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Prioritisation of Human Resource Strategies in the Digital Transformation Process of SMEs
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 -
Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited. -
Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited. -
Prioritizing Factors Affecting Customers Satisfaction in the Internet Banking Using Artificial Intelligence
Internet banking has revolutionised the way customers interact with their banks, providing them with convenient access to a wide range of financial services from the comfort of their homes or mobile devices. Customer satisfaction the success of an endeavour is contingent upon a vital component internet banking Service provision, as it pertains directly impacts customer retention and loyalty. This research explores the application of artificial intelligence (AI) techniques, specifically random forest and convolutional neural networks (CNN), to prioritise the factors that affect customer satisfaction in internet banking. The study begins with data collection from a diverse sample of internet banking customers, including demographic information, transaction history, and customer feedback. These may include the ease of navigation, the response time of the platform, and the level of trust in the bank's security measures. Furthermore, convolutional neural networks (CNN) are utilised to analyse unstructured data such as customer feedback and reviews. By applying natural language processing techniques, CNN s extract sentiment and topic information from customer comments. This approach can ultimately lead to improved customer retention and loyalty, ensuring the long-term success and competitiveness of internet banking platforms. In conclusion, this study showcases the power of AI, specifically Random Forest and CNN, in prioritising factors affecting customer satisfaction in internet banking. It highlights the significance of using both quantitative and qualitative investigations in order to attain a comprehensive comprehension of customer sentiments and preferences in the digital banking landscape. 2024 IEEE. -
Prioritizing the Essentials: The MBA Aspirants Dilemma
Objective decision-making while choosing an appropriate college for a Master in Business Administration (MBA) is only half-done. It is critical that the student be able to find the best placement at the end of the course by acquiring the most critical skills/specializations affecting placements and involves data-driven decision-making based on past placement trends. Viti and Vania have done their preliminary selection, of ABC College for their MBA course, based on the colleges credence quality. However, they are trying to understand the key success factors (KSFs) affecting placements at ABC to focus their next two years on getting most placement-ready. Having been provided with the placement details of the outgoing batch, they are looking to analyze the data to discover the most critical parameters affecting placements. NeilsonJournals Publishing 2023. -
Priority based prediction mechanism for ranking providers in federated cloud architecture
Cloud computing is a growing and excellent technology, as exponentially increasing the interest among users to utilize cloud applications; they need to depend on any one of the particular service provider. Now a days number of service providers also rapidly increasing in wide range, this leads ambiguity and distrust among the users. In this paper, enhanced broker based federated cloud architecture is proposed to resolve the selection of service provider issue using grading techniques and results proved that better performance improvement than single service provider selection. This broker architecture also addresses to selects the appropriate service provider automatically in the federated cloud architecture for the users submitted requests by previous experience with help of Bayesian network model. The former one implemented through concept of grade system. It is constructed for categorizing the providers based on the level of available resources. Grade and grade values distributed by applying the grade distribution algorithm for distinguishes the components. Total grade values computed for every service provider and sorted using quick sort algorithm to grade the cloud service providers. Priority based feedback decision tree technique added with this for separates similar grade cloud service provider in the selected list. Second Bayesian network model also used to rank the cloud service providers according to the previous performance of the providers with customers. Probability of satisfied customers feedback calculated for individual Service Measurements Index of Cloud Service Providers. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Priority-driven Unbalanced Transportation Problem (PUTP) to obtain better Initial Feasible Solution
In this paper, we tackle the Priority-driven Unbalanced Transportation Problem (PUTP), a scenario where total demand exceeds total supply. An innovative algorithm, the Penalty-driven Priority-driven Unbalanced Transportation Problem (PPUTP) is introduced to solve this challenge. PPUTP allocates supplies to high-priority demands by computing penalties and sequentially addressing the most penalized demands, thereby ensuring priority demands are met efficiently. A comparative analysis with Vogel's Approximation Method (VAM) across various problem sets ranging from 5x5 to 50x50 dimensions demonstrates the efficiency of our algorithms. PPUTP consistently shows lower percentage increments from the optimal solution, indicating its robustness in providing near-optimal solutions. This study highlights the importance of algorithm selection based on problem set dimensions and complexity in Priority-driven Unbalanced Transportation Problem, with PPUTP emerging as a versatile and robust solution across various scenarios. 2024 IEEE. -
Privacy breach perceptions and litigation intentions: Evidence from e-commerce customers
This paper examines the formation of litigation intentions among e-commerce customers under the privacy breach due to the influence of antecedents such as vulnerability, social risk, privacy dispositions, effectiveness privacy policy, awareness of data management and moderators such as privacy control beliefs, efficacy in coping and litigation complexity. A structural equation modelling analysis revealed that reasons for privacy breach perceptions are customer dispositions about privacy, anticipated vulnerability due to privacy breach, and social risk related to personal information disclosure. The control beliefs and coping skills of customers under privacy threat positively moderate litigation intentions. Similarly, the task complexity of litigation significantly reduces litigation intentions. 2021 -
Privacy Optimization in Sensors Based Networks With Industrial Processes Management
The Internet of Things (IoT) also known as IoT has the potential that is required to revolutionize industries, this has been discussed in this research article. Advancements in technology have made devices affordable, efficient and reliable. Different sectors have already started to incorporate these devices into their operations to boost productivity, to minimize failure and downtime. They also use it to optimize resource utilization which is also an important factor. However, the use of these devices also has some security challenges which need to be handled. This research paper proposes a security model specifically designed for process management in the industries. The goal of this model is to find the vulnerabilities, to minimize the risks and threats. Also ensuring integrity, confidentiality and availability of processes is a part of the goal. This paper gives evidence from its implementation and trial apart from its explanation. During the implementation phase, the sensitive data achieved a 100% encryption rate, for protection. Also, integrity checks were conducted on 99.8% of data to guarantee data integrity. 2023 IEEE. -
Privacy over instant messaging platforms: are users making the right decisions?
This article explores the impact of perceived vulnerability, self-efficacy, resistance to change, and habit on users perception of privacy over users intention to use messaging platforms. The conceptual model includes perceived vulnerability, self-efficacy, resistance to change, habit, and its impact on users perception of privacy over users intention to use messaging platforms. A structural equation and hierarchical regression model were used for data analysis. The results show that age and profession affect peoples decision of shifting to a different platform significantly. The study is based on a few specific instant messaging platforms at one particular point in time and is undertaken in India; hence, the findings cannot be extended/applicable to other countries. The paper discusses the factors impacting the users sensitivity to data privacy while using a communication application through an electronic device, especially a mobile phone. Copyright 2025 Inderscience Enterprises Ltd. -
Privacy-Preserving Federated Learning for Prognostic Modeling in Rare Diseases: A Scalable Case Study on Kawasaki Disease
Predictive modeling in rare diseases faces major challenges, including data scarcity, class imbalance, and strict privacy regulations that limit cross-border collaboration. These challenges are particularly critical in Kawasaki disease (KD)a rare vasculitis in childrenwhere 10% to 20% of patients are resistant to intravenous immunoglobulin (IVIG), the standard first-line treatment. This significantly increases the risk of coronary artery abnormalities (CAA), making early and accurate prediction of resistance to IVIG essential for improving patient outcomes. Our work proposes a federated learning (FL) approach to address the constraints imposed by security and privacy concerns. We investigate convolutional neural networks (CNN) as the shared model, collaboratively trained across clients. Coupled with strategies to address class imbalance resulting from the rarity of the condition, the federated approach yielded promising results when evaluated against conventional machine learning (ML) models. The proposed approach demonstrated strong performance, achieving 94% accuracy, 93% precision, 89% recall, and 91% F1 score. To ensure robustness and generalizability, an independent dataset was also used, where the proposed model excelled similarly. These results highlight the potential of FL to overcome data privacy barriers and provide a scalable, secure solution for predictive modeling in rare diseases, supporting its integration into medical prediction workflows. 2025 by the authors of this article. -
Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE. -
Private tuitions: Boon or bane?
The demand for private tuition in India can be attributed to several sociocultural factors. Indian students are pressured to excel in high-stakes examinations, such as board exams and competitive entrance tests. -
Prob(E)abilities for enhanced research and interdisciplinarity: An exploration of innovative practices in english studies, languages, and media
An analysis of the history of research as well as contemporary trends in higher education in India reveals the predominance of scientific research. Public policies, developmental strategies, and market forces often determine the nature and output of research in humanities and social sciences. Research in literatures, languages, and media needs articulationa process that reveals the significant interdisciplinary interventions that can be brought to the process of research and highlights the need for purpose-driven research as research rather than need-based research for research. This paper offers a critical overview of the role of research in universities in India, contemporary approaches to research at the national level, and the need to engage in interdisciplinary, collaborative, and action-based research, especially in humanities and social sciences departments in universities across India. Further, the paper offers a few examples of interdisciplinary and collaborative research to highlight the need for a symbiotic approach to academic research. 2020 IUP. All Rights Reserved. -
Probability of Medication Adherence When Alarm Is Used as a Reminder
The main objective of this research is to find the effect of alarm as a form of reminder in improving medication adherence rate. Medication non-adherence is a problem that adversely impacts patients' health, finances, and longevity. Several factors are associated with medication non-adherence. This research uses the method of probability estimates, risk difference, relative risk, and odds ratio to analyze the probability of an increase in medication adherence among patients who use the alarm as a form of reminder. By clustered sampling and a structured questionnaire, 525 responses were obtained from patients suffering from different types of diseases in the state of Sikkim, India. It has been observed that using the alarm as a form of reminder significantly improves adherence rates. The odds of not missing a dose reduces to 49.3%. At a personal level, the chance of not missing the dose reduces by 32.6%, and if the total population is considered, 16.4% of people will not skip the dose if a reminder in the form of an alarm is used. 2022 International Journal of Reliable and Quality E-Healthcare. All rights reserved. -
Probing star formation in five of the most massive spiral galaxies observed through ASTROSAT UltraViolet Imaging Telescope
We present highly resolved and sensitive imaging of the five nearby massive spiral galaxies (with rotation velocities > 300 km s?1) observed by the UltraViolet Imaging Telescope onboard Indias multiwavelength astronomy satellite ASTROSAT, along with other archival observations. These massive spirals show a far-ultraviolet star formation rate in the range of ? 1.4 13.7 M? yr?1 and fall in the Green Valley region with a specific star formation rate within ? 10?11.5 10?10.5 yr?1. Moreover, the mean star formation rate density of the highly resolved star-forming clumps of these objects is in the range 0.011 0.098 M? yr?1 kpc?2, signifying localized star formation. From the spectral energy distributions, under the assumption of a delayed star formation model, we show that the star formation of these objects had peaked in the period of ? 0.8 2.8 Gyr after the Big Bang and the object that has experienced the peak sooner after the Big Bang show relatively less star-forming activity at z ? 0 and falls below the main-sequence relation for a stellar content of ? 1011 M?. We also show that these objects accumulated much of their stellar mass in the early period of evolution with ? 31 42 per cent of the total stellar mass obtained in a time of (1/16) (1/5)th the age of the Universe. We estimate that these massive objects convert their halo baryons into stars with efficiencies falling between ? 7 and 31 per cent. 2024 Oxford University Press. All rights reserved. -
Probing the effect of newly synthesized phenyltrimethylammonium tetrachloroaluminate ionic liquid as an inhibitor for carbon steel corrosion
The corrosion protection effect of phenyltrimethylammoniumtetrachloroaluminate[PTMA]+[AlCl4]?as an inhibitor was explored in the present work. In this paper, the authors have explored a non-heterocyclicbased ionic liquid as a corrosion inhibitor for metal protection in the acid cleaning process of metal. In particular, a negative ion is designed based onthe lewis acid concept by which it could cover the maximum surface by the bigger molecule size. The inhibition efficiency was found to be steadily increasing as the concentration of the [PTMA]+[AlCl4]? ionic liquids increased.These studies revealed thatthe inhibitor exhibited a remarkable potential for corrosion protection on carbon steel in 1 N HCl solution. Stable corrosion protection efficiency (96%) was achieved for 1.3 mMof inhibitor. The adsorption of the inhibitive molecule was studied by Langmuir adsorption isotherm. The anti-corrosion effect of ionic liquid on the surface protection was revealed by scanning electron microscope (SEM)and lower surface roughness attained at an optimum concentration of inhibitor in atomic force microscope (AFM) analysis. In this study, with the view of the experimental and theoretical investigation (gaseous and aqueous forms of [PTMA]+[AlCl4]? ionic liquid in presence of HCl)was investigated, and finding deduced that the ionic liquid offered maximum dispenses with the heterocyclic group. In addition, to validate the experimental result, dynamic simulation studies were performed in both gaseous and liquid stimulation conditions. 2021 The Author(s) -
Probing the formation of megaparsec-scale giant radio galaxies II. Continuum and polarization behavior from magneto-hydrodynamic simulations
Context. The persistence of radiative signatures in giant radio galaxies (GRGs & 700 kpc) remains a frontier topic of research, with contemporary telescopes revealing intricate features that require investigation. Aims. This study aims to examine the emission characteristics of simulated GRGs, and correlate them with their underlying three-dimensional dynamical properties. Methods. Sky-projected continuum and polarization maps at 1 GHz were computed from five 3D relativistic magnetohydrodynamical (RMHD) simulations by integrating the synthesized emissivity data along the line of sight, with the integration path chosen to reflect the GRG evolution in the sky plane. The emissivities were derived from these RMHD simulations, featuring FR-I and FR-II jets injected at different locations of the large-scale environment and with propagation along varying jet frustration paths. Results. Morphologies, such as widened lobes from low-power jets and collimated flows from high-power jets, are strongly shaped by the triaxiality of the environment, resulting in features such as wings and asymmetric cocoons, thereby making morphology a crucial indicator of GRG formation mechanisms. The decollimation of the bulk flow in GRG jets gives rise to intricate cocoon features, most notably filamentary structuresmagnetically dominated threads with lifespans of a few mega-year. High jet power cases frequently display enhanced emission zones at mid-cocoon distances (alongside warmspots around the jet head), contradicting the interpretations of the GRG as a restarting source. In such cases, examining the lateral intensity variation of the cocoon may reveal the sources state, with a gradual decrease in emission suggesting a low active stage. This study highlights that applying a simple radio powerjet power relation to a statistical GRG sample is unfeasible, as it depends on growth conditions of individual GRGs. Effects such as inverse-Compton cooling due to cosmic microwave background photons and matter entrainment significantly impact the long-term emission persistence of GRGs. The diminishing fractional polarization with GRG evolution reflects increasing turbulence, underscoring the importance of modeling this characteristic further, particularly for even larger-scaled sources. The Authors 2025. -
Probing the formation of megaparsec-scale giant radio galaxies: I. Dynamical insights from magnetohydrodynamic simulations
Context. Constituting a relatively small fraction of the extended-jetted population, giant radio galaxies (GRGs) form in a wide range of jet and environment configurations. This observed diversity complicates the identification of the growth factors that facilitate their attainment of megaparsec scales. Aims. This study aims to numerically investigate the hypothesized formation mechanisms of GRGs extending ?1 Mpc in order to assess their general applicability. Methods. We employed tri-axial ambient medium settings to generate varying levels of jet frustration and simulated jets with a low and a high power from different locations in the environment. This approach formulated five representations evolving under a relativistic magnetohydrodynamic framework. Results. The emergence of distinct giant phases in all five simulated scenarios suggests that GRGs may be more common than previously believed. This prediction can be verified with contemporary and forthcoming radio telescopes. We find that different combinations of jet morphology, power, and evolutionary age of the formed structure hold the potential to elucidate different formation scenarios. In all of these cases, the lobes are overpressured, prompting further investigation into pressure profiles when jet activity ceases, potentially distinguishing between relic and active GRGs. We observed a potential phase transition in GRGs marked by differences in lobe expansion speed and pressure variations compared to their smaller evolutionary phases. This suggests the need for further investigation across a broader parameter space to determine if lobe evolution in GRGs fundamentally differs from smaller radio galaxies. The axial ratio analysis reveals self-similar expansion in rapidly propagating jets, while there is a notable deviation when the jet forms wider lobes. Overall, this study emphasizes that multiple growth factors simultaneously at work can better elucidate the current-day population of GRGs, including scenarios such as the growth of GRGs in dense environments, GRGs extending several megaparsecs, development of GRGs in low-powered jets, and the formation of morphologies such as GRG-XRGs. The Authors 2025. -
Probing the formation of megaparsec-scale giant radio galaxies: I. Dynamical insights from magnetohydrodynamic simulations
Context. Constituting a relatively small fraction of the extended-jetted population, giant radio galaxies (GRGs) form in a wide range of jet and environment configurations. This observed diversity complicates the identification of the growth factors that facilitate their attainment of megaparsec scales. Aims. This study aims to numerically investigate the hypothesized formation mechanisms of GRGs extending ?1 Mpc in order to assess their general applicability. Methods. We employed tri-axial ambient medium settings to generate varying levels of jet frustration and simulated jets with a low and a high power from different locations in the environment. This approach formulated five representations evolving under a relativistic magnetohydrodynamic framework. Results. The emergence of distinct giant phases in all five simulated scenarios suggests that GRGs may be more common than previously believed. This prediction can be verified with contemporary and forthcoming radio telescopes. We find that different combinations of jet morphology, power, and evolutionary age of the formed structure hold the potential to elucidate different formation scenarios. In all of these cases, the lobes are overpressured, prompting further investigation into pressure profiles when jet activity ceases, potentially distinguishing between relic and active GRGs. We observed a potential phase transition in GRGs marked by differences in lobe expansion speed and pressure variations compared to their smaller evolutionary phases. This suggests the need for further investigation across a broader parameter space to determine if lobe evolution in GRGs fundamentally differs from smaller radio galaxies. The axial ratio analysis reveals self-similar expansion in rapidly propagating jets, while there is a notable deviation when the jet forms wider lobes. Overall, this study emphasizes that multiple growth factors simultaneously at work can better elucidate the current-day population of GRGs, including scenarios such as the growth of GRGs in dense environments, GRGs extending several megaparsecs, development of GRGs in low-powered jets, and the formation of morphologies such as GRG-XRGs. The Authors 2025.