Journal of Computers, Mechanical and Management

Strategic Market Entry Routes for Specialized Industrial Goods

P. Lavanya, T. Vara Lakshmi, T. Sanjay Kumar — Thu, 30 Apr 2026 00:00:00 +0300

Indian industrial firms operating in business-to-business (B2B) markets are expanding into emerging economies, yet selecting an appropriate market entry route remains a complex strategic decision. This study examines how Indian firms dealing in specialized and high-complexity industrial goods evaluate key entry options such as exporting, licensing, partnerships, joint ventures, and wholly owned subsidiaries. Using a qualitative multi-case approach supported by industry data, expert insights, and comparative analysis from sectors including nutraceutical ingredients, industrial machinery, and medical devices, the research identifies major determinants influencing entry-mode choice. Product complexity, regulatory requirements, buyer expectations, firm capabilities, market uncertainty, and competitive intensity emerged as the most influential drivers. The study proposes a decision-making framework that aligns internal competencies with external market conditions, offering exploratory insights into how firms may evaluate risk, cost, and control trade-offs when selecting entry strategies. The findings contribute to the limited literature on outward internationalization of Indian B2B firms and offer practical strategic guidance for managers entering highly regulated and technologically demanding global markets.

Evaluation of Mechanical Properties of Graphene-Reinforced PETG Filaments Fabricated by FDM

Ram Kishore Shakya, Dharamvir Mangal, Nagendra Kumar Maurya — Thu, 30 Apr 2026 00:00:00 +0300

In this study, graphene-reinforced polyethylene terephthalate glycol (PETG) composites are fabricated using the Fused Deposition Modeling (FDM) technique. The influence of process parameters, namely layer thickness, print speed, and nozzle temperature, on tensile and flexural strength is investigated. A Taguchi design of experiments is employed to optimize the process parameters, and regression models are developed to predict tensile strength and flexural strength. The optimum combination for tensile strength is identified as a layer thickness of 0.1 mm, print speed of 20 mm/s, and nozzle temperature of 230 °C, while the optimum combination for flexural strength is found at a layer thickness of 0.1 mm, print speed of 20 mm/s, and nozzle temperature of 235 °C. Microstructural characterization performed using Scanning Electron Microscopy (SEM) revealed uniform dispersion of graphene within the PETG matrix and strong interlayer bonding at optimized conditions. Fractography analysis conducted using Field Emission Scanning Electron Microscopy (FE-SEM) confirmed ductile fracture behavior with limited void formation. The developed regression models exhibited strong predictive accuracy, demonstrating their suitability for process optimization and the prediction of mechanical properties.

Humidity-Aware Hybrid Transformer-LSTM Framework for IoT-Enabled Photovoltaic Power Prediction

Ahmed Mohammed, Ranjit Sarban Singh, Saad Aslam — Thu, 30 Apr 2026 00:00:00 +0300

Accurate short-term photovoltaic (PV) power forecasting is essential for Internet of Things (IoT)-enabled monitoring, control, and performance assessment of small-scale solar installations. While electrical variables and temperature are widely used in data-driven PV forecasting models, the contribution of ambient humidity remains insufficiently characterized, particularly in persistently humid environments. This study investigates the role of ambient humidity as a contextual environmental feature and evaluates a humidity-aware Hybrid Transformer-LSTM framework for short-term PV power prediction using real-world IoT data collected from multiple photovoltaic panels over a 34-day monitoring period. The proposed hybrid architecture integrates a Transformer-based self-attention mechanism for cross-feature interaction modeling with LSTM-based recurrent learning to capture temporal persistence. Model performance is evaluated against LSTM-only, Transformer-only, Random Forest, and Linear Regression baselines using a strictly time-ordered train-test split, complemented by architectural and feature ablation studies, rolling time-based validation, cross-panel testing, and robustness analysis under input perturbation. Experimental results show that LSTM-based models achieve the highest predictive accuracy on the short-duration dataset, while ambient humidity provides only marginal and context-dependent benefit as a supplementary environmental feature. Transformer-only models perform poorly under data-limited conditions, while the Hybrid Transformer-LSTM achieves competitive accuracy and demonstrates stable behavior under temporal validation, spatial generalization, and sensor noise. These findings highlight that the primary contribution of this study lies in rigorous evaluation and deployment-aware validation rather than absolute accuracy gains, positioning hybrid attention-recurrent architectures as robustness-oriented solutions for IoT-enabled solar PV monitoring systems.

The Integration of Strategic Management and Digital Marketing in Enhancing Overall Business Performance

Thu, 30 Apr 2026 00:00:00 +0300

In an increasingly digitalized and highly competitive business environment, organizations are required to strategically integrate digital tools into their management systems to enhance both competitiveness and long-term sustainability. This study examines the impact of integrating strategic management and digital marketing on overall organizational performance, with particular emphasis on their complementary and synergistic roles. A quantitative research design was employed, using a structured questionnaire administered to a sample of 200 medium- and large-sized enterprises operating across diverse sectors. The collected data were analyzed using multiple linear regression techniques with SPSS version 23, complemented by correlation analysis and diagnostic testing to ensure robustness. The findings indicate that strategic management is significantly associated with organizational performance (β = 0.40, p < 0.001 in a simple regression model), highlighting its central role in guiding organizational direction and resource allocation. Digital marketing practices also demonstrate a strong positive relationship with performance (β = 0.35, p < 0.001), reflecting their importance in enhancing customer engagement, market reach, and operational efficiency. Furthermore, the results suggest that strategic alignment strengthens the effectiveness of digital marketing initiatives, reinforcing the importance of integrating digital tools within a coherent strategic framework. The model explains a substantial proportion of the variance in organizational performance (R² = 0.62), indicating strong explanatory power. This study contributes to the literature by proposing and empirically supporting an integrated framework in which strategic management and digital marketing act as complementary drivers of financial, operational, and customer-related performance. Practical insights are also provided for managers seeking to leverage digital transformation through strategic alignment. Future research directions are proposed to further explore mediating and moderating mechanisms.

Cross-Cultural Academic Writing Challenges and the Role of Peer Support

Zhao Yue, Seng Yue Wong , Kenny Cheah Soon Lee — Thu, 30 Apr 2026 00:00:00 +0300

Increased global mobility has led to a rise in international doctoral students, yet cross-cultural academic writing challenges persist as a key barrier to scholarly success, particularly among Chinese PhD candidates navigating unfamiliar academic norms. This study examines how cross-cultural writing challenges (CCWC), peer emotional support (PES), and intercultural competence (IC) shape students' academic identity (AI) and influence their academic writing self-efficacy (AWSE), framed through Bandura's Social Cognitive Theory. Data were collected from 300 Chinese doctoral students enrolled in Malaysian research universities. Structural equation modeling using SmartPLS 4.0 assessed both direct and indirect relationships among constructs. Results revealed significant direct effects of CCWC (T = 3.837, p < 0.001) and PES (T = 5.533, p < 0.001) on AI, as well as a significant effect of AI on AWSE (T = 5.482, p < 0.001). Mediation analysis demonstrated that AI partially mediated relationships between CCWC and AWSE (T = 3.699, p < 0.001) and between PES and AWSE (T = 3.520, p < 0.001). These findings underscore the critical role of academic identity in translating cross-cultural and social support factors into positive academic writing experiences, offering practical implications for institutional strategies to strengthen doctoral students' academic identity and writing development in cross-cultural contexts.

Structural Model of Perceived Interdisciplinary Competency Development Through Computer Aided Engineering Drawing in Computer Science and Engineering Curricula

N. Sudharshan, M. Shreyas, K. B. Vinay, T. R. Praveen Yadav — Thu, 30 Apr 2026 00:00:00 +0300

This research examines the impact of Computer-Aided Engineering Drawing (CAED) programs on the development of perceived interdisciplinary competency in engineering and computer science courses through Structural Equation Modeling (SEM), focusing on two mediating mechanisms: student attitudes toward CAED programs and the learning process within a supportive institutional environment. The structural equation model explained a substantial proportion of the variance in perceived competency (R² = 0.744) and indicated that both attitudes (β = 0.787) and learning experience (β = 0.782) were considerably influenced by CAED program design. Competency mediated by student attitudes (β = 0.229) and by learning experience (β = 0.370) had significant indirect effects, and the institutional context itself positively impacted competency (β = 0.327), likely reflecting the resources available and the promotion of interdisciplinary teamwork. Overall, the results highlight the significance of designing CAED programs in accordance with industry requirements, alongside active learning methods that foster positive student perceptions, and the importance of robust institutional support systems that equip students with the competencies required in modern engineering practice. The study is limited by its cross-sectional nature and single-institution sample. Future research can examine the long-term effects of these relationships, investigate how institutional variables may moderate them, and test the validity of the relationships across multiple institutions.

Mapping the Landscape of Street Food Research

Anirudh Thakur, Sanjeeb Pal, Rakesh Ahlawat, Manish Verma — Thu, 30 Apr 2026 00:00:00 +0300

Street food has experienced considerable growth, particularly in developing countries, and has become an important component of destination marketing and the tourist experience. Its increasing popularity has attracted significant interest from researchers across various fields, including tourism and marketing. Guided by the PRISMA framework, this study examines the structure and evolution of the street food research landscape through a bibliometric approach. A total of 186 articles from the Dimensions database were analyzed using VOSviewer software. The analysis encompassed author networks, influential journals, contributing countries and institutions, and thematic keyword clusters. The findings reveal substantial growth in research on this topic, with Indonesia, India, and Turkey emerging as key contributors. Major journals publishing research on street food include the Journal of Hospitality and Tourism Management and the British Food Journal. Vikas Gupta and Raj Kumar Gupta are among the most active researchers in this area. Thematic analysis identifies three main clusters: cultural and tourism studies, marketing and consumer behavior, and behavioral theories and intentions.

Digitalized Organizational Career Management Systems

Timsy Kakkar, Bharti — Thu, 30 Apr 2026 00:00:00 +0300

This study investigates Digitalized Organizational Career Management Systems (DOCMS) through two complementary perspectives: functional design and user experience. A systematic literature review was conducted on 87 peer-reviewed articles published between 2010 and 2024, applying PRISMA guidelines to ensure transparency and rigor. Braun and Clarke's six-phase thematic analysis framework was employed for synthesis. The study first developed a bibliometric mapping as a pre-analysis step and then identified six emergent themes: career management strategies, information technology integration, organizational career management, job satisfaction and organizational commitment, career self-management, and career motivation. The review contributes to the literature by combining both bibliometric and thematic approaches, thereby providing a structural overview of the field while enabling a deeper thematic interpretation.</p>

<p>The findings indicate that DOCMS research has matured considerably along functional sense-making dimensions (i.e., functional design) while remaining fragmented with respect to experiential aspects (i.e., experience design), thereby necessitating greater integration between the two. Significant contradictions were identified within the literature: between algorithmic objectivity and organizational politics, between system-driven guidance and individual autonomy, and between functional design criteria and relational evaluation processes. A dual-lens framework is proposed to integrate these perspectives through a socio-technical lens. The review offers both theoretical explanations of the socio-technical structure of DOCMS—in terms of organizational functionality—and practical implications for the development of DOCMS that balance organizational efficiency and employee career development. The framework provides organizations with a diagnostic tool for identifying functional-experiential gaps and understanding the underlying contradictions that may limit DOCMS effectiveness regardless of technical sophistication.

Workload-Specific Performance Evaluation of Python Just-in-Time Compilers

Sattaru Harshvardhan Reddy, Priya Gupta, Deepak Kumar, Ritu Singhal — Thu, 30 Apr 2026 00:00:00 +0300

The persistent performance gap in dynamic languages like Python has driven the development of numerous compiler solutions. This paper presents a comparative performance analysis of two prominent Python compilers, Numba and Cython, across distinct computational workloads: recursive (Fibonacci series) and arithmetic-intensive (Euclidean distance). Addressing a gap in existing literature, this study provides an evidence-based framework that maps compiler performance directly to workload types. Experiments conducted in a controlled environment measured execution time, speedup ratios, and memory usage. Results demonstrate that Numba achieves a speedup of up to 6.18× over pure Python for arithmetic-intensive tasks, while Cython performs better in deep recursion cases. The study concludes by offering a workload-to-compiler decision framework, which serves as a practical tool and a contribution to the literature on scenario-based compiler recommendations.