Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

Emerging IT investments and firm performance: a perspective of the digital options

Purpose

This study aims to explore the mediating effect of digital options on the relationship between emerging information technology investments (ITIs) and firm performance (FP). In particular, it analyses the performance impacts of investments in five emerging technologies of IT or non-IT firms.

Design/methodology/approach

Secondary data are collected from Chinese A-share listed companies from 2010 to 2018. The authors propose an econometric model focusing on the impact of ITIs on a firm’s market value and profit. A propensity score matching model is applied to control endogeneity.

Findings

The ITIs’ effect on FP is found to be completely mediated by digital options, and the reach of digital options plays a more positive role in the relationship between ITIs and Tobin’s Q, whereas the richness of digital options is stronger between ITIs and return on net assets (ROE). The group study shows that the impact of process technologies such as cloud computing and the Internet of Things has a more profound impact on Tobin’s Q, and the knowledge technologies represented by artificial intelligence, blockchain and big data strongly affect ROE. In addition, the positive relationship between ITIs and FP is unrelated to IT/non-IT firms.

Research limitations/implications

First, the data are based on 219 publicly announced emerging ITIs in China and thus may not be generalizable to other cultural/national contexts. Second, there is a lack of a large sample data set of emerging ITI information in China, and the duration of this study is constrained to the relatively short rise of emerging technologies.

Practical implications

This study provides firm decision-makers with practical implications. The results imply that the effect of ITIs on FP depends on digital options, so both IT firms (e.g., Big Tech giants) and non-IT firms (e.g., incumbents) should discover how to balance firm value and profit in their management of emerging technology investment projects with digital options thinking.

Originality/value

To the best of the authors’ knowledge, this is the first empirical study to investigate the relationship between ITIs and FP from the perspective of digital options, exploring five emerging technologies and considering firm life, size, and state ownership in a sample of Chinese listed firms.

Data-based drivers of big data analytics utilization: moderating role of IT proactive climate

Purpose

This study uses the resource-based view (RBV) and isomorphism to investigate the influence of data-based resources (i.e. bigness of data, data accessibility (DA) and data completeness (DC)) on big data analytics (BDA) use under the moderation effect of organizational culture (i.e. IT proactive climate). It also analyzes the possible relationship between BDA implementation and value creation.

Design/methodology/approach

The empirical validation of the research model was performed through a cross-sectional procedure to gather survey-based responses. The data obtained from a sample of 190 IT executives having relevant educational backgrounds and experienced in the field of big data and business analytics were analyzed using structural equation modeling.

Findings

BDA usage can generate significant value if supported by proper levels of DA and DC, which are benefits obtained from the bigness of data (high volume, variety and velocity of data). In addition, data-driven benefits have stronger impacts on BDA usage in firms with higher levels of IT proactive climate.

Originality/value

The present paper has extended the existing literature as it demonstrates facilitating characteristic of data-based resources (i.e. DA and DC) on BDA implementation which can be intensified with an established IT proactive climate in the firm. Additionally, it provides further theoretical and practical insights which are illustrated ahead.

Exploring the effect of team climate on knowledge management in teams through team work engagement: evidence from knowledge-intensive firms

Purpose

Based on the Job Demands-Resources (JD-R) model, this paper aims to investigate the impact of team climate (TC) supporting trust, cohesion and innovativeness on knowledge management (KM) in teams through the mediating role of teamwork engagement (TWEG).

Design/methodology/approach

Data were collected from 246 employees working in Tunisian knowledge-intensive firms (KIFs) and involved in 69 service R&D teams. The authors applied the partial least squares-structural equation modeling approach for data analysis using SMART PLS 3.2 software.

Findings

Findings provide evidence that TWEG mediates the link between TC characterized by trust, cohesion and innovativeness and KM in teams. In the line of the JD-R model, results also show that such TC provides job resources to team members that act as vitamins nurturing TWEG, which in turn boosts KM in teams.

Originality/value

To shed light on the micro-level foundations of KM, this paper enriches the KM literature through pioneering the examination of the effect of TC on KM in teams. It proposes an extension of the JD-R model through highlighting the TWEG’s mediating role in the motivational pathway leading teams to invest in KM activities under a supportive TC. It provides key insights into the importance for managers to implement team-oriented policies toward cultivating trust, innovativeness and cohesion within teams to create more dedicated, vigorous and absorbed teams in which intrinsically motivated knowledge workers are likely to collectively engage in KM activities.

A scholarly network of AI research with an information science focus: Global North and Global South perspectives

This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research.

Digital transformation of traditional marketing business model in new industry era

Purpose

Impact of the digitalization on the production and service sector is a highly popular topic in these days and especially, new business models receive increasingly more attention. Under the light of digitalization, the Fourth Industrial Revolution, so-called Industry 4.0, and its impacts on all kinds of process is a promising topic in the academia and also beneficial for the practitioners. Since there are arguments from scholars that Industry 4.0 has an important and shaping effect on marketing, the concept of 7P's in marketing should be incorporated in Industry 4.0 elements. From this point of view, this study focuses on developing the understanding of 7P's based on contemporary perspectives of Industry 4.0.

Design/methodology/approach

In order to do that, different criteria related to integration of Industry 4.0 and marketing practices under each marketing-mix element are presented and one of the multi-criteria decision-making (MCDM) methods, the best–worst method (BWM) is used to prioritize the criteria for future implications.

Findings

Results indicated that product, process and physical evidence are the most affected marketing-mix factors by considering Industry 4.0. managerial implications which were also presented based on the numerical results.

Originality/value

An in-depth analysis of literature review related to Industry 4.0 revealed that changing and developing technologies are examined in detail mostly around a production perspective. In order to fulfill the gap in the knowledge, this study focuses on the examination of impacts of Industry 4.0 on the marketing-mix strategy. To the best of the authors’ knowledge, this is the first study which merges Industry 4.0 with the marketing mix.

Harvesting reflective knowledge exchange for inbound open innovation in complex collaborative networks: an empirical verification in Europe

Purpose

This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.

Design/methodology/approach

This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.

Findings

Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.

Originality/value

This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.

Barriers, practices, methods and knowledge management tools in startups

Purpose

The purpose of this paper is to present the main barriers, practices, methods and knowledge management tools in startups that are characterized as agile organizations with dynamic capabilities to meet the demands of a business environment of high volatility, uncertainties, complexity and ambiguity.

Design/methodology/approach

The conceptual basis of the research focused on the triad: agile organization, dynamic capabilities and knowledge management. Field research began by interviewing experts to identify the barriers, practices, methods and knowledge management tools in startups. Based on the theoretical review, on the desk research and on the result of interviews with experts, a quantitative research was carried out with the leading startups coworking of São Paulo city. The obtained data made it possible to develop descriptive analyses and run linear regressions and cluster analysis for exploratory research.

Findings

Startups with higher maturity in innovation level, solution development level, and scalability development level, present a higher degree of utilization of the practices, methods and tools dedicated to knowledge management.

Practical implications

It is expected that results of the research presented in detail will be able to illustrate concrete examples of practices, methods, and knowledge management tools for large established companies seeking the organizational agility of startups.

Originality/value

This study contributes to the identification of barriers, practices, methods and tools of management of knowledge in startups, through the conceptual triad: agile organization, dynamic capabilities and knowledge management.