Abstract

Due to the industrial revolution and, thereby with, the emergence of Industry 4.0, there has been an impact on the marketing of business firms, enabling them to digitally transform and adopt Marketing 4.0 practices supported by smart technologies. One of these is Big Data Analytics (BDA), which enables a firm to improve its marketing practices. The main objective of this study is to evaluate how integrating big data technology improves the marketing strategies of Indian FMCG companies operating in a dynamic and highly competitive market distinguished by its size and diversity, constantly changing consumer behaviour, and Marketing 4.0 trends. This study adopts a systematic approach to coding an interview with seasoned Indian FMCG professionals to explain the capabilities of Big Data in molding marketing practices. A framework is constructed based on the same. The theoretical and practical implications are discussed to visualize its applicability in business. Further, the Resource-based View (RBV) theory is discussed for describing the insights into the role and adoption of Big Data by Indian FMCG firms for enhancing performance and achieving competitiveness, along with demonstrating its benefits for decision-making, customer loyalty and internal capability assessment.

Introduction

With technological advancements, industries now utilize innovative technologies that bolster a range of corporate activities and procedures, profoundly influencing the internal operations of these firms. (Cammarano et al., 2023, Singh et al., 2022b). The fourth industrial revolution, known as Industry 4.0, which is driving this technological change for business firms, serves as a bridge between intelligent products and adaptive processes and has a significant impact on both business models and production processes (Rosário & Dias, 2022; Salkin et al., 2018). Industry 4.0 enables the digital transformation of firms, which is further impacted by different internal and external factors related to the firm. The digital transformation is required to improve customer experiences and retain customers (Kitsios et al., 2021). Despite their implementation challenges and hazards, these advancements increase market analytics, cost, channel management, client connections, professional skills, and organizational procedures (Pascucci et al., 2023). With Industry 4.0, there is a steady trend towards understanding the consumers, which includes their needs, behaviours, storytelling, and anticipation (Lampropoulos, 2023; White & Pathak, 2023). These are the core components of marketing techniques that have evolved into marketing 4.0 (Yakut, 2022). As an outcome of the emergence of trends like Big Data (BD), social media, corporate social responsibility, and e-commerce, the shift to Marketing 4.0 is accelerating (Guven, 2020). Marketing 4.0 is driven by the convergence of offline and online marketing strategies and incorporates technical advancements to provide better flexibility (Rathod et al., 2020; Kotler et al., 2017). The improvement of the consumer experience, the induction of repurchase intention, and brand promotion are some of the significant achievements of Marketing 4.0 (Dash et al., 2023). Consumer usage patterns of social media, online platforms, search engines, contextual interactions, and community influences are some of the areas the companies are looking into. Companies use the information mainly for promotional and consumer engagement activities. The consumers are seen to be transformed into advocates through marketing 4.0, where a sense of community building takes place, resulting in a meaningful engagement of the marketers and consumers (Gau, 2019).

Conclusion

This study discusses and examines the role of BD and how an FMCG firm can adopt BD to improve its overall performance and attain a competitive position in the market. This research is the first of its kind to explore the implications of BD in the Indian FMCG sector. The relationship among BD, marketing practices and FMCG from in Indian perspective is a unique contribution to this paper. In addition to its usefulness, the study explores the crucial roles that automation and decision science play. This study discusses the benefits the firm achieves for accepting BD in the business. The study reveals some of how BD helps in decision-making for FMCG firms. For Indian FMCG firms, BD is crucial to value creation and achieving business operations’ scalability. FMCG organizations are using BD and analytics solutions more and more since they help create value, scale corporate operations, and increase output in today’s competitive market. BD makes it possible to analyze consumer preferences and behaviour at each transaction stage with a business. FMCG companies may now provide personalized experiences through targeted messaging, unique offers, and channel-specific communication optimization. BD also offers in-the-moment customer insights via sentiment, preference, and behaviour research, which aids FMCG companies in creating goods that enhance customer experiences and provide them with the necessary competitive edge.

Additionally, BD facilitates effective market campaigns through real-time predictions, data processing, and analysis. BD can also be strategically used through automation, new business model innovation, decision science, and risk management. Furthermore, customer segmentation based on BD makes marketing techniques more effective, and real-time customer insights, like sentiment analysis and tracking of consumer preferences and behavior, help organizations respond quickly to market changes.

Abstract

The application of big data technology to global tax management is becoming increasingly widespread. China has been implementing increasingly mature technologies for tax governance using big data systems in recent years. By collecting data through web scraping on the earliest implementation times of big data tax administration in various provinces of China, we explore the relationship between big data tax administration and corporate bank credit in emerging markets. Our results show that big data tax administration enhances firms’ ability to obtain bank loans. Mechanism tests indicate that big data tax administration improves the quality of corporate information disclosure, facilitating access to bank credit loans. We find that big data tax administration improves the corporate financing environment, enhancing the efficiency of resource allocation in the credit market.

Introduction

The digital economy has expanded rapidly around the world. Driven by continuous upgrades in Internet functionality and the widespread application of big data, significant changes are occurring in governments, corporate business models and in people’s daily lives (Chen and Srinivasan, 2024). Taxation departments provide a good example of such changes, as big data technology is expanding the traditional auditing model. “Big data tax administration” combines big data with tax auditing; it involves acquiring big data from Internet platforms and integrating and comparing multiple sources of data (Bassey et al., 2022). Its implementation, which has become a new trend in national tax governance, reflects the modernization of national governance capabilities and systems within the tax system and significantly enhances the efficiency of governments’ tax collection (Canares, 2016). A key question is whether, looking beyond strict tax enforcement, modernized tax governance can guide firms to improve the quality of their information disclosure, thereby enhancing the efficiency of resource allocation in capital markets? Examining this question yields insights relevant to governments worldwide who are implementing governance based on big data technology.

Bank loans are an important financial resource and whether they are allocated in a timely and appropriate manner is an important issue from both theoretical and practical perspectives. An extensive body of research discusses the various factors that influence firm credit, including corporate characteristics and the policy environment. However, few studies explore whether innovations and modernizations within the tax governance system can improve the financing environment for firms. Studying the effects and mechanisms of big data tax administration on corporate bank credit enhances the understanding of the social effects of modernized governance and provides empirical evidence for the motivational impact of “tax administration with data” on firms.

Conclusions and implications

The widespread adoption and application of the Internet and big data have reduced the cost of information flow, and the multi-party storage and sharing of big data has gradually become a new form of social capital. The use of big data by tax authorities not only improves the capabilities and efficiency of tax collection but also promotes the rational allocation of financial capital at the national level and facilitates enterprise financing. We investigate the effects of big data tax administration on corporate credit and find that implementing big data tax administration (1) has a positive impact on corporate bank credit; (2) improves the quality of corporate information, thereby promoting corporate credit and (3) has a more pronounced effect on enterprises with stronger (vs. weaker) financial constraints. The conclusions of our study are beneficial because they enhance academic understanding of the economic consequences of big data tax administration, and also have practical significance for tax authorities actively seeking external forces to improve tax collection procedures. Based on our theoretical and empirical analyses, we put forward the following policy recommendations.

First, the government should enhance cooperation between tax authorities and third-party online platforms, synchronously strengthening tax administration under the “delegation, management and service” framework. We show that by enhancing information collecting capabilities, big data tax administration improves the quality of corporate information disclosure, thereby optimizing corporate financing efficiency. In the context of the digital transformation of enterprises and the increasing application of big data in society, tax inspection departments should make full use of the data at the societal level, engaging in cooperation with online platforms and online assessment agencies. By utilizing the correlation between multi-party data and the data provided by enterprises, tax inspection departments can strengthen their verification of corporate information. This will help eliminate the corporate practices of inflating projects and manipulating information disclosure, and encourage and guide firms to improve their information quality.

Abstract

As big data becomes an apparent challenge to handle when building a business intelligence (BI) system, there is a motivation to handle this challenging issue in higher education institutions (HEIs). Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources. This paper reviews big data and analyses the cases from the literature regarding quality assurance (QA) in HEIs. It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper. The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data. The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’ QA systems. This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Introduction

Quality assurance (QA) in higher education (HE) is emerging as the higher education institutions (HEIs) find it difficult to monitor their levels of quality of services. Governments impose national QA standards in order to achieve the minimum level of quality of services provided by these institutions. In the Kingdom of Saudi Arabia (KSA), Education and Training Evaluation Commission (ETEC) imposes, through its National Centre for Academic Accreditation and Assessment (NCAAA), the QA standards that all HEIs in the KSA are required to comply with for accreditation [1]. To measure compliance with these standards, in 2018, NCAAA developed 23 key performance indicators (KPIs). In 2022, NCAAA introduced a revised version of KPIs. This revised version comprises a total of 17 KPIs, and all HEIs are mandated to monitor and report on compliance. This evaluation process of compliance with these KPIs should take place during the forthcoming accreditation cycle for HEIs [2].

In addition, the increasing amount of data generated from traditional sources and through social media plays negative influence on business intelligence (BI). There is a desire to build low-cost data platforms that will be able to handle this increasing amount of data. This is sometimes known as the big data challenge [3,4].

In this paper, different BI architectures are presented. These BI architectures encompass different BI technologies that HEIs may adopt, taking into consideration their capabilities and specific requirements of HEIs. A proposed architecture is also presented to show how the big data challenge can be addressed to deal with huge amounts of data that the QA system might require particularly if the data is being linked to social media. The recent trend of measuring students’ satisfaction is through students’ comments posted on social media.

Conclusion

This paper showed how the HF-HEQ-BI framework can be applied to develop a BI prototype dashboard for monitoring quality in HEIs and a dashboard has been developed from the framework. The paper has also outlined different BI architectures that HEIs may adopt to handle the big data challenge while monitoring QA. These architectures show different configurations of BI systems that HEIs may adopt based on their requirements and available resources. The development process of the BI dashboard was discussed to show how HF-HEQ-BI FUT may be used to determine the requirements to be presented in the dashboard. HF-HEQ-BI FUT assists in determining the required visualisation for each KPI to be displayed in the dashboard. FUT is customisable and can be adapted by users to reflect the requirements of individual HEI. FUT shows how decision-makers in HEIs may select various visualisation dashboards while monitoring QA compliance regarding NCCAA standards.

A prototype dashboard was developed to illustrate the application of the framework and also to evaluate the HF-HEQ-BI framework. The developed dashboard shows a monitoring display of the mandatory NCAAA KPIs, institutional specific KPIs, additional KPIs, and social media analytics. The social media analytics presented in the dashboard included the sentiment analysis of data drawn from Twitter through Twitter API. Additionally, the proposed dashboard allows the dashboard users to represent additional KPIs related to performance monitoring in the institutions.

The developed dashboard based on the HF-HEQ-BI framework shows how data retrieved from social media is presented in summarised diagrams to handle the big data challenge. The prototype dashboard is dynamic and KPIs can be added or modified easily based on the changes in regulations or institutional directions of the strategic plans. Our future work will focus on the evaluation of the proposed dashboard to meet the big data challenge.

Abstract

Purpose
This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach
This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings
The paper examines the multi-wave implementation of big data analytics in a Taiwanese bank.

Originality/value
The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Challenges with big data analytics

Despite its potential, the adoption of big data analytics is hindered by numerous challenges. Financial consideration and human resources are the two main factors obstructing its implementation. Studies suggest many banks are yet to realize the full benefits and returns of leveraging big data. Most organizations also lack adequate managerial and technical skills to gain value from big data usage. Indeed, many banks unfamiliar with this new practice lack the capabilities to embed big data analytics into their operations and processes. A difficulty firms face is reducing a large and diverse dataset derived from several sources into easy-to-interpret results that can support decisionmaking. It becomes critical for firms to employ experienced data scientists who can uncover insights that can help improve the bank’s operation and performance. Banks also face many non-technical challenges in big data applications. Organizations need the buy-in from organizational members from all ranks-from senior management to lower levels across various functions. The adoption of new technologies necessitates changes in operating practices which may result in organizational resistance. Security, privacy and government regulations can also impact big data applications.

Impact of big data analytics in bank A

Using insights from customer clustering and product affinity models, Bank A was able to adapt its offerings across channels. For example, the organization used cluster affinity scores to target specific marketing campaigns for those customers. Bank A implemented these changes in three waves across ten months. The results showed that the different divisions (e.g. wealth management and personal finance) improved their response rates using the two data analytics methods.

Abstract

Today’s digital economy presents HRM with new challenges related to changes in the “language” of business. In fact, firms rely ever more heavily on employees skilled in understanding both the language of business operations and that of business-related data. However, often employees do not have the combined skills of data analytics and management. With the relative ease with which data is available from digital sources and the increase in computational power, employees are often called to make data driven decisions that affect firm standard operations, functions, and eventually performance. Consequently, value creation through (big) data does not solely depend on a firm’s ability to analyze big data, but it also requires management capabilities such as leadership, training, talent management, employee upskilling and the creation of an evidence-based, data-driven culture (Mcafee et al. 2012; Rodgers et al. 2023).

1 The process

We advertised the open call for papers through our networks and at major conferences such as the European Academy of Management annual meeting. Here in particular, we encouraged contributors to the track on “New forms of work, data analytics and big data” to consider submitting their work. Overall, we received a total of 41 submissions. Of those, 17 manuscripts (i.e., 41%) were desk rejected, primarily because they did not fit with the aims of the Special Issue. Another 12 manuscripts (i.e., 29%) were evaluated negatively and thus eventually rejected. One manuscript was withdrawn. Ultimately, we accepted 11 manuscripts (29%). Before final acceptance was granted, the handling guest editor shared the manuscript with the guest editorial team for final approval and potential further comments. In our decision making we tried to strike a balance between conceptual and empirical works, both quantitative and qualitative. We hope that our selection of manuscripts will find the interest of RMSC’s readership.

2 The contributions

Our Special Issue starts off with three literature review articles that, to some extent, lay the foundations for the topics discussed in some of the empirical works also present in this collection. These contributions are then followed by two conceptual contributions. Next the section of empirical papers has a focus on HRM and its connection to AI and data analytics. We conclude with the topic of innovative behavior. In the following sections we briefly introduce the articles.

Abstract

The Internet of Things (IoT) provides to the client an effective technique for communicating with the Web world through ubiquitary object enabled networks. The rapid progress in IoT connected devices creates a huge amount of data in a second from personal and industrial devices. This information should be utilized to help business and functional objectives. Thus, there is an urgent requirement for adopting cloud computing, big data, and artificial intelligence techniques to enable storage, analytics, and decision making. In this article, we focused our consideration to integrate Cloud Computing, Big data and Artificial Intelligence technique with the Internet of Things devices. Cloud computing, Big Data, Artificial Intelligence, and IoT are different techniques that are already part of our life. Their adoption and uses are expected to make them more comprehensive and make them essential components of the future Internet. The Internet of Things (IoT) is a system of interconnected gadgets, digital or mechanical machines that are given exceptional identifiers and the capacity to move information over an organization without expecting human-to-human or human-to-pc collaboration.

Introduction

With the wide-spread discovery of techniques in the current digital era, increasing physical entities are interconnected to the Internet of Things (IoT) devices. In recent years IoT technologies are applied with different techniques such as Artificial Intelligence, Big Data, and Cloud Computing. Artificial Intelligence (AI) is a technique which has the ability to compute a huge amount of task that is usually done by a human. Artificial Intelligence uses different learning techniques to facilitate automatic rules and regulations for decision making. Artificial intelligence is divided into two different modules such as learning module and predicate module [1]. The learning module is used for effective data collection, training, and data modeling. And the predicate module is used to take action on the current situation. The flow and storage of exponentially increasing data are easily managed by Artificial Intelligence (AI). The integration of cloud computing and IoT are also two different technologies that assume a vital part in our daily life. Cloud computing and the Internet of Things are merged together is expected to break both current and future internet which we called as new paradigm CloudIoT [2]. In the era of the internet which plays a fundamental role in cloud computing, it seems to be represented as a medium or the platform through which many different cloud computing services are accessible or delivered its services. If you are thinking that the internet as a virtual “space” for connecting users from over the globe, it is like a cloud, sharing information by using the internet. Cloud computing is the trending technology in the daily life of everyone which provides on-demand web services such as networking devices, data storage, servers, and applications. It provides higher flexibility and cost efficiency while users try to use cloud computing resources and applications.

Conclusion

The Internet of Things is a wide field and contains incredible and different variety of applications. The main aim of IoT is to provide a facility of exchange information and synergic performance between devices and peoples via global machine-to-machine (M2M) networks. Due to M2M network, exchange of information between the devices creates an exponential amount of data. It is impossible to manage or keep secure these generated personal or organizational data in local devices. The IoT techniques need centrally storage space for personal or industrial data. To avoid these problems, in this paper we have introduced the integration of Cloud Computing, Big Data and Artificial Intelligence techniques with the Internet of Things devices. The integration of cloud computing with IoT techniques stores the information centrally generated by the IoT devices. And the collected data which is centrally stored in cloud computing is managed, processed, and analyzed by the Big Data Techniques. To extract the high volume of IoT data in real-time, processing needs machine learning and AI algorithms.

Abstract

This study examines the best practices for the optimization of the corporate digital brand name by taking into consideration customers’ behavioral big data and web analytics. In the first stage of the study, customers’ satisfaction big data have been extracted with the assistance of a web scrapping tool from TripAdvisor for 189 hotels in Hubei province, and behavioral data from Hubei province hospitality websites have been gathered with the assistance of web analytics platforms for 5.7 million website visitors for the last 18 months. In the second stage of the research, those data have been statistically analyzed including descriptive, correlation, and regression analysis. Then, a fuzzy cognitive map has been created to present the intercorrelation between the parameters and two optimization scenarios have been developed for digital brand name and customer satisfaction. Finally, an Agent-based model has been created in order to simulate the customers’ behavior in the corporate website and TripAdvisor. The results indicated that hotels in Hubei province need to invest less in social media advertisements than search engine advertisements in order to achieve a competitive advantage and improve their digital brand name. Additionally, hotels need to develop their websites with more engaging content to maintain the customer on the corporate website for more time in order to optimize customer satisfaction in contrast to healthcare and libraries websites.

Introduction

The COVID-19 pandemic forced all destinations to introduce travel restrictions, with airplanes on the ground and hotels, restaurants and travel agencies closed, making the Hospitality, Travel and Tourism (HTT) sector one of the worst affected with tremendous economic losses (Obembe et al., 2021). The World Tourism Organization has released the 11th and last one report on travel restrictions on 26th November 2021 (UNWTO, 2021), stating that 98% of all destinations have some kind of travel restrictions in place. As of 2 November 2022, where there have been 628.035.553 confirmed cases of COVID-19 globally (WHO, 2022), most countries have lifted their travel bans. This new situation, while it offers a pandemic relief, it simultaneously provokes the public opinion to be split over this policy (Stoeckel et al., 2022). Given the great number of confirmed cases, this dichotomy raises concerns on how risk perception contributes to the adoption of new patterns of behavior after severe outbreaks familiarity (Sakas et al. 2021).

Zajenkowski et al., (2020) state that during novel crisis, such as the Covid-19 pandemic, people perceptions are more likely to be influenced by situational cues, rather than personality traits. Although the research is geographically limited in one country, it is an important outcome which further supports the research of Moya et al., (2020) regarding customers’ extensive behavioral change as a consequence of crisis situations. As the public opinion has changed throughout the stages of the pandemic (Mahdikhani, 2022), so does the digital behavior of online users (Sakas et al., 2022a), pushing companies over the technology tipping point (Nasir et al., 2022). Especially for the HTT ecosystem, tourist behavior has been changed due to travel and mobility limitations, psychological and economic factors (Marques Santos et al., 2020). Cognitive, personality, and affective factors would predict travel behavior and travel preferences during the COVID-19 pandemic (Morar et al., 2021).

Conclusions

The purpose of the current paper is to provide evidence on the impact of customers’ online behavior on corporate digital branding and offer guidelines on how companies could optimize customers’ satisfaction by leveraging the dynamic of big data analytics. Gathering and analyzing the digital behavior and journey of million users can provide fruitful information and valid insights on businesses within the HTT sector as the literature suggests (Buhalis & Volchek, 2021; Lv et al., 2022). However, there is little empirical evidence on how marketing managers could benefit from the information overload (Saxena & Lamest, 2018). The authors of the present research attempt to analyze the data collected from 189 hotels and 5,7 million visitors, generated from websites and social platforms, and interpret the results into actual guidelines so as to develop an effective brand communication strategy.

Abstract

1 Introduction

2 Literature review

3 Research methodology

4 Discussion

5 Conclusions

References

Abstract

     This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures.

Introduction

     Huge competition and fluctuating demand patterns increase the data generation in the supply chain (SC) (Arunachalam et al., 2018). This pushes the companies to adopt the supply chain analytics (SCA) so as to gain competitive advantage (Davenport and O’dwyer, 2011; Shafiq et al., 2020). Adopting the SCA practices improves the accuracy and overall performance of the SC. As firms are already using statistical methods for decision making in one way or another, some practitioners are against the implementation of SCA because of the time and cost incurred in adoption of these solutions (Kusiak, 2006; Trkman et al., 2010). Implementing SCA in businesses is a tricky task because of the uncertainty associated with the data and the changing requirements of the customer (Handfield and Nicholas 2004; Liberatore & Luo, 2010; Huner et al., 2011; LaValle et al., 2011; Manyika et al., 2011).

Conclusions

     The MCDM methods support the decisions given in the business, which contains many criteria that have to be satisfied to make any decision. The uniqueness of this study for the best big data practice to improve the overall performance of the supply chain is that, the qualitative and quantitative factors with different scales are combined in the same technique, also we need to know the risk concept in the analysis and last, TODIM method is applied to this problem. Although there are many decision-making methods that are based on complex calculations, the proposed framework can be applicable easily by the companies in various industries in order to make decisions based on many criteria. To get more accurate data and efficient decisions in the decision-making process, we can implement fuzzy logic to the TODIM method and Pythagorean Fuzzy TODIM so that we can reduce uncertainty and ambiguity in the decisions taken by the companies. The criteria chosen in this study are relevant to the Indian retail supply chain. These criteria can vary across the industries. Hence, the dominance values change with criteria which are different for different industries like for some industry cost may be the main criteria but for others demand management is the main criteria, so it varies from industry to industry. Hence, obtained priorities may not be generalized.

Abstract

Introduction

Disclosure risk as a legal issue

How does big data differ compared to “traditional” data?

The ethical use of big data

Principle 1: Public good and avoiding causing harm

Principle 2, the risk of re-identification and attribute disclosure

SDC for big data? Implications and recommendations

Concluding remarks

References

Abstract

Big data holds great potential for research and for society, large volumes of varied data can be produced and made available to researchers much faster compared to ‘traditional’ data. Whilst this potential is recognized, there are ethical concerns which users of big data must consider. With the volume and variety of information in big data, comes a greater risk of disclosure. Researchers and data access services working with highly detailed and sensitive, secure data have grappled with this for many years. The sector has developed both ethical frameworks and statistical disclosure control techniques which could be utilized by those working with big data. We discuss the challenges, present some of the frameworks and techniques and conclude with recommendations for secure data access of big data.

1. Introduction

There is great potential in big data, the potential for making new discoveries made possible for the first time by vast amounts of data. With the emergence of new forms of data, has come new ways of thinking about and analyzing data, requiring new platforms for analysis. Big data is generated in higher frequencies than other forms of data, such as from social surveys and national censuses that can take months even years to be made available to researchers. For researchers used to navigating the various, sometimes lengthy, application processes for other forms of data, the scale and speed of production and the ease of access make big data an attractive prospect to those with the computational skills and power to handle it.

There is no single consensus on what makes data ‘big’, but a common way of thinking about big data is that it consists of multiple data sources that have been combined or explicitly linked to create a data source of significant size. Big data can be thought of as having several key characteristics: volume, variety and speed (Soria-Comas and Domingo-Ferrer, 2016; Schroeder, 2014). Volume is self-explanatory, it refers to the size of the dataset, formed from multiple sources of data combined through some linking variable. These are much larger than social survey datasets and require significantly more computational power (Sfetcu, 2019). The combination of data sources leads to the second characteristic – variety. A big data set will contain information on many different aspects of people’s lives. For example, digital trace data might combine information on retail transactions, location histories collected using a mobile phone’s GPS, websites visited and so on. The final characteristic, velocity refers to the increased speed of data collection and processing.

That big data offers great potential is certain, but it is not a unanimously positive picture. Like with all forms of data there are challenges and concerns with big data, and these have been widely discussed 1. Many ethical issues have been raised in relation to big data, around issues of consent and privacy. These ethical issues are complex, and a complete overview is not attempted here. Neither is the goal to provide a complete instruction guide on how to use big data ethically. The focus of this article is on disclosure risk (the risk that individual data subjects will be identified) as a key ethical issue of big data and exploring what lessons can be applied from the experiences of secure data access services.

Abstract

1. Introduction

2. Literature Review

3. Research Methodology

4. Study Results

5. Conclusion

References

Abstract

     Big Data as Service (BDaaS) provides an alternative solution to overcome the challenges when a business embarks on adopting and implementing a big data strategy. Although BDaaS provides a viable alternative for SMMEs, there is limited research on the specific factors that affect the adoption of BDaaS in SMMEs. The study aims to explore the factors that affect the adoption of BDaaS in SMMEs. The study adopted the TOE framework as the lens to explore the technological, organizational, and environmental factors affecting the adoption of BDaaS in SMEs. The study results indicate that SMMEs need to consider technological, organizational, and environmental factors when adopting BDaaS. The study contributes to the body of knowledge on factors that affect the adoption of BDaaS in SMEs based on the study results. Despite the limitation of the study of not being empirical (used secondary data), the study contributes to the body of knowledge on factors affecting the adoption of BDaaS in SMEs. In addition, the study may stimulate further research on factors that affect the adoption of BDaaS in SMEs using other research methods.

Introduction

     The concept of digital transformation is generally related to the desire to leverage new technologies to gain and sustain a competitive advantage in a digital age, where products and services are required to be delivered both online and offline [1]. The digital transformation implemented successfully requires not only the acquisition and deployment of technological resources, but it is a managerial aspect too including matters such as the re-design of processes in the business, training, and investing in resources such as organizational capabilities and human resources [2]. Due to businesses’ newly discovered asset, big data, businesses now build their transformation models focusing on leveraging the organization’s big data. Through the adoption of Big Data, SMMEs can generate value from their data insights and as a result, gain significant competitive advantage, generate increased profits, and increased operational efficiency. Big Data as Service provides an alternative solution to overcome the challenges when a business embarks on adopting and implementing a big data strategy. BDaaS vendors utilize cloud platform solutions to provide to businesses for cost-cutting and efficiency in managing the business’ big data [3]. The study explores factors affecting the adoption of Big Data as a Service in SMEs. The structure of the rest of the paper is as follows: Section 2 Literature Review, Section 3 Research Methodology, Section 4 Study Results, and Section 5 Conclusion.

Conclusion

     This research study conducted a systematic review of the literature to explore and conceptualize BDaaS factors affecting adoption in SMMEs. The research objectives were to explore factors affecting the adoption of BDaaS in SMMEs. The results show a decrease and an increase of published of articles on the factors affecting BDaaS adoption in SMMEs during period under-review. The European region had the highest number of published articles on the factors affecting BDaaS adoption in SMMEs. The qualitative research method was the most used method on reviewed published articles during period under-review. The study results revealed the most of the technological factors (security/privacy concerns, complexities, costs, and resources) affect BDaaS adoption in SMMEs. The study results indicate that organizational factors (organizational readiness, employee knowledge, financial costs, and infrastructure) affect BDaaS adoption in SMMEs. Lastly, the study results show that three environmental factors (legislation, vendor capabilities, and competition) affect BDaaS adoption in SMMEs.

Abstract

     In the era of knowledge economy, the competition between countries and enterprises is increasingly manifested in the competition of talents and education system. In this article, aiming at the drawbacks of the traditional HRM model of university teachers, we design and construct a management innovation model and decision-making model based on intelligent big data analysis. This article introduces DM technology. It also introduces the related knowledge of DM and the analysis and design process of HRM decision system. In this system, DM technology is used to analyze and process the existing data, predict the future situation, and provide auxiliary support for decision-making. Through simulation, the decision-making accuracy of this model can reach 95.68%, which is about 10.02% higher than other systems. It has certain practicability and reliability. This article makes a useful attempt for the application of DM technology in HRM. The research in this article is expected to play an important decision-making reference and service support for HRM of university teachers and further promote the development of HRM innovation of university teachers.

Introduction

     In the modern environment, the demand for knowledge and intellectual capital in society is stronger than in any previous era, which leads to the aggravation of talent shortage. In some ways, economic growth mainly depends on the quality of labor force. Therefore, human resources are increasingly becoming strategic resources for economic and social development [1]. The competition between countries and enterprises is increasingly manifested as the competition of talents and the competition of educational system. As the quality of teaching in institution of higher learning directly determines the level of knowledge and personal quality of talents, the society has paid more attention to the management of college teachers who train senior talents [2]. However, the traditional management mode is increasingly showing many drawbacks. For example, the ideas of managers and managed people lag behind; the management mode is relatively closed and rigid; operating mechanisms such as competition mechanism, incentive mechanism, interest mechanism, responsibility mechanism, and flow mechanism are not well introduced into the management of teachers; the overall quality of teachers needs to be further improved; and teachers’ resources have not been effectively allocated and developed. College teachers are the direct undertaker of college production activities and the main body and core of college human resources. The quantity, quality, and structure of teachers’ resources directly determine the educational quality and school-running efficiency of institution of higher learning and then affect the national human resources and the level of economic and social development [3]. The purpose of human resource management of teachers in institution of higher learning is to mobilize the enthusiasm and creativity of every faculty member to the maximum extent and make more contributions to the school. Under the background of economic globalization, in order to realize the scientific task of human resource management in institution of higher learning, we must actively and effectively reform and innovate the human resource management of college teachers.

Conclusions

     Human resources, in comparison to other economic resources, have a distinct purpose, subjective initiative, and unique creativity. Traditional human resource management modes and information acquisition methods limit the development of human resource management in higher education institutions. In light of the growing drawbacks of traditional HRM, this article investigates various issues in HRM in higher education institutions using intelligent big data analysis technology and related HRM and DM theories. This article develops and implements an innovative mode and decision-making model for university teachers’ human resource management. This system employs DM technology to analyze and process existing data and forecast future events and provide decision-making support. The outcome of the experiment is in line with the hypothesis. This model has a decision-making accuracy of 95.68%, which is 10.02% higher than other systems. This system can significantly reduce the workload of intelligent scheduling of university teachers’ human resources based on big data analysis as well as the time it takes to fuse and calculate stage task data. It is feasible and practicable in some ways. This research has yielded some results, but due to my limited knowledge and research time, there are still some areas that could be improved. The model structure will be improved in future work, as will the model performance, and a quantitative analysis scheme will be proposed to solve the HRM problems of university teachers.

Abstract

     The purpose of this study is to understand public awareness of cybersecurity-related issues and discussions during the COVID-19 pandemic. Employees and confidential organizational data are vulnerable to cyber-attacks as a result of the pandemic, which has raised concerns about cyber security about the new normal of working from home. The public’s main sentiments and aspects related to cyber security concerns have been mined from tweets on the microblogging social media website Twitter. Sentiment Analysis and Topic Modeling techniques have been applied to understand the perspectives, emotions, and themes discussed by people. The analysis reveals people are becoming more aware of cybercrime-related concerns and are more positive than negative in combating the challenge of cybercrime. The paper also highlights the main themes revealed by Topic Modeling.

Introduction

     The Internet has now integrated into human lives, with about 3 billion users all over the world. Billions of dollars are generated globally by this global network [13]. In the current scenario, the majority of the activities and interactions, whether commercial, cultural, governmental, nongovernmental, individual, or organizational, are all performed in cyberspace [1], causing unprecedented growth in cyberspace [2]. The innovations in the field of the internet have made it more accessible, user-friendly, and affordable for most people [34], thus integrating it into human lives profusely. The lives of humans on earth are intertwined in cyberspace and any turbulence or insecurity impacts different facets of human lives [18]. Such a tremendous growth in information sharing provides a platform to those with malevolent intentions and raises cybersecurity issues [15]. Due to COVID-19 pandemic role of technology has escalated in the professional lives with remote working becoming new normal. However organizations are still not able to provide cybercrime safe remote working. In today’s contemporary world social media is a great source of exchanging information, opinions, and discussions by providing a universal platform. Twitter, a microblogging platform has 206 million daily active users enabling users to create and post messages and follow each other [4]. People post their experiences, opinions especially emotions via electronic media. Therefore, microblogging websites like Twitter are a momentous source of data followed by opinion mining and sentiment analysis [17, 29, 30].

Discussion and Conclusion

     The Twitter data analysis explains the awareness and perception of the users on cybersecurity-related issues. Users, in general, discussed the issues like ransomware, malware, and cyber-attacks in different parts of the world. Since the study took place in November and December 2021, the people were well aware and perhaps more trained to combat the cyber-attacks than initially when the COVID-19 outbreak took place as implied by a large number of positive and neutral tones of the polarity of sentiments The Sentiment Analysis further enlightened that people were having ‘trust’ as the major emotion in tweets. This may be the people and their organizations have leveraged better tools and techniques to mitigate the online scams. Also, they were more accustomed to working remotely. Moreover, high anticipation emotion also predicts that people were hopeful that the situation will improve and in future, the cyber security will enhance as companies will take better practical approaches to protect their devices and people. Although, the high value of ‘Fear” in the sentiment analysis depicts that there are issues people are concerned about and the cyber attacks in Russia, ransomware, hacking, and malware show people are still afraid of cybercrimes. The cyber criminals keep on deploying new techniques to scam the devices and people which require a more precautious take on the issues. But the sentiment analysis reveals as people became more accustomed to the new normal and improved vigilant employment of technology the sentiments were more neutral or positive.

Abstract

Introduction

Literature Review

Case Review of Retail Sector in UAE

Conclusion

References

Abstract

     Despite research on big data analytics being conducted in past, there are no studies focused on the opportunities i.e., growth prospects and its associated challenges in specific industries in UAE. In this study, big data analytics were used in SMEs retail sector in UAE region to assess the use of these technologies. Companies can use these to gain new understandings from their customers and suppliers perspective utilizing big data analytics techniques to take informative decisions on pricing, inventory management, payment solutions and marketing decisions. Big data can be used to help SMEs foresee the needs and preferences of their target audiences. The focus of this study is on SMEs since these are the pillar of every economy and can make quick adjustments to productivity changes. It aims to analyze and identify major chances and threats of Big Data and propose the best ways for SMEs to utilize it for improving their efficiency and business practices.

Introduction

     The COVID-19 viral pandemic has had a significant impact on corporate growth across several industries. Small and midsized enterprises (SMEs) are among the most economically disadvantaged, a fact that has prompted governments of all views to enact unprecedented measures to help small businesses – such as tax holidays, soft loans, and implementation of digital technology to increase operational efficiency and ultimately revenue generation. On a regional level, several regulators and authorities have taken proactive measures to mitigate the impact on small enterprises. The ability of a small business to pivot and adapt to a new reality reflects its financial vulnerability, and it has been seen that businesses are innovating in the face of adversity – whether it’s changing existing services, catering to the requirements of newly housebound consumers, or adjusting product offerings to support government efforts to combat the coronavirus [1]. Most bigger firms are undergoing much-needed digital transformations on a small or large scale in order to stay competitive and be very customer-centric. Economic growth is bolstered by a growing SME sector, which decreases unemployment and poverty as well as encourages entrepreneurial activity and thus making it important for any country [1]. The Emirate is working to transition from an oil and gas dependent GDP to a free – market economy focused on trade, services, and manufacturing [2]. The contemporary market economy, on the other hand, relies heavily on SMEs, which account for over 40% of Dubai’s GDP [3].

Conclusion

     There has been a dramatic increase in the amount of data generated by both real and virtual worlds in the last few decades. Companies of allsizes use big data toolsto manage vast amounts of data and obtain comprehensive information that advances their business intelligence and allows them to make smart decisions. The retail business is one of the most diverse businesses, and some retailers are among the UAE’s top corporations. Simultaneously, the retail industry has many tiny “mom-and-pop” establishments, making it vital to many families’ livelihoods. Big Data Analytics has the potential to help these people. By analyzing data and creating correlations between diverse things, Big Data Analytics can enable these stores to discover new changes in their units. Finally, Big Data Analytics must be recognized as a crucial aspect in making the SMEs retail sector’s business a success story, considering all the facts, figures, and prospects covered in the article. If SMEs want to take advantage of Big Data’s capabilities, they must first undergo a cultural transformation. This necessitates them investigating data-handling tools and procedures outside of their smaller organizations, as well as being prepared to actively use Big Data in their decision-making processes. They must be prepared to dive in and explore the ever-expanding ocean of data that awaits them out there. Sayings alone, however, will not be enough to get SMEs on the path to data analytics. All shareholders, including domestic and worldwide regulators, the IT diaspora, the industry group, and the digital transformation community, face a difficult dilemma as a result of the previously identified issues. Further investigation and research can be done by doing empirical study and collecting raw data from various SMEs in retail sector in U.A.E. And then meaningful information can be extracted for further analysis to demonstrate the feasibility and reliability of the model mentioned in the paper