during the destruction period. The findings offer insights for predicting business model innovation as a value creation platform and for predicting innovative business models in industrial technology markets.",
keywords = "Business model innovation, Dynamics of innovations, Semiparametric Cox analysis, temporal QCA",
author = "To, {Kin Man} and Chau, {Kwok Pui} and Kan, {Chi Wai}",
note = "Funding Information: Fourth, like the other statistical inference methods, the Cox analysis estimates the net effect sizes of each hypothesized variable. This is a deductive process. The results cannot advise any implications of multiconjunctivity among these variables leading to the predicted outcomes. In reality, the destruction of a business model by innovation does not necessarily depend on the occurrence of all these variables at the same time. Such cases also occur for temporal events, as investigated for business model innovation destructing in this study. The QCA method can addresses such issues of causality and temporality. However, the QCA is a partially inductive process and is restricted by the issue of ?limited diversity? (Ragin, 2008). It needs to place theoretic propositions to limit the number of configurations, and thus reduce the possible solution set into a manageable size. In light of such restrictions, the temporal QCA separates the analysis of points in time into two contextual conditions: business and organizational. The inspection of their similarity and differences across the solution conditions can systematically explain the time-relevant solution consistency. Like the conventional causality analysis, the temporal QCA is subject to the availability of data that are contextually stable over time: that is, the causes and outcomes are independent of time; and the manageability of the number of points in time.This research project is funded by The Hong Kong Polytechnic University (Project account: 1-ZVLD). Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",
year = "2020",
month = may,
doi = "10.1016/j.jbusres.2019.10.023",
language = "English",
volume = "112",
pages = "502--520",
journal = "Journal of Business Research",
issn = "0148-2963",
publisher = "Elsevier Inc.",
}