Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions

Artificial Intelligence (AI) is increasingly adopted by organizations to innovate, and this is ever more reflected in scholarly work. To illustrate, assess and map research at the intersection of AI and innovation, we performed a Systematic Literature Review (SLR) of published work indexed in the Clarivate Web of Science (WOS) and Elsevier Scopus databases (the final sample includes 1448 articles). A bibliometric analysis was deployed to map the focal field in terms of dominant topics and their evolution over time. By deploying keyword co-occurrences, and bibliographic coupling techniques, we generate insights on the literature at the intersection of AI and innovation research. We leverage the SLR findings to provide an updated synopsis of extant scientific work on the focal research area and to develop an interpretive framework which sheds light on the drivers and outcomes of AI adoption for innovation. We identify economic, technological, and social factors of AI adoption in firms willing to innovate. We also uncover firms' economic, competitive and organizational, and innovation factors as key outcomes of AI deployment. We conclude this paper by developing an agenda for future research

Towards a business analytics capability for the circular economy

Digital technologies are growing in importance for accelerating firms’ circular economy transition. However, so far, the focus has primarily been on the technical aspects of implementing these technologies with limited research on the organizational resources and capabilities required for successfully leveraging digital technologies for circular economy. To address this gap, this paper explores the business analytics resources firms should develop and how these should be orchestrated towards a firm-wide capability. The paper proposes a conceptual model highlighting eight business analytics resources that, in combination, build a business analytics capability for the circular economy and how this relates to firms’ circular economy implementation, resource orchestration capability, and competitive performance. The model is based on the results of a thematic analysis of 15 semi-structured expert interviews with key positions in industry. Our approach is informed by and further develops, the theory of the resource-based view and the resource orchestration view. Based on the results, we develop a deeper understanding of the importance of taking a holistic approach to business analytics when leveraging data and analytics towards a more efficient and effective digital-enabled circular economy, the smart circular economy.publishedVersio

Strategic enterprise management systems : tools for the 21st century

https://egrove.olemiss.edu/aicpa_guides/1228/thumbnail.jp

The disruptive role of cloud computing in global distribution strategies : the case of Quatenus at Sinfic

Cloud computing is emerging as a computing paradigm wherein virtual distribution channels are enabled and used as innovative entry modes (Brown and Johnson, 2012). Through this technology, global scale efficiency is promised, delivering operational capabilities with important value to the development of hybrid international marketing strategies underlying today’s global competitive set (Brown and Johnson, 2012) where access to contextualized knowledge is becoming crucial (Bughin, Byers and Chui, 2011). Current practices however indicate that companies are not addressing these capabilities to build flexible delivery platforms and engage knowledge-driven strategies (Brown and Johnson, 2012). Boosted by the gap between these initiatives, this research explores how to develop knowledge-driven internationalization strategies based on cloud architectures, pursuing a case study analysis on a Portuguese software vendor, which recently invested on a cloud-based delivery platform to assemble a knowledge-driven internationalization strategy. Significant influences of cloud computing were found in the development of flexible delivery platforms during the process of externalization of the company. These findings contribute with further insights into understanding the coupling between cloud-based distribution strategies and knowledge-driven internationalization patterns. A consistent example of a cloud enterprise as a business enabler in knowledge-driven economies is thus proven possible, suggesting how flexible delivery platforms can be engaged within the development of metanational strategies in the current competitive environment

Improving Consulting Processes in Web Analytics: A Framework for Multichannel Analytics

To control and optimise their marketing activities, organisations analyse customer behaviour on their online and offline channels. This is referred to as multichannel analytics (MCA). As enterprises often do not have the necessary know-how to implement analytics processes, analytics consultants support them in such projects. The problem for the consultants is that a standardised approach, which provides orientation and guidance during such projects, is currently not available. The goal of this paper is to develop a framework, which guides consultants in order to avoid common project-related problems. It is developed employing Design Science Research Methodology. Empirical data collection and iterative validation of the framework are based on literature research, document analysis, expert interviews and a focus group. Results highlight that it is useful to combine a capability maturity model and an analytics procedure model. This allows taking into account the different degrees of organisational maturity during the consulting process

Collaborative Practices and Multidisciplinary Research : The Dialogue Between Entrepreneurship, Management, and Data Science

Author's accepted version (post-print).Available from 06/06/2020.acceptedVersio

Big Data and the Data Value Chain: Translating Insights from Business Analytics into Actionable Results - The Case of Unit Load Device (ULD) Management in the Air Cargo Industry

Business intelligence and analytics enjoy a great deal of attention today. However, there is a lack of studies considering the full data value chain from (raw) data through business analytics to valuable decisions, i.e. also scrutinizing the latter stages of the data value chain, namely timely deployment and operational usage of valuable insights as demanded by practice. Following a design science approach, we develop a concept for the fast and flexible integration of valuable insights into daily decision support. A key feature of our concept is to provide valuable insights from business intelligence in an understandable manner to decision makers using a rule-based expert systems approach. In order to demonstrate the feasibility of our concept, we implemented a prototype in a complex real-world scenario, i.e. unit load device (ULD) management in the air cargo industry. This research in progress presents our preliminary findings and outlines the potential of the proposed concept

E-CRM and CMS systems: potential for more dynamic businesses

Any change in customer’s behaviour affects the customer’s value. In addition, profitability and economic viability also change. Most companies still do not know entirely their customer base characteristics. They find difficult to define criteria that segment their customer base to find high-value customers. They need to focus on target selections to carry on with marketing campaigns which involve high investments. Given the potential of e-CRM and CMS as powerful tools to guide customer-oriented understanding and analysis, greater attention is required. Several companies, operating within the same business and having access to the same information and technology, differ in e-CRM performance. Without sufficient evidence, managers are prone to making investment decisions that are neither efficient nor effective. So it is imperative to base the decision of e-CRM and CMS adoption, on not only their analytical power, but also on economic viability criteria for sustainable business dynamic

Business intelligence and big data in hospitality and tourism: a systematic literature review

Purpose This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization. Findings Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data