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Despite the potential of Big Data analytics, the analysis of Micro Data represents the main way of forecasting the expected values of recorded amounts and/or ratios for small auditing firms and certified public accountants dealing with analytical procedures. This study aims to examine how effective Micro Data analytics are by testing the forecast accuracy of the ratio of the allowance for doubtful accounts to the trade accounts receivable and the natural logarithm of the net sales of goods and services, the first exposed to a greater uncertainty than the second.

Micro Data are low in volume, variety, velocity and variability, but high in veracity. Given the over-fitting problems affecting Micro Data analytics, the in-sample and out-of-sample forecasts were made for both tests. Multiple regression and neural network models were performed using a sample of 35 Italian industrial listed companies.

The accuracy level of the forecasting models was found in terms of mean absolute percentage error and other accuracy measures. The neural network model provided more accurate forecasts than multiple regression in both tests, showing a higher accuracy level for the amounts exposed to less uncertainty. Moreover, no generalized conclusions on predictors included in the models could be drawn.

The examination of forecast accuracy helps auditors to evaluate whether analytical procedures can be successfully applied to detect misstatements when Micro Data are used and which model gives the most accurate forecasts.

This is the first study to measure the forecast accuracy of the multiple regression and neural network models performed using a Micro Data set. Forecast accuracy is crucial for evaluating the effectiveness of analytical procedures.

There is a knowledge gap regarding the determinants of open innovation processes and outcomes in a joint value creation context, as well as what role artificial intelligence (AI) and data management play in facilitating open innovation processes. One strategy to better understand joint value creation through open innovation, supported by AI and data management, is to conduct studies on the digital business ecosystem (DBE). The purpose of this paper is to improve our current knowledge of this urgent issue in contemporary management through the lens of an ecosystem-based theory by conducting an empirical study on two DBEs (called ecosystem micro-communities (EMCs)), developed by Haier, as well as multiple literature reviews on the key concepts “Haier EMC” and “digital business ecosystem”.

By building on multiple literature reviews and empirical data from a multi-year and ongoing research program driven by Haier, this study examines Haier’s EMC model for AI-driven DBEs. Secondary data were collected through iterative literature reviews on DBEs, the EMC concept and the two selected EMC cases. The empirical data were collected through a qualitative study of two Haier EMCs in China.

Haier's ecosystem micro-community concept represents a radical shift towards a more flexible, responsive and innovative cross-industry organizational structure, offering valuable lessons for business leaders and scholars. Haier’s ecosystem micro-community model, part of their RenDanHeYi philosophy and here viewed as a DBE, is a pioneering management concept that not only redefines the management of the firm and the traditional corporate structure, but also the traditional view on innovation management, business strategy, human resource management and marketing (customer centricity). The concept has therefore an important and big impact on traditional management. For scholars, the gap in understanding innovation processes in open business ecosystems is addressed by the concept. However, the concept also opens new areas for academic research, particularly in innovation management, business strategy, human resource management and marketing. The concepts further encourage more interdisciplinary research.

The DBE is a relatively new research area that will need more research. While the EMC model is promising as an effective version of a DBE, its effectiveness across different industries and organizational cultures needs to be explored further. Future research should investigate its applicability and impact in diverse business environments. To understand the EMC’s long-term impact, longitudinal studies are needed. These should focus on the sustained competitive advantages, potential market disruptions and the evolution of customer value propositions over time. Finally, considering increasing concerns about data privacy and security, future research should also explore how DBEs solve the issue of data protection and IP while promoting open innovation and value sharing.

For managers and practitioners, the EMC concept could inspire leaders to learn how to foster innovation by creating smaller, autonomous teams that can respond quickly to market changes in the form of a DBE. The concepts exemplify how value creation and capture could be enhanced for any company and even could be a new strategy in the company’s digital transformation and repositioning into a more competitive, high-end player on the market. The concept also emphasizes employee empowerment and ownership, which can lead to higher job satisfaction and retention rates. The concept can further improve companies’ adaptability and resilience by decentralizing decision-making. Finally, the micro-communities allow businesses to be more customer-centric, developing products and services that better meet specific customer needs.

The social implications could be positive, as complex social problems commonly need an ecosystem approach to develop and deliver impactful solutions. In addition, Haier’s ecosystem micro-community model seems inherently scalable and culturally adaptable.

Haier’s EMC model is well-known in the research literature and is a novel approach to DBEs, which has been proven successful and replicable in different countries and industries. Providing insights from multiple literature reviews and two unique Haier EMC cases will contribute to a better understanding of highly effective data- and AI-driven business ecosystems, as well as of determinants of open innovation processes and outcomes in a joint value creation context, as well as what role AI and data management play in facilitating open innovation processes.

The purpose of this paper is to report on an industry policy implementation case involving around 30 manufacturing firms, where the intellectual capital (IC) lens, and especially the intellectual capital navigator (ICN) approach, was found to be very useful for evaluating alternative servitisation strategies. Servitisation is a form of business model innovation and as such involves restructuring the firm’s resource deployment system including its IC resources.

The ICN was one of several methods and themes used by a sample of manufacturing firms during a 12 month period. Data capture were through video filming, observation, and formal interviewing during and after the interventions.

The ICN is considered to be the third most valuable theme in a strategic and operational servitisation programme for manufacturing firms, primarily in the domain of effectiveness evaluation of alternative resource deployment strategies and as such should be one of the key dimensions in a business model template for manufacturing firms that aim to servitize. This research also illustrates the usefulness of the intellectual capital lens in the policy implementation process.

The findings of this study is limited to the servitization process of SME manufacturing firms in an Anglo-Saxon operating environment which very rapidly have gone from low to high cost.

The development of service-oriented business models for manufacturing firms suffers due to traditional business model frameworks not having a high relevance for servitising manufacturing firm. Consequently it is important to understand the potential contribution that the IC lens through the ICN can make in the servitisation process.

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

Micro, small, and medium enterprises (MSMEs) can play a significant role in achieving sustainable development goals (SDGs) as they have the ability to reduce unemployment. Digitalization helps MSMEs in a number of ways, including lowering transaction costs, quickening access to information, and bettering communication with extended supply chain members. This chapter aims to understand the level of digitalization in MSMEs in an emerging economy such as Bangladesh. MSMEs in Bangladesh account for 25% of the gross domestic product and employ 87% of civilians. This chapter builds on qualitative data from 60 MSMEs from various manufacturing and service sectors such as textile, retail, food delivery, IT companies, etc. The interviews were semi-structured and followed an interview protocol. The length of interviews varied between 40 and 50 minutes. Content analysis was used to analyze the data. Findings suggest that counterintuitively the level of digitization in MSMEs is not low in Bangladesh. Many micro and small enterprises use MS Excel to help them manage customer and product data. Medium Enterprises use Enterprise Resource Planning (ERP) software for planning enterprise-wide resources. Some medium enterprises also use powerful data analytics software such as Oracle, Power BI, Google Analytics, Python, and SPSS. Results also reveal barriers to digitization in MSMEs, which include a lack of employee awareness, training, and motivation of top management. This chapter maps the digitalization levels in MSMEs in Bangladesh and provides implications for SGDs. The chapter also presents policy recommendations for improving the digitalization level in emerging economies.

This chapter examines the ways social media, analytics, and disruptive technologies are combined and leveraged by political campaigns to increase the probability of victory through micro-targeting, voter engagement, and public relations. More specifically, the importance of community detection, social influence, natural language processing and text analytics, machine learning, and predictive analytics is assessed and reviewed in relation to political campaigns. In this context, data processing is examined through the lens of the General Data Protection Regulation (GDPR) effective as of May 25, 2018. It is concluded that while data processing during political campaigns does not violate the GDPR, electoral campaigns engage in surveillance, thereby violating Articles 12 and 19, in respect to private life, and freedom of expression accordingly, as stated in the 1948 Universal Declaration of Human Rights.

The outbreak of COVID-19 is one of the most serious health events in recent times. In the business landscape, its effects may be more detrimental to micro-, small- and medium-sized enterprises (MSMEs) because they tend to have limited financial and human resources to manage the challenges caused by COVID-19. To help MSMEs enhance their resilience, this paper aims to discuss how they can leverage mass collaboration to build social media-based knowledge ecosystems to manage interactions among internal and external stakeholders for knowledge creation and innovation.

The paper proposes a model for MSMEs to build an online knowledge ecosystem and a standalone text analytics tool to use the advanced data analytics, e.g. topic modeling, to analyze and aggregate collective insights. Design science research methodology is used to develop the model and the tool.

Through mass collaboration using social media and advanced data analytics technology, MSMEs can generate new business ideas, leading to enhanced resilience to meet the challenges caused by COVID-19 or other unexpected or extraordinary circumstances, such as natural disasters and financial crises.

To the best of authors’ knowledge, this paper is one of the first papers in social media adoption for knowledge creation and innovation research, providing detailed approaches for MSMEs to build a knowledge ecosystem on social media and to use advanced data analytics to mine the meaning of the generated data.

This study aims to investigate organizational learning, represented by the absorptive capacity, as a condition for the firm to learn about marketing data and make more informed decisions. The authors also aimed to understand how the behavior of micro, small and medium enterprises (MSME) businesses differ in this scenario through a multilevel perspective.

Placing absorptive capacity as a mediator of the relationship between business analytics and rational marketing decisions, the authors analyzed data from 224 Brazilian retail companies using structural equation modeling estimated with partial least squares. To test the cross-level moderation effect, the authors also performed a multilevel analysis in RStudio.

The authors found a partial mediation of the absorptive capacity in the relation between business analytics and rational marketing decisions. The authors also discovered that, in the MSMEs firms’ group, even if smaller companies find it more difficult to use data, those that do may reap more benefits than larger ones. This is due to the influence of size in how firms handle information.

The sample size, despite having shown to be consistent and valid, is considered small for a multilevel study. This suggests that our multilevel results should be viewed as suggestive, rather than conclusive, and subjected to further validation.

Rather than solely positioning business analytics as a tool for decision support, the authors’ analysis highlights the importance for firms to develop the absorptive capacity to enable ongoing acquisition, exploration and management of knowledge.

MSMEs are of economic and social importance to most countries, especially developing ones. This research aimed to improve understanding of how this group of firms could transform knowledge into better decisions. The authors also highlight micro and small firms’ difficulties with the use of marketing data so that they can have more effective practices.

The research contributes to the understanding of organizational mechanisms to absorb and learn from the vast amount of current marketing information. Recognizing the relevance of MSMEs, a preliminary multilevel analysis was also conducted to comprehend differences within this group.

The purpose of this study is twofold. First, this study elaborates an integrative conceptual framework of tourism destination competitive productivity (TDCP) by blending established destination competitiveness frameworks, the competitive productivity (CP) framework and studies pertaining to big data and big data analytics (BDA) within destination management information systems and smart tourism destinations. Second, this study examines the drivers of TDCP in the context of the ongoing 4th industrial revolution by conceptualizing the destination business intelligence unit (DBIU) as a platform able to create sustained destination business intelligence under the guise of BDA, useful to support destination managers to achieve the tourism destination’s economic objectives.

In this work, the authors leverage both extant literature (under the guise of research on CP, tourism destination competitiveness [TDC] and destination management information systems) and empirical work (in the form of interviews and field work involving destination managers and chief executive officers of destination management organizations and convention bureaus, as well as secondary data) to elaborate, develop and present an integrative conceptual framework of TDCP.

The integrative conceptual framework of TDCP elaborated has been found helpful by a number of destination managers trying to understand how to effectively and efficiently manage and market a tourism destination in today’s fast-paced, digital and hypercompetitive environment. While DBIUs are at different stages of implementation, often as part of broader smart destination initiatives, it appears that they are increasingly fulfilling the purpose of creating sustained destination business intelligence by means of BDA to help tourism destinations achieve their economic goals.

This work bears several practical implications for tourism policymakers, destination managers and marketers, technology developers, as well as tourism and hospitality firms and practitioners. Tourism policymakers could embed TDCP into tourism and economic policies, and destination managers and marketers might build and make use of platforms such as the proposed DBIU. Technology developers need to understand that designing destination management information systems in general and more specifically DBIUs requires an in-depth analysis of the stakeholders that are going to contribute, share, control and use BDA.

To the best of the authors’ knowledge, this study constitutes the first attempt to integrate the CP, TDC and destination management information systems research streams to elaborate an integrative conceptual framework of TDCP. Second, the authors contribute to the Industry 4.0 research stream by examining the drivers of tourism destination CP in the context of the ongoing 4th industrial revolution. Third, the authors contribute to the destination management information systems research stream by introducing and conceptualizing the DBIU and the related sustained destination business intelligence.

This paper aims to investigate data elements, transfer, gaps and the challenges to implement data analytics in facilities management. The goal is not to search for a definite solution but to gather necessary information, understand the challenges faced and develop a proper foundation for future study.

This paper used a case study approach with a qualitative method. The case of the Georgia Institute of Technology was investigated by having a semi-structured interview with six relevant personnel. The recorded interview content was analyzed and presented based on six work processes.

Higher education institutions are taking initiatives but facing challenges in implementing data analytics. There were 36 software tools used to manage different aspects of facilities at Georgia Tech. Identified data elements and data processing indicated that major challenges for data-driven decision-making were inconsistency in data input and structure, the issue of interoperability among different software tools and a lack of software training.

The authors only interviewed individuals who work closely with data gathering, transfer and processing. Thus, the study did not explore the perspective of individuals in the leadership level or the user group level.

Facilities management departments in higher education institutions perform multi-disciplinary functions, including building automation, continuous commissioning and preventative maintenance, all of which are data- and technology-intensive. Managing this overwhelming amount of information is often a challenge, but well-planned data analytics can be used to draw keen insights about any aspect of facilities management and operations and assist in evidence-based decision-making.