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The aim of the study is to explore the possible practical impact of big data/business intelligence and Internet of Things on the purchasing process of premium automotive manufacturers, and to evaluate its theoretical impact with a transaction cost economics approach.

An exploratory multiple-case study was carried out, using qualitative content analysis and cross-case synthesis.

Collaborative platforms and a new purchaser role were found to impact the entire process. In the strategic purchasing 4.0 process, co-creation of specifications, automated prequalification, and parameter-based negotiations are some expected changes. The operative purchasing 4.0 process is shaped by, for example, interactive call-offs. Transaction cost is expected to decrease by reduced uncertainty and supplier specificity, as well as by lowered information search, negotiation, and monitoring costs.

The description of a potential purchasing 4.0 process for premium automotive manufacturers is given.

Premium automotive manufacturers can develop strategies to push the existing standards of purchasing. Suppliers can create scenarios to allow for future compliance at the purchasing–sales interface.

New technologies' effects on the workforce are considered.

No identified study focused on the impact of Industry 4.0 technologies on the purchasing process of premium automotive manufacturers.

Many power producers are looking for ways to develop smarter energy capabilities to tackle challenges in the sophisticated, non-linear dynamic processes due to the complicated operating conditions. One prominent strategy is to deploy advanced intelligence systems and analytics to monitor key performance indicators, capture insights about the behavior of the electricity generation processes, and identify factors affecting combustion efficiency. Thus, the purpose of this paper is to outline a way to incorporate a business intelligence framework into existing coal-fired power plant data to transform the data into insights and deliver analytical solutions to power producers.

The proposed ten-step business intelligence framework combines the architectures of database management, business analytics, business performance management, and data visualization to manage existing enterprise data in a coal-fired power plant.

The results of this study provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and consequently explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

Once the framework is integrated into the power generation process, it is important to ensure that the top management and the data analysts at the plants have the same perceptions of the benefits of big data and analytics in the long run and continue to provide support and awareness of the use of business intelligence technology and infrastructure in operational decision making.

The key finding of this study helps the power plant prioritize the important factors associated with the emission of NO_x; closer attention to those factors can be promptly initiated in order to improve the performance of the plant.

The use of big data is not just about implementing new technologies to store and manage bigger databases but rather about extracting value and creating insights from large volumes of data. The challenge is to strategically and operationally reconsider the entire process not only to prepare, integrate, and manage big data but also to make proper decisions as to which data to select for the analysis and how to apply analytical techniques to create value from the data that is in line with the strategic direction of the enterprise. This study seeks to fill this gap by outlining how to implement the proposed business intelligence framework to provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and to explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

In particular, it is worth mentoring new and more efficient solutions that can meet the increasingly specific needs of each company, especially in food management. A business intelligence (BI) solution can help your food company better understand and manage business processes more effectively. Management information is essential for all levels of an organisation to make quick and correct decisions. However, what exactly is BI, and what can it mean for a food company?

The PRISMA stands for (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and content analysis strategy used the SLR (systematic literature review) methodology to examine 151 papers published in peer-reviewed academic journals and industry reports between 2016 and 2023.

The findings show that artificial intelligence and digitalisation are linked to the UN 2030 Agenda. BI management ranks first (66%), followed by crop and land mapping systems (40%), agricultural machinery monitoring tools (39%) and decision support systems (31%). The road to digital transformation remains extended, with the main impediments being more compatibility between enterprise systems and a shortage of expertise.

The section relating to methodological perspective adopts the PRISMA methodology for systematic review. Interoperability is easily managed by assigning qualified teams to projects. The added value of a consulting firm with extensive project management experience in the food industry is closely related to the results achieved.

BI: What exactly is it, and why a data-driven culture is essential in the food and beverage industry?

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

This paper aims to help small- and medium-sized enterprises (SMEs) to seize the potential of Big Data in their marketing strategies to leverage a competitive advantage in the rising digital marketplace and lead the post-pandemic economic recovery. However, Spanish SMEs have had serious difficulties in working with Big Data.

In this study, a roadmap is designed to pave the way for a successful Big Data implementation.

SMEs with updated information on how to access the historic funding opportunities might profit to this opportunity. It not only generates financing opportunities for SMEs but also grants continuity to their strategies by offering the most profitable techniques by which they can efficiently analyse data and gain consumer insights, thus overcoming the many problems they face when working with Big Data as well as helping them to monetise their marketing strategies.

The main advantage of this research is its innovative approach to business strategy as it provides. The value added by this paper lies in its holistic and updated approach to supporting SMEs’ Big Data strategies.

The goal of this work is to propose guidelines for the Information Technology (IT) companies in Brazil to implement the concepts of Industry 4.0.

This research study used inductive method, exploratory and descriptive research, and a bibliographic search was performed, besides using a qualitative research as field research. The IT professionals of companies in Brazil were used as the research university.

With the advances of technology and the advents of industrial revolutions, the profile of professionals and the relationship between employees and companies have been altered, fact that was demonstrated in the field research, making it possible to highlight: the knowledge degree of managers about the theme, it means, the perception of what Industry 4.0 is; how the organizations have already been adjusting to the new industrial revolution; the main challenges for this adjustment; the relevance for clients and opponents; among other discoveries. Therefore, companies must adapt to the new market demands, by improving their processes, investing in new technologies and training their employees.

Actions for implementing Industry 4.0 in IT companies were proposed, such as understanding its organization, defining in which area of the business model the organization intends to make/have changes, discovering tendencies and developments that will have influence over the organization when applying the concepts of Industry 4.0, overcoming the major challenges and adhering to new technologies.

With the contribution toward the organization, it is expected that this study can fulfill the demands of clients and provide cost, personnel and time savings the best way possible. For the academy, the contribution lies in presenting a research study with a new theme and with focus on IT.

Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.

In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.

Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.

The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.

This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.

This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.

The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

Given the importance of performance consistency of chain hotels in customers’ decision-making and service evaluation, this study aims to explore how consistently chain hotel brands offer quality service and carry out their performance from the eyes of customers through online reviews on TripAdvisor of the top five US hotel chains (i.e. Choice, Hilton, InterContinental, Marriott and Wyndham) and their brands.

The research objectives were achieved through methodological triangulation: business intelligence, data visualization analytics and statistical analyses. First, the data collection and pre-processing of consumer-generated media (CGM) (i.e. TripAdvisor online reviews) were performed using business intelligence for further analyses. Using data visualization analytics (i.e. box-and-whisker plot by region and brand), the geographic patterns of performance attributes (i.e. online review ratings, including location, sleep, cleanliness, room and service) were depicted. Using a series of analyses of variance and regression analyses, the results were further assessed for the impacts of brand performance inconsistency on consumers’ perceived value, sentiment and satisfaction.

The empirical results demonstrate that there are significant performance inconsistencies in performance attributes (location, sleep, cleanliness, room and service) by brands throughout the six regions in the US hotel market. More importantly, the findings confirm that brand performance consistency significantly influences consumers’ perceived service quality (i.e. perceived value, satisfaction and sentiment).

This study is one of the first attempts to empirically explore hotel brand performance consistency in the US hotel market from customer reviews on CGM. To measure hotel brand performance in the US hotel market, this study collected and analyzed user-generated big data for the top 5 US hotel chains through business intelligence, visualization analytics and statistical analysis. These integrated and novel research methods would help tourism and hospitality researchers analyze big data in an innovative data analytics approach. The findings of the study contribute to the tourism and hospitality field by confirming hotel brand performance inconsistency and such inconsistent performance affected customers’ service evaluations.

This study demonstrates the significant impact of hotel brand performance consistency on consumers’ perceived value, emotion and satisfaction. Considering that online reviews are perceived as a credible source of information, the findings suggest that the hotel industry pays special attention to brand performance consistency to improve consumers’ perceived value, emotion and satisfaction.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.