Data‐Driven Marketing Processes: A Roadmap for Big Data Analytics Adoption in a Brazilian SaaS SME

Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.

In today’s digitalised era, the adoption of Industry 4.0 technology pillars can boost businesses by enhancing organizational performance. As one of the crucial pillars, big data potential has been receiving heightened interests from academic and practitioners. Small Medium Enterprise (SMEs) played a vital role in Malaysia and Big Data Analytics (BDA) is seen as a new strategic business tool that enables organizations to gain valuable insights for decision-making towards better performance. Most multinationals and larger companies may already be implementing BDA aiming at enhancing organizational performance, but many SMEs are still at the exploratory stage. Malaysia is heavily invested in accelerating the BDA adoption and innovation for SMEs. In 2020, Malaysia is encouraged by the government to become a BDA hub in ASEAN. The present study, drawing upon Technological, Organizational and Environment (TOE) model, proposes and investigates the influence of TOE factors on BDA adoption among Malaysian SMEs. The result will contribute to the understanding of the determinants affecting BDA adoption.

Purpose The rapid advancement of Generative Artificial Intelligence (Gen-AI) offers transformative opportunities for enhancing digital competence and management quality in small and medium-sized enterprises (SMEs). Given the challenges of a Volatile, Uncertain, Complex and Ambiguous (VUCA) business environment, SMEs face risks of losing competitive advantages to larger corporations. This study aims to explore Gen-AI trends and identify strategies that can support SMEs in building digital resilience and operational efficiency. Design/methodology/approach Through text analysis of patent data and using Gen-AI technology, this research examines potential AI applications to address SME challenges, such as labor shortages, productivity declines and operational inefficiencies. The study uses topic modeling and an OS-matrix framework to analyze trends in Gen-AI patents, aligning technological developments with SME-specific needs. Findings The study finds that Gen-AI technologies, such as automated content creation and predictive analytics, provide targeted solutions for key SME challenges. The OS-matrix framework reveals that specific Gen-AI applications can enhance SMEs’ adaptability and competitive positioning in dynamic markets. Research limitations/implications While this research underscores the potential of Gen-AI for SME digital transformation, limitations include a reliance on patent data and a lack of consideration of various industrial features of SMEs. Future research should expand data sources and apply findings across diverse SME sectors. Originality/value This study contributes insights by mapping Gen-AI advancements to SME needs under VUCA conditions. Therefore, integrating topic modeling with OS-matrix for aligning Gen-AI technologies to SME operational challenges, offers a strategic framework for digital adoption.

Big Data Analytics (BDA) provides an important resource for businesses seeking to enhance their performance and gain a competitive advantage, although not all organizations are adopting BDA techniques, and small and medium-sized enterprises (SMEs) in Jordan have been slow in this regard, despite being key players in any healthy economy, and the fact that BDA adoption can be facilitated by using the Technology Acceptance Model (TAM). The purpose of this study is to investigate the drivers of behavioral intention among managerial-level employees in Jordanian ICT SMEs to adopt BDA through a quantitative correlational research approach. The TAM questionnaire was used to gather data from 271 online survey participants in Jordan using Google Forms. The target group included management level staff working in small and medium-sized ICT firms (SMEs). Confirmatory factor analysis (CFA) was used to evaluate the research instrument's reliability and validity, and structural equation modeling (SEM) was utilized to test the study's hypotheses. The findings revealed that perceived usefulness, perceived ease of use, and perceived “privacy and security” significantly influenced managerial-level employees' behavioral intention to use BDA in their organizations. The research findings also supported the application of TAM, and the results of the investigation indicated that managerial-level employees would be willing to use BDA techniques providing they were perceived to be useful, user-effortless, and posed little concern about privacy and security. Overall, the current study's results demonstrate that the suggested model had good predictive power, 51% of the variance in behavioral intention, and was therefore capable of predicting managers' intentions to use BDA.

This paper provides a comprehensive review of how small and medium-sized enterprises (SMEs) can leverage big data for market analysis and customer insights, offering strategies to enhance competitiveness in today’s data-driven economy. The study explores the role of big data in helping SMEs understand customer behavior, preferences, and trends, enabling them to make informed decisions and improve customer satisfaction. It highlights the practical challenges SMEs face in adopting big data solutions, including limited resources and technical expertise, and suggests scalable, cost-effective strategies for data collection, storage, and analysis. Key utilization strategies discussed include the integration of cloud-based platforms for data management, the use of data analytics tools to process customer feedback, and the application of machine learning algorithms to predict customer behavior. The paper also examines case studies where SMEs successfully implemented big data solutions to achieve significant market insights and improve business outcomes, such as enhanced marketing strategies, personalized customer experiences, and streamlined operations. The review concludes that, despite the challenges, SMEs can effectively harness the power of big data by adopting tailored strategies that align with their specific business needs and resources. By utilizing big data, SMEs can gain a competitive edge, improve customer engagement, and ultimately drive business growth. The paper emphasizes the importance of training, data literacy, and the need for partnerships with technology providers to ensure the successful integration of big data into business processes.

NLP in SMEs for industry 4.0: opportunities and challenges

The digital revolution has fundamentally changed how businesses create value. Small and medium enterprises (SMEs), with their limited resources, must adapt to this new landscape to maintain a competitive edge through digital transformation (DT). This paper explores how SMEs can build sustainable competitive advantage (SCA) via DT. Using the WOS Core Collection database, we conducted a comprehensive review and bibliometric analysis, examining 1,856 articles on SCA, with a detailed focus on 57 that specifically address DT. The findings show that DT enhances SMEs’ innovation and dynamic capabilities, helping them overcome resource constraints and better respond to market shifts. Key digital technologies such as big data, artificial intelligence, and digital platforms are crucial in driving these results. Practical insights are also offered for SMEs managers to tailor digital strategies, optimize operations, and build SCA.

Micro, Small and Medium Enterprises (MSMEs) have a very important role in national economic growth. MSMEs are actually nothing new. However, there are still many rural communities who do not know what MSMEs are and what kinds of businesses are categorized as Micro, Small and Medium Enterprises. Besides that. Village communities who have become MSME actors experience several problems in developing MSMEs. This lack of knowledge about MSME development strategies is one of the factors causing MSMEs to be difficult to develop in rural areas. Cimaung Village has one MSME in the form of processed tempe. Where this tempeh is processed into various flavored tempeh chips. This is quite good because there has been innovation in the tempe processing. However, the marketing process is still limited, traditionally done in the physical market. Based on this, it is necessary to disseminate information regarding the introduction and strategies regarding the development of this MSME. This socialization aims to provide a brief introduction to MSMEs to people who are not perpetrators of MSMEs, and also provide information on what strategies can make MSMEs grow

Purpose. This research aims to investigate how small and medium-sized enterprises (SMEs) can strategically leverage key performance indicators (KPIs) to enhance their performance and competitiveness in the dynamic business landscape. Through a multifaceted research methodology, including secondary data analysis, case studies, comparative analysis, and future trends analysis, the study seeks to address the specific challenges faced by SMEs and provide actionable insights for optimizing performance through KPI utilization. Results. The findings of this research reveal the intricacies of SME performance challenges, ranging from financial constraints to operational complexities. Through a comprehensive examination of theoretical foundations and real-world case studies, the study demonstrates successful strategies used by SMEs in effectively utilizing KPIs. The comparative analysis identifies patterns and distinctions in KPI deployment across industries, enriching the understanding of tailored approaches. The forward-looking future trends analysis anticipates advancements in data analytics, artificial intelligence, and technological tools that will shape the landscape of SME performance measurement. Scientific novelty. This research contributes to the field by offering a nuanced exploration of challenges faced by SMEs and the transformative potential of KPIs within their unique operational environments. The comparative analysis and future trends exploration add a novel dimension, providing a forward-looking perspective on the evolving landscape of KPI utilization in SMEs. The research pioneers an investigation into emerging technologies and methodologies, positioning itself at the forefront of anticipating future trends in SME performance measurement. Practical value. The practical value of this research lies in its provision of actionable insights for SME leaders, policymakers, and researchers seeking to enhance the resilience and competitiveness of SMEs. By synthesizing theoretical foundations with real-world examples, the study offers practical guidance on addressing challenges and optimizing performance through KPI utilization. The exploration of tools, technologies, and future trends equips SMEs with the knowledge needed to navigate the intricate business landscape effectively.

Technology has revolutionized business operations around the world. There is an ever-increasing trend to digitize business operations around the world. It is not uncommon to see the application of Big Data Analytics (BDA) throughout a vast range of industries and organizations in today's extremely competitive world. However, BDA adoption in supply chain (SC) is less accepted and research in this area remains at its infancy. BDA is extremely useful throughout supply chain functions like manufacturing, distribution, procurement, and marketing. However, a sizable portion of sectors continues to hold divergent opinions regarding big data's alleged benefits. Moreover, there is not a lot of empirical studies that have been published that addresses the adoption of such a tool or better yet, that investigates what factors affect the decision to adopt BDA in supply chain management (SCM). This study aims to develop and extend technology acceptance model (TAM) that considers the critical factors that may affect BDA adoption in SCM by critically analyzing the existing literature. Upon reviewing the literature, it became evident that the main categories of factors that have been thoroughly examined and recognized to be crucial in comprehending BDA adoption and acceptability in SCM are the organizational- and individual-related factors, such as top management and computer self-efficacy.

Although Business process modelling is an increasingly popular research area for both organisations and enterprises due to its usefulness in optimizing resources management, business reengineering and business performance, it is not yet widely accepted in Small and Medium size Enterprises (SMEs), which are vital for sustainable economy development. The understanding of Business Process is an essential approach for an Organization to achieve set objectives and improve its operations. It has been able to show Business Analysts, and Managers where bottleneck exists in the system, how to optimize the Business Process to reduce cost of running the Organization, and the required resources needed for an Organization. This is clear for large scale organizations and enterprises but under research investigation for SMEs. Business Process Modelling (BPM) is a representation of the processes of the Organization. Several modelling techniques have been proposed and used to capture the characteristics of business processes. However, limited guidelines exist for selecting appropriate modelling techniques based on the characteristics of the problem and its requirements. This is even more accurate in the case of selecting proper BPM software tools to achieve those BPM modelling and simulation goals. This paper aims at presenting a comparative analysis of some relatively popular business process modelling techniques focusing on the associated software tools. The comparative framework proposed is based on the following major criteria: data flow capability, logical reasoning and understandability, specification of roles, and capability of simulation, flexibility and ease of use, simulation support and scope. However, the emphasis of this investigation is put on SME applicability of these tools and techniques. Therefore, the first goal is to define proper selection criteria for the application of BPM tools to SMEs. The proposed framework can serve as the basis for evaluating further modelling techniques and generating selection procedures focusing on a comparison of existing tools to implement these techniques properly for SMEs. This is to enable a potential user/modeller choose the right technique and tool in modelling critical SME Business Processes in order to analyse and optimize its operations.

Large language models (LLMs) are advanced artificial intelligence (AI) tools capable of generating human-like text and are increasingly used in education, clinical care, and research. Little is known about their use within oral and maxillofacial surgery (OMFS) training. This study investigates LLM usage trends, perceived value, and educational integration among OMFS residents in the United States. A national, anonymous cross-sectional survey was distributed to OMFS residents via program directors. It gathered demographic data, LLM usage patterns, applications, perceived limitations, and attitudes toward incorporating LLMs into formal education. Eighty-one residents responded, 79.0% (64/81) reported having used an LLM, and of that group, 96.9% (62/64) use ChatGPT. 51.9% (42/81) of respondents used LLMs at least monthly in residency; however, 97.5% (79/81) reported having received no formal LLM education during residency. Residents used LLMs for clinical decision support, board preparation, research, and career planning. Free-text responses revealed a wide spectrum of views. Some advocated for curricular integration and patient education applications, while others questioned the need for formal instruction. Some respondents supported integrating LLMs into curriculums and patient education while others questioned the need for formal instruction. LLMs are used frequently by OMFS residents for a variety of purposes. As AI and LLMs become embedded in healthcare, understanding how OMFS residents interact with LLMs is vital. These findings may guide curriculum development, fostering responsible and effective use of LLMs in surgical training and practice.

Indonesia is known as a nation that is abundant in natural and human resources. This abundant resource has become a great opportunity for improving the economy of the Indonesian people with Micro, Small and Medium Enterprises (MSMEs). MSME is an important effort in the national. At this time, MSMEs are intensively run in various regions in Indonesia including in the Penitentary. Since 2005, the level of sales of MSME products has increased. Thus, to penetrate and maintain MSMEs a marketing strategy is needed. Marketing strategy is the main thing in the business process, the following are important things that can improve and be able to improve MSME products, among others: strategy in making new changes that are made according to the needs and demands of consumers, supply strategies market needs with market expectations that will seek the opposite product, a strategy related to prices in accordance with market needs, the ability to adjust to economic and social problems of consumers, which also facilitates the ability of a product to provide value assessments to consumers, and SWOT analysis. With these strategies, it is expected that the prison entrepreneurship products produced by MSMEs will be able to improve their products in the local market. In this study using descriptive qualitative research and literature study data collection techniques. The research objective is to study how marketing management strategies to increase product sales of Micro and Small and Medium Enterprises in Penitentiary. It is hoped that this research has toeritis and practical benefits that will help in implementing marketing strategies in developing MSMEs in Lembaga Pemasyarakatan.

Micro, Small, and Medium Enterprises (MSMEs) in the clothing industry are one of the key pillars of the economy, contributing significantly to Gross Domestic Product (GDP) and employment. However, MSMEs face considerable challenges related to market competition, shifting consumer trends, and fluctuating demand. Advances in data analytics and machine learning offer solutions to improve sales forecasting accuracy, thereby supporting more effective business strategies. This study aims to develop a sales forecasting model based on Long Short-Term Memory (LSTM) tailored to the characteristics of clothing MSMEs in Indonesia. The research was conducted at Ananda Kids MSME in Purbalingga, using 30,885 daily transaction records collected over 23 months. The dataset included product categories, sales volume, and revenue, which were further processed through normalization, handling of missing values, and the addition of seasonal features. The LSTM model was designed with 128 neurons and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the LSTM model achieved high accuracy for certain product categories. The “Set” and “Children’s Fashion” categories recorded MAPE values below 10%, demonstrating the model’s effectiveness in forecasting stable sales patterns. In contrast, categories with high volatility, such as accessories, produced larger prediction errors. These results highlight that data quality and sales pattern stability are crucial factors in enhancing model performance. Overall, the study demonstrates that the application of LSTM holds significant potential in supporting strategic decision-making for MSMEs through more accurate sales forecasting. Beyond its practical contributions for business actors, the study also provides a basis for the development of digitalization policies for the MSME sector in Indonesia.

The purpose of this paper is to do a comprehensive analysis of Cloud based Big Data challenges and opportunity for SMEs in India. Big Data (BD) are big buzz words, and many large enterprises are making significant advances in their product development, customer relations, and increasing profitability by targeting specific customers. Therefore, it has generated interest for both the academic and industry world as it has the potential to enhance the decision making process by providing valuable insights. The large organisations are benefiting from using BD as a tool to gain a competitive edge over rivals by driving new revenue streams and improving internal operational efficiency. The large organisations are already investing as well leveraging BD. On the contrary, Small and Medium Enterprises (SMEs) in India are slow adopters of new technologies due to many factors and pose to lose their competitive edge on the world stage. SMEs can also leverage BD to gain a strategic competitive advantage but are resource poor in terms of investment in manpower and financial resources. Major challenges still exist in the adoption of BD especially in information technology architecture. Cloud Computing (CC) can eliminate the need to maintain expensive computing hardware, software, and networking resources. CC can also provide a solution to scalability issues, high speed performance, and fault tolerance for using BD. With CC usage, SMEs can take advantage of BD without significant investment in information technology and manpower. This research uses secondary data as a research data collection technique, which is its limitation. However, a quantitative research methodology using statistical surveys is planned to be conducted in the near future to understand the factors impacting on the adoption of Cloud based Big Data for Indian SMEs. This paper contributes to exploring and enhancing the understanding of the challenges and opportunities for SMEs in India for adopting BD. This study will also have both industrial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BD adoption in developing countries.
 Keywords: Cloud Computing (CC), Big Data (BD), Small and Medium Enterprises (SMEs), Challenges, Opportunities, India.