Welcome to the October issue of the Technology Innovation Management Review. This month features two more invited papers from the 31st ISPIM Innovation Conference, with the theme "Innovating in Times of Crisis", held virtually on June 7-8th, 2020. Two other papers add further contributions rounding out an edition that explores artificial intelligence for platform innovation, data-driven business logic, business models in disruptive industries, and sustainability communications patterns by companies spending on R&D.
Sergey A. Yablonsky’s “AI-Driven Digital Platform Innovation” begins the issue by focusing on the business shift towards big data (BD) involved with emerging digital enterprise platforms. He highlights the potential of advanced analytics (AA) and artificial intelligence (AI) to enhance value chain growth and efficiency as companies grow their AI capacities. The paper “develops a multi-dimensional AI-driven platform innovation framework with AI/BD/AA innovation value chain and related levels of AI maturity improvement” (pg. 5). It addresses “new ways to reuse and extract value from BD assets through AI-driven platform innovation” (pgs. 14-15) and proposes that “today’s leaders [also] need to more openly embrace AI and become involved in contributing to the discussion of AI ethics” (pg. 15).
Petra Kugler follows this with “Approaching a Data-Dominant Logic”. Her paper also looks at data science, here in the context of developing a new type of “dominant logic” for business that makes better use of data. “[F]irms first need to establish a new mindset,” says Kugler, “in which data plays a central role” (pg. 17). Researching the ways data can be used to impact businesses led her to propose a data-dominant logic (DDL) framework, which she applies in this paper based on an empirical study of the organizational and managerial requirements of SMEs. Through a survey and interviews with representatives from 16 SMEs in Austria, Germany, and Switzerland, she develops a list of DDL working hypotheses, noting that “many firms have no clear repertoire to act on a data strategy within the changing setting and therefore cannot fully exploit the potential inherent to data science practices” (pg. 26).
Alina Marie Herting and Alexander Lennart Schmidt partner on the paper “A Systematic Analysis of how Practitioners Articulate Business Models across Disruptive Industries”. They start with the problem that “[t]oo little is still known about how practitioners highlight different characteristics of business models across industries confronted with disruptive dynamics” (30). To explore the different characteristics and how business models are articulated in disruptive industries, they studied the business models of companies based on 1,095 press releases and company reports across 11 industries published between 1995 and 2019. From this, they identify various challenges and components of business models that differ across specific disruptive industries.
The final paper is by Giacomo Liotta*, Stoyan Tanev, Andrea Gorra, and Alicja Izabela Pospieszala focusing on “Sustainability-related Communication Patterns on the Websites of European Top R&D Spenders”. Their research draws attention to sustainability patterns in corporate communication that could inform sustainable innovation business decision-making. The authors use a web-based data collection methodology and principal component analysis of frequencies of words in publicly available textual data to make the key observation that a “focus on sustainable operations serves as most companies’ key communication pillar, which they complement with a focus on stakeholder benefits and sustainable innovation” (53). The findings show “a strong relationship between the communication of sustainable innovation aspects and sales, which offers a promising message to companies looking for evidence about the potential impact of their commitment to sustainable operations and innovation” (pg. 44).
The TIM Review currently has Calls for Papers on the website for Upcoming Themes with special editions on "Digital Innovations in the Bioeconomy" (Feb. 2021) and “Aligning Multiple Stakeholder Value Propositions” (March 2021). For future issues, we invite general submissions of articles on technology entrepreneurship, innovation management, and other topics relevant to launching and scaling technology companies, and for solving business practical problems in emerging domains such as artificial intelligence and blockchain applications in business. Please contact us with potential article ideas and submissions, or proposals for future special issues.
Keywords: Advanced Analytics, AI maturity. Data science, AI value chain, AI-driven platform innovation, Artificial Intelligence (AI), big data, business decision-making, business model components, business models, content analysis, data-dominant logic, dominant logic, empirical study, enterprise platform, industries, online communication, online data collection, organizational and managerial requirements, principal component analysis, R&D, research and development, secondary data. Sustainability, SMEs. Disruptive innovation, sustainable innovation