Smart Irrigation System Using Soil Moisture Sensor
Internet of things (IoT); Micro-controller; Over-Irrigation; precision irrigation; Sensor; Under-Irrigation
The impart of climatic change apparently affect accessibility of good water for agriculture irrigation system in addition to human dependency and demand on farm products for survival, increases water unavailability challenges in the farm affecting ecosystem when there is no balance between country food production capacity and population growth. Agricultural sector is challenged with unequal distribution of water in the farm plantations reducing food production strength, this is more severe in under-developing regions whereby require smart irrigation systems as promising solution to mitigate against threat of water distribution to farm products. This research work design and develop smart irrigation system with less expensive microcontroller components, implementing water distribution system using sensor captured information of soil condition, infusing precision irrigation system that calculate and supplies exact water require based on soil dryness level. The research adopts internet of things IoT technology where sensor will be used to monitor and capture soil information and control water distribution based on available soil dataset. The outcome of the research gives absolute control on over-irrigation and under-irrigation system that increases agricultural productions with advance technological means, precision irrigation system mechanisms reduces water wastage and ensure equal distributions. Multivariate soil type test will greatly enhance general acceptability of this concept in cross-functional regions. 2025 IEEE.
Okewumi, Peter Olufeyi; Iwendi, Celestine; Ojo, Olayinka Anthony; Aboutorabi, Negin; Sharma, Vandana; Lemeke, Collins
2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICTBIG68706.2025.11323957 https://www.scopus.com/pages/publications/105032857203?origin=resultslist
Okewumi P.O., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Iwendi C., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Ojo O.A., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Aboutorabi N., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Lemeke C., Centre of Intelligence of Things, University of Greater Manchester, Bolton, United Kingdom
Predictive Modelling of Microwave Link Failures Using Machine Learning and Deep Learning Techniques
Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Microwave Link Failure; Microwave radio; Quality of Service
Microwave radio links play a vital role in keeping mobile networks running, especially when it comes to backhaul-the part of the network that connects base stations to the core. Gradual failure in these links could disrupt services and cost providers a lot in both revenue and customer trust. In this study, we explore how machine learning can help predict such failures before they happen. Network performance data from a mobile network operator in Nigeria was collected, cleaned and used to achieve the purpose of the study. Four algorithms belonging to machine learning (ML) and deep learning (DL) were adopted and used for training the dataset and predicting link failures. Results show that the Long ShortTerm Memory (LSTM - a deep learning model effective for handling time-series data) performed best with prediction accuracy of 92%, distantly followed by others. These findings indicate that the LSTM is better in modelling temporal patterns in network behaviours. This study provides a practical framework for automating microwave link monitoring and maintenance, thereby reducing manual diagnostics, preventing outages, and improving service reliability. The proposed solution supports the integration of predictive intelligence into network operations, enhancing the quality of service and operational efficiency for telecom providers. 2025 IEEE.
Okewumi, Peter Olufeyi; Ojo, Olayinka Anthony; Iwendi, Celestine; Sharma, Vandana; Uwah, Salome Enoshi; Aboutorabi, Negin
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
https://doi.org/10.1109/ICRITO66076.2025.11241758 https://www.scopus.com/pages/publications/105029853032?origin=resultslist
Okewumi P.O., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Aboutorabi N., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
Solutions for time-fractional coupled nonlinear Schringer equations arising in optical solitons
Caputo derivative; Coupled nonlinear Schringer equations; Laplace transform; q-homotopy analysis transform method
In this work, an efficient novel technique, namely, the q-homotopy analysis transform method (q-HATM) is applied to obtain analytical solutions for a system of time-fractional coupled nonlinear Schringer (TF-CNLS) equations with the time-fractional derivative taken in the Caputo sense. This system of equations incorporate nonlocality behaviors which cannot be modeled under the framework of classical calculus. With numerous important applications in nonlinear optics, it describes interactions between waves of different frequencies or the same frequency but belonging to different polarizations. We first establish existence and uniqueness of solutions for the considered time-fractional problem via a fixed point argument. To demonstrate the effectiveness and efficiency of the q?HATM, two cases each of two time-fractional problems are considered. One important feature of the q?HATM is that it provides reliable algorithms which can be used to generate easily computable solutions for the considered problems in the form of rapidly convergent series. Numerical simulation are provided to capture the behavior of the state variables for distinct values of the fractional order parameter. The results demonstrate that the general response expression obtained by the q?HATM contains the fractional order parameter which can be varied to obtain other responses. Particularly, as this parameter approaches unity, the responses obtained for the considered fractional equations approaches that of the corresponding classical equations. 2021 The Physical Society of the Republic of China (Taiwan)
Chinese Journal of Physics, Vol-77, pp. 965-984.
https://doi.org/10.1016/j.cjph.2021.10.014 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127522396&doi=10.1016%2fj.cjph.2021.10.014&partnerID=40&md5=00653f6696c68f97f5bfb98c0d7b3ed8
Okposo N.I., Department of Mathematics, Delta State University, Abraka, PMB 1, Delta state, Nigeria; Veeresha P., Center for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Okposo E.N., Department of Mathematics, University of Delta, Agbor, PMB 2090, Delta state, Nigeria
Predictive Analysis of Voice Pathology Using Logistic Regression: Insights and Challenges
logistic regression; machine learning; voice pathology
Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individuals quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. 2025 by the authors.
Olakkengil, Divya Mathews; Aurelia, Sagaya P.
Engineering Proceedings;Volume;107;Issue;1;Article No.;28;
Multidisciplinary Digital Publishing Institute (MDPI)
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3390/engproc2025107028 https://www.scopus.com/pages/publications/105025653560?origin=resultslist
Olakkengil D.M., Department of Computer Science, CHRIST University, Bangalore, 560029, India, Department of Computer Science, Vimala College (Autonomous), Thrissur, 680009, India; Aurelia S.P., Department of Computer Science, CHRIST University, Bangalore, 560029, India
Fostering Artificial Intelligence in Small Businesses in Sub-Saharan Africa
AI; Business intelligence; business performance; Competition; customer retention; D63; family businesses; L10; L24; L25; M10; Nigeria; O30; O55; Policy; Robots; SMEs; Start-ups
In the present era, there are numerous consumer data sources, particularly through web services and terminals such as point of sales to better understand customers. More so, it has become challenging to gather and evaluate this vast amount of data manually. This chapter highlights the role of artificial intelligence (AI) in improving relationships with customers and explores the techniques used to analyze customers data in order to predict their demands and reach their satisfaction. The objective of this study was to empirically test the effect of AI on customer satisfaction of selected family-owned businesses listed in Nigeria, the most populous and one of the biggest economies in Africa. This implies that AI is a significant predictor of customer satisfaction. The study suggests that companies should employ AI solutions to improve operational efficiency. Through the implementation of AI, companies can optimize their operations, decrease expenses, and enhance their ability to adapt to market fluctuations and client needs. 2026 Timilehin Olasoji Olubiyi
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita
Contemporary Issues in Entrepreneurship Research;Volume;1;Issue;2;
https://doi.org/10.1108/S2040-724620250000022003 https://www.scopus.com/pages/publications/105024795547?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria, Faculty of Management and Social Sciences, West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India
The Effect of Digital Transformation Capability on Business Model Innovation in Industry 5.0: Evidence from Vietnamese Small and Medium Enterprises
AI; Business model innovation; C83; Competition; D22; digital transformation; Industry 5.0; M15; organization capability; Policy; risk management; Robots; SMEs; Start-ups; TOE framework
Digital transformation (DGT) in the industry 5.0 not only creates a comprehensive and sustainable socio?economic system but also helps businesses increase their resilience. This study aims to determine the effect of DGT capability on business model innovation (BMI) of Vietnamese small? and medium?sized enterprises (SMEs). The data were surveyed from 307 leaders working at SMEs in Vietnam and processed in two steps using SmartPLS 4.0 software. The results show that technology capability (TCC), organizational capability (OGC), strategy capability (STC), ecosystem capability (ECC), and risk management (RSM) have a positive effect on DGT. The results also show that DGT has a significant and positive influence on BMI, and the mediating role of DGT has also been confirmed. Afterward, Vietnamese SMEs have been advised to consider governance implications when enhancing their business models to align with the 5.0 industry. 2026 Tang My Sang
Contemporary Issues in Entrepreneurship Research;Volume;10;Issue;1;
https://doi.org/10.1108/S2040-724620250000022013 https://www.scopus.com/pages/publications/105024824627?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India
Prioritisation of Human Resource Strategies in the Digital Transformation Process of SMEs
AI; Competition; Digital transformation; human resources strategies; L26; M54; multi-criteria decision analysis; O33; Policy; prioritisation; Robots; SME digitalisation; SMEs; Start-ups
This chapter focuses on the importance of human resource (HR) management strategies in the digital business strategy for small- and medium-sized enterprises (SMEs). With the increasing influence of digital transformation that alters organisational structures and implements new technologies, SMEs have no other choice but to evolve. However, due to the scarcity of resources, it becomes very important for SMEs to allocate its HR appropriately and effectively. To support decision-making on strategies like employee reskilling, recruitment of digital talent, leadership development, and promoting a digital culture, this chapter presents a multi-criteria decision analysis (MCDA) framework. The prioritisation is crucial because not all the strategies can be executed at once, and SMEs should target the most effective ones in the long run, affordable, and relevant to their digital transformation agenda. This chapter illustrates methods for how SMEs can use the proposed prioritisation framework effectively. The hypothetical case study demonstrates the real challenges faced by SMEs during digital transformation and how MCDA assists leaders in selecting the most beneficial HR strategies. The case highlights the necessity of fitting strategies to organisational challenges to allow the customisation of training and leadership to align with business demands and maximise effectiveness while minimising costs. Upon use, this framework enables SMEs to comprehend and direct their digital transformation path more effectively. 2026 Tu? ?im?ek and Ahmet Bahad?r ?im?ek
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; ?im?ek, Tu?; ?im?ek, Ahmet Bahad?r
Contemporary Issues in Entrepreneurship Research;Volume;40;Issue;5b;
https://doi.org/10.1108/S2040-724620250000022031 https://www.scopus.com/pages/publications/105024787906?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; ?im?ek T., Ghane University, Turkey; ?im?ek A.B., Ghane University, Turkey
Talent Management Practices and Product Differentiation as Resilience Strategies in Africa
AI; Competition; Deposit money bank; L25; M10; O55; Policy; product differentiation; resilience strategies; Robots; senior employees and managers; SMEs; Start-ups; talent management practices
The challenge of weak product among other issues confronting resilience strategies of an organization has been attributed to insufficient investment in talent management. This study investigated talent management practices as resilience strategies from a product differentiation perspective among selected deposit money banks (DMBs) in Nigeria. A survey research design was adopted, and the population was 2, 169 senior employees and managers. A stratified random sampling technique was adopted, and a sample size of 425 respondents was determined using Rao soft online sample calculator. A validated structured and adapted questionnaire was used in gathering data while Cronbachs alpha reliability coefficients for the constructs ranged from 0.929 to 0.942 with a response rate of 85.6%. Findings indicated that talent management components had a significant effect on product differentiation (adjusted R2 = 0.779, F(5, 366) = 261.564, p < 0.05). The conclusion was that talent management practices significantly affect product differentiation. It was recommended to the management of the DMBs in Nigeria particularly and Africa in general to prioritize talent management practices as resilience strategies to advance product differentiation in their various units of operations. 2026 Solomon Olusegun Adeoye and Johnson Ashiemamho Egwakhe
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Adeoye, Solomon Olusegun; Egwakhe, Johnson Ashiemamho
Contemporary Issues in Entrepreneurship Research;Volume;3;Issue;2;
https://doi.org/10.1108/S2040-724620250000022039 https://www.scopus.com/pages/publications/105024805363?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Adeoye S.O., Department of Business Administration and Marketing, Babcock University, Nigeria; Egwakhe J.A., Department of Business Administration and Marketing, Babcock University, Nigeria
Impact of Information and Communication Technology on E-Business of Small- and Medium-Scale Enterprises in Nigeria
AI; Competition; E-business; enterprises; gross domestic product; ICT; L26; M54; medium; O33; Policy; Robots; SMEs; Start-ups
The researchers examined the impact of information and communication technology (ICT) and e-business adoption on the performance and growth of small- and medium-scale enterprises (SMEs) in Nigeria. Using a survey research approach, the research integrates quantitative data from a survey of 30 SMEs across various sectors with the link of a well-structured questionnaire sent to a carefully determined sample of SMEs within Abuja and Lagos. The findings revealed that SMEs in Abuja and Lagos make use of ICTs for e-business, of which some of the tools are computers, internet, tablets, smartphones, videoconferencing, and more. Findings also showed that the adopted ICTs are effective in the conduct of the business activities of the SMEs. ICTs as utilised by the SMEs were found to be useful and therefore are suited for recommendations to other business people. The researchers conclude that ICTs are important in the business architecture of Nigeria and also recommend that strategic investments in ICT infrastructure, targeted training programmes, and supportive government policies are crucial for maximising the benefits of e-business for SMEs in Nigeria. These findings are of significance to business owners and the nation in general as they provide a comprehensive understanding of the critical role of ICT in the e-business architecture of Nigeria and how that can also contribute to the fostering of sustainable growth of gross domestic product (GDP). 2026 Mary Agamalafiya, Kelvin Inobemhe and Tsegyu Santas
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Agamalafiya, Mary; Inobemhe, Kelvin; Santas, Tsegyu
https://doi.org/10.1108/S2040-724620250000022029 https://www.scopus.com/pages/publications/105024833244?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Agamalafiya M., Bingham University, Nigeria; Inobemhe K., Department of Mass Communication, Glorious Vision University, Nigeria; Santas T., Department of Media Management, University of Religions and Denominations, Pardisan, Iran, Department of Journalism and Media Studies, Faculty of Communication and Media Studies, Nasarawa State University, Nigeria
Sustainable Growth in SMEs With Generative AI: Reaping Rewards, Tackling Challenges, With Human Insights
031; AI; Competition; entrepreneurial orientation; entrepreneurial resilience; Generative AI; L26; M13; Policy; Q01; Robots; SMEs; SMEs; Start-ups; sustainability
Generative artificial intelligence (Gen-AI), which refers to autonomous creation of content, is an area of artificial intelligence (AI) that presents a lot of potential for small- and medium-sized enterprises (SMEs). This technology can boost productivity, help to communicate and attract customers, and stimulate innovations, thus becoming a major asset for SMEs in the increasingly competitive environment. This chapter provides insight into the role of Gen-AI in leveraging the innovativeness in SMEs and identifies key strategies for successful implementation, including enhancing employee skills, fostering effective leadership and company culture, promoting collaboration, and building strong external partnerships. It highlights crucial tactics for successful execution; additionally, it also offers insight into how Gen-AI leads to economic and social benefit. The study discusses data privacy and security issues as the majority of AI applications depend on massive informatics, and protection of data ethical concerns, as well as the appropriate training of the workforce, has to be accurately addressed to ensure that AI fits into the SME business strategy. Lastly, this study also demonstrates how the adoption of AI in SMEs is providing an upper hand to tackle competitive scenarios. 2026 Umme Ara and Shivani Pandey
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Ara, Umme; Pandey, Shivani
Contemporary Issues in Entrepreneurship Research;Volume;16;Issue;6;
https://doi.org/10.1108/S2040-724620250000022023 https://www.scopus.com/pages/publications/105024782414?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Ara U., Department of Commerce, CMP Degree College, University of Allahabad, India; Pandey S., Department of Commerce, CMP Degree College, University of Allahabad, India
Disruptive Technology and Performance of Manufacturing SMEs as Moderated by Technological Innovation in South-West Nigeria
AI; Competition; Disruptive technology; economic growth; firm performance; L26; M54; manufacturing SMEs; O33; Policy; Robots; SMEs; Start-ups; technological innovation
The manufacturing sector is critical and significant to economic growth, offering dynamic benefits for industrial transformation. However, many manufacturing small- and medium-sized enterprises (SMEs) face challenges such as limited capacity and technical expertise, leading to reduced profitability, market share (MS), and overall business performance. In todays competitive business environment, these SMEs are particularly vulnerable to the effects of disruptive technology (DT), which poses both challenges and opportunities. This study examined the impact of DT on the performance of selected manufacturing SMEs in Lagos and Ogun States, Nigeria, with technological innovation (TIN) as a moderating factor. A survey research design was employed, targeting a population of 2, 603 manufacturing SMEs (504 in Ogun State and 2, 099 in Lagos State). Using Cochrans formula, a sample size of 436 was determined, and stratified random sampling was used to select respondents. Data were collected through a structured questionnaire, yielding an 88.3% response rate. Descriptive and inferential statistical analyses revealed that DT significantly influenced SME performance, with TIN strengthening this relationship (adjusted R2 = 0.913, 0.932, and 0.935 across models, p < 0.05). The findings suggested that adopting DT is significant for improving the long-term performance of manufacturing SMEs. It is recommended among others that management should prioritize the integration of DT components to enhance overall business outcomes and competitiveness. 2026 Olamide Titilayo Ayodeji, Timilehin Olasoji Olubiyi, Titilayo Doris Ibikunle, Israel Olabode Aroge and Abiodun Richard Obisanya
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Ayodeji, Olamide Titilayo; Ibikunle, Titilayo Doris; Aroge, Israel Olabode; Obisanya, Abiodun Richard
Contemporary Issues in Entrepreneurship Research;Volume;22;Issue;4;
https://doi.org/10.1108/S2040-724620250000022033 https://www.scopus.com/pages/publications/105024812799?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria, Department of Business Administration, School of Management and Social Sciences, West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Ayodeji O.T., Department of General Studies, School of Communication and Information Technology, D.S. Adegbenro ICT Polytechnic, Nigeria; Ibikunle T.D., Department of Entrepreneurship Management, School of Management Sciences, Olusegun Agagu University of Science and Technology, Nigeria; Aroge I.O., Department of Project Management, School of Management Sciences, Olusegun Agagu University of Science and Technology, Nigeria; Obisanya A.R., Department of Public Administration, School of Management Sciences, D.S. Adegbenro ICT Polytechnic, Nigeria
Digital Marketing Strategy for SMEs in India and South East Asian Countries in Industry 5.0: A Literature Review
AI; Competition; Digital marketing; economic growth; India; L1; M3; M31; M310; Policy; Robots; SMEs; SMEs; Southeast Asia; Start-ups
Lower level of income, underdeveloped industrial bases, lower standards of living and a lack of access to modern technology are the basic challenges of any developing economy. Expansion of the small business sector plays a pivotal role in growing the economies of developing countries and reducing unemployment. Digital marketing is an online platform to promote small- and medium-sized enterprises (SMEs) to reach a global audience without a physical presence in multiple locations. This is particularly beneficial for SMEs in developing countries, where access to international markets may otherwise be limited. The present chapter is a review of global digital marketing strategies and explores how developing nations, particularly India and Southeast Asian countries, can benefit from them and can adopt the advanced strategies to establish the SME sector. It is found that while digital marketing offers substantial benefits for SMEs in India and Southeast Asia, its adoption is influenced by a range of factors, including digital infrastructure, government support and access to digital literacy programs. SMEs in India and Southeast Asia should overcome the existing barriers and continuously adapt to the evolving digital landscape to realize the potential of digital marketing. 2026 Sabita Bhagabati
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Bhagabati, Sabita
Contemporary Issues in Entrepreneurship Research;Volume;9;Issue;3;
https://doi.org/10.1108/S2040-724620250000022016 https://www.scopus.com/pages/publications/105024777288?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Bhagabati S., Faculty of Commerce and Management, Bhattadev University, India
Cyberloafing and the Future of Business Productivity in Developing Economies: Insights From India
AI; Competition; Cyberloafing; cyberloafing activities; cyberslacking; D12; employee productivity; goldbricking; innovation; L81; M31; Policy; Robots; SMEs; Start-ups
Online activities are increasing every day, and cyberloafing is a relatively new phenomenon. Scholars are increasingly focusing on the adverse effects of digitization on human lives in personal and professional contexts. Cyberloafing is one such effect and digitization-related workplace behavior that has garnered attention in both academic and mainstream media. This chapter aims to understand the specific loafing activities and its effects on employee productivity in India, one of the most populated and largest developing economies. To achieve this goal, the research involved conducting field research among employees in both private and public sectors in Haryana, India, with a primary focus on those employed in the medium-sized education and healthcare business sectors. The data were collected from 300 respondents through questionnaires and analyzed using partial least squares structural equation modeling (PLS-SEM) to determine the impact. The studys results suggest that engaging in cyberloafing activities like maintaining social networks, playing online games, online dating, shopping online, and using the internet for non-work purposes while at the workplace has a modest yet notable adverse impact on employee productivity. To put it differently, when employees indulge in social, leisure, and virtual activities, it has a detrimental effect on their productivity. These research findings offer valuable insights to organizations regarding the types of cyberloafing activities employees are involved in and how they influence employee productivity, encompassing factors like attendance, work quality, performance capability, and personal aspects. 2026 Divya, Timilehin Olasoji Olubiyi and Mahabir Narwal
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Divya, D.; Narwal, Mahabir
Contemporary Issues in Entrepreneurship Research;Volume;14;Issue;1;
https://doi.org/10.1108/S2040-724620250000022019 https://www.scopus.com/pages/publications/105024781428?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria, West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Divya D., Kurukshetra University, India; Narwal M., Kurukshetra University, India
Strengthening Legal Frameworks for Small Businesses Integrating Artificial Intelligence in Consumer Products: Navigating Nigerian Regulations in the ERA of Industry 5.0
AI; Artificial intelligence; Competition; consumer protection; D12; entrepreneurs; L81; legal frameworks; M31; Policy; Robots; small and medium enterprises; small businesses; SMEs; Start-ups
This chapter examines the legal frameworks governing artificial intelligence (AI) integration in consumer products by small businesses in Nigeria, within the context of Industry 5.0. The mission of this research is to address a critical knowledge gap on the legal challenges faced by small businesses in this rapidly changing technological landscape. The complexity of existing legal frameworks, difficulties comprehending and adhering to regulations, striking a balance between innovation, consumer protection and ethics, the effect of regulatory uncertainty on business growth and AI adoption and the adequacy of current legal frameworks are some of the key concerns. It aims to provide a comprehensive understanding of the regulatory environment, assist small businesses in navigating regulations, guide the creation of policies and contribute to academic discourse on AI governance in developing economies. Using a mixed-methods approach combining legal analysis, case studies and surveys of 100 small business owners and legal experts, it has revealed significant challenges for small businesses in navigating the legal landscape of AI integration. The findings refute the idea that small Nigerian enterprises are uninterested in complying with regulations by highlighting a strong association between legal guidance and successful regulatory navigation. This chapter speculates that lack of awareness and understanding of legal frameworks may be a major barrier to entry for small businesses in the AI-enabled consumer products market. This research contributes to literature on AI regulation in developing economies and provides practical insights for policymakers, legal practitioners and small business owners in Nigerias emerging Industry 5.0 landscape. 2026 Rosetta Okiemute Isiavwe, Mary-Ann Onoshioke Ajayi, Folajimi Olayiwola Jejelola and Olayiwola Owoade Oladele
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Isiavwe, Rosetta Okiemute; Ajayi, Mary-Ann Onoshioke; Jejelola, Folajimi Olayiwola; Oladele, Olayiwola Owoade
Contemporary Issues in Entrepreneurship Research;Volume;119;Issue;1;
https://doi.org/10.1108/S2040-724620250000022034 https://www.scopus.com/pages/publications/105024806624?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Isiavwe R.O., College of Law, Bowen University, Nigeria; Ajayi M.-A.O., College of Law, Bowen University, Nigeria; Jejelola F.O., Faculty of Law, University of Ibadan, Nigeria; Oladele O.O., College of Law, Bowen University, Nigeria
Resilience Strategies and Sustainability in Business
AI; Business; Competition; L26; O14; Policy; Q31; Q56; resilience; Robots; SMEs; stakeholders; Start-ups; strategies; sustainability
Businesses must develop robust resilience strategies to guarantee durable sustainability in a volatile global environment marked by rapid technological advancements, climate change, and socio-economic uncertainties. This chapter explores the intersection of resilience and sustainability in business, focusing on how companies can adapt to disruptions while fostering sustainable practices that contribute to their longevity and success. Resilience strategies involve the capacity of a business to anticipate, prepare for, and respond to disruptions, whether they stem from economic downturns, natural disasters, or shifts in consumer behavior. These strategies encompass risk management, adaptive leadership, and the integration of flexible operational models. By building resilient infrastructures and cultivating a culture of innovation, businesses can navigate challenges more effectively, maintaining continuity and minimizing losses during crises. Sustainability refers to adopting practices that meet current needs without conceding the strength of future generations to meet theirs. Sustainable business practices include reducing environmental impact, supporting social equity, and guaranteeing economic viability. Incorporating sustainability into core business strategies addresses global challenges like climate change and enhances brand reputation, customer loyalty, and long-term profitability. The synergy between resilience and sustainability is essential for modern businesses. By embedding sustainability into resilience strategies, companies can create value beyond financial performance, contributing to environmental stewardship and social well-being. This holistic approach positions businesses to thrive in an uncertain future, balancing immediate resilience with sustainable growth. As businesses increasingly recognize the significance of these strategies, they are better prepared to withstand disruptions and achieve long-term success in a rapidly evolving world. 2026 Godwin Ayodeji Nwogu
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Nwogu, Godwin Ayodeji
Contemporary Issues in Entrepreneurship Research;Volume;19;Issue;1;
https://doi.org/10.1108/S2040-724620250000022009 https://www.scopus.com/pages/publications/105024783800?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Nwogu G.A., Department of Adult Education, Federal University Oye-Ekiti, Nigeria
Quantum?Inspired Strategies and Business Resilience in Industry 5.0: A Study of Small Businesses
AI; Business resilience; Competition; industry volatility; L25; M10; M15; O32; organisational culture; Policy; Robots; SMEs; Start-ups; superposition capability; technological readiness
The discussion of the study revolves around the assessment of the various quantum strategies influencing the resilience of a business in Industry 5.0. Quantum strategy implementation (QSI), technological readiness (TR), superposition capability (SPC), innovation capacity (IC), leadership style (LS) and organisational culture (OC) were identified as independent variables which might have a significant influence on business resilience (BR), the dependent variable for the study. Moreover, industry volatility (IV) is taken as a mediating variable. Questionnaire was framed involving the 5-point Likert scale, and data collection was carried out. Among 250 responses, 32 was either incomplete or invalid. Final determination of sample size was 218. Stratified random sampling technique was adopted to ensure different industries and geographical locations were covered. Pilot testing with the 30 initial responses was carried out for better clarity and validity before full deployment. SPSS 25 and AMOS 23 were the statistical tools involved to analyse the data. Reliability and validity check, correlation, regression, confirmatory factor analysis, mediation analysis and structural equation modelling (SEM) are the statistical methods used in the study. Highest correlation is seen among IC and BR. Regression results show that IV and LS have an insignificant impact on the BR. Results of SEM show the index values are well within the recommended value range indicating a good model fit. With the results, small businesses may adapt to rapid technological changes and increase resilience. 2026 Saladi Jaswanth Seshasai, R. Vijay Raja and G. Kumar
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Seshasai, Saladi Jaswanth; Raja, R. Vijay; Kumar, G.
https://doi.org/10.1108/S2040-724620250000022011 https://www.scopus.com/pages/publications/105024826503?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Seshasai S.J., Faculty of Management, SRM Institute of Science and Technology, India; Raja R.V., Faculty of Management, SRM Institute of Science and Technology, India; Kumar G., Faculty of Management, SRM Institute of Science and Technology, India
Resilience: Future Trends and Challenges in Small and Medium Enterprises
agile business models; AI; Competition; digital transformation; Industry 5.0; L26; M54; O33; organizational culture; Policy; Robots; SME resilience; SMEs; Start-ups; sustainability; technological advancements
Small- and medium-sized enterprises (SMEs) are essential drivers of innovation, employment, and economic growth in the global economy. However, the rapid technological advancements associated with Industry 5.0 introduce unprecedented challenges and vulnerabilities for these businesses. This chapter delves into the resilience of SMEs, with a focus on the future trends and challenges that will shape their survival and growth in this ever-evolving environment. By leveraging secondary data from reputable databases such as Scopus and Web of Science, this study synthesizes the available literature to deliver a thorough analysis of SME resilience. In addition to digital transformation, this chapter discusses the growing importance of sustainability in building resilience. It advocates for the adoption of sustainable practices that mitigate environmental risks while aligning with the increasing demand for corporate social responsibility. This chapter also underscores the necessity of fostering a resilient organizational culture capable of withstanding economic and political uncertainties. By leveraging data from previous studies, this chapter offers practical recommendations for enhancing SME resilience. It can be a critical resource for policymakers, business leaders, and researchers seeking to understand and address the factors that will determine the future success and sustainability of SMEs in the age of Industry 5.0. 2026 Mohit Sharma, Rishi Chaudhry, Raj Kumar, Nitika Malik and Kuldeep Chaudhary
Olubiyi, Timilehin Olasoji; Longe, Olumide Babatope; Susela Devi, K. Suppiah; Sinha, Mudita; Sharma, Mohit; Chaudhry, Rishi; Kumar, Raj; Malik, Nitika; Chaudhary, Kuldeep
Contemporary Issues in Entrepreneurship Research;Volume;42;Issue;18;
https://doi.org/10.1108/S2040-724620250000022026 https://www.scopus.com/pages/publications/105024826329?origin=resultslist
Olubiyi T.O., West Midlands Open University, Nigeria; Longe O.B., West Midlands Open University, Nigeria; Susela Devi K.S., Sunway University, Malaysia; Sinha M., Christ University, India; Sharma M., Institute of Management Studies and Research, Maharshi Dayanand University, India; Chaudhry R., Institute of Management Studies and Research, Maharshi Dayanand University, India; Kumar R., Institute of Management Studies and Research, Maharshi Dayanand University, India; Malik N., Institute of Management Studies and Research, Maharshi Dayanand University, India; Chaudhary K., Institute of Management Studies and Research, Maharshi Dayanand University, India
Rendering support for the empowerment of rural women to overcome life impediments
Empowerment; Livelihood; Rural; Self-help group; Status; Women
The socio-cultural and economic landscape of rural India is not totally conducive for women. Surveys and reports in this regard would suggest the continuous existence of gender-based discrimination and its negative effects on the status and livelihood of rural women. Recognizing this situation, the Centre for Social Action (CSA) has pioneered its efforts to sensitize and involve youth (especially students) in the mitigation of rural issues and has been supporting women in select villages by promoting Self Help Groups (SHGs). These SHGs, in turn, organize capacitybuilding and empowerment programmes for the women to enhance their livelihood and socio-economic well-being. The present study is designed to understand the impact of CSA's intervention on the status and livelihood of women in the select project sites. Towards this end, we collected data from a sample of 150 women beneficiaries of CSA's initiative using a structured interview schedule. The study used a mixedmethod design. The study's outcome indicates a positive correlation between women's participation in CSA initiatives and their status and livelihood improvements. The results are encouraging that they would help one formulate effective models similar to this one for the empowerment of women. 2024 Nova Science Publishers, Inc.
Models for Social Responsibility Action by Higher Education Institutions, pp. 87-98.
Om Prakash L.T., Department of Sociology and Social Work, Christ University, Bengaluru, India; Shaji A., Centre for Social Action, Christ University, Bengaluru, India
Co Adaptation Vs Signal Altering Regularization Layer in Deep Learning: A Trade Off Analysis via Node Redundancy and Transfer Learning
Deep learning; Gaussian Dropout; Node redundancy; Overfitting; RDrop; Regularization; Regularization Layer; Transfer learning
This study investigates the trade-off between incorporating regularization layers-such as Dropout, R-Drop, and Gaussian Dropout-and the phenomenon of co-adaptation in deep learning models. While regularization is designed to enhance generalization by disrupting hidden layer activations and reducing overfitting, it may also introduce node redundancy, potentially diminishing the model's capacity to learn efficiently. Conversely, co-adaptation, though often considered undesirable, may help preserve beneficial internal representations that contribute to learning generalizable data patterns-particularly in transfer learning scenarios-where regularization may inadvertently hinder such learning. Using the CIFAR-10 dataset, this study conducts an empirical analysis of how various regularization strategies influence neuron redundancy and downstream transfer performance. The results indicate that, although regularization effectively controls overfitting, excessive distortion in hidden representations can impair the model's ability to generalize across tasks. These findings provide insights into the need for balanced regularization strategies that maintain useful structure while minimizing detrimental redundancy. 2025 IEEE.
INDISCON 2025 - IEEE 6th India Council International Subsections Conference, Proceedings;
https://doi.org/10.1109/INDISCON66021.2025.11252043 https://www.scopus.com/pages/publications/105030165687?origin=resultslist
Omathil G.G., Christ University, Department of Computer Science, Bangalore, India; Sreeja C.S., Christ University, Department of Computer Science, Bangalore, India
Tevarude Ana /