Search Results (89)

Shopping cart abandonment remains a persistent challenge in e-commerce, yet evidence regarding the role of perceived risk remains fragmented across shopping contexts. This study aims to clarify when and under what conditions perceived risk is most strongly associated with shopping cart abandonment by quantitatively synthesizing 153 effect sizes from 60 independent studies reported in 49 empirical articles published between 2004 and 2026. A multilevel random-effects meta-regression approach was employed to account for dependent effect sizes and to test risk source, product category, shopping purpose, sample location, and publication year as moderators, together with two theoretically specified interactions. The results show that perceived risk is positively associated with shopping cart abandonment (r = 0.244, 95% CI [0.191, 0.296]). The association was stronger for transaction than product-related risk, for fast-moving consumer goods rather than durable goods, and in hedonic rather than utilitarian shopping contexts. Sample location and publication year were not significant moderators. Risk source also interacted with shopping purpose and product category, indicating that risk effects depend on configurations of contextual conditions. Sensitivity analyses and publication-bias diagnostics did not materially alter the pooled estimate. The findings shift attention from whether perceived risk matters to the contexts in which it is most likely to interrupt purchase completion. Full article

Vulnerability management (VM) is becoming increasingly important as organizations face growing cybersecurity threats. This study examines how organizations adapt their vulnerability management routines in response to evolving vulnerability signals through the integration of artificial intelligence (AI) and automation. Drawing on data from an international fast-moving consumer goods (FMCG) company, we investigate how human expertise and AI interact across the full VM process, from triage to remediation. Using Organizational Routine Theory (ORT), we show that AI does not simply automate tasks but acts as a co-performer, influencing how decisions are made, work is coordinated, and actions are adapted. We develop a three-phase model capturing (1) the integration of AI-enabled automation into strained routines, (2) the manifestation of tensions between human expertise and automation as well as between usability and system complexity, and (3) the stabilization of hybrid routines through iterative adaptation and feedback loops. We identify two key tensions in this process: technology versus human expertise, and usability versus the complexity of multi-vendor tools. These tensions create frictions in practice but also open opportunities for learning and improvement. Rather than treating AI as a technical tool, our findings highlight its role as an active routine participant. Importantly, we show that routine evolution enables organizations to improve how vulnerability signals are interpreted and acted upon, thereby supporting more coordinated and adaptive cybersecurity practices. This has both theoretical implications for understanding how routines evolve with technology and practical relevance for improving adaptive cybersecurity practices. By linking micro-level routine dynamics to broader organizational outcomes, this study contributes to explaining how organizations sustain stable and adaptive operations under conditions of continuous cyber threat exposure. Full article

Retail demand forecasting for fast-moving consumer goods (FMCGs) presents significant challenges due to high product variety, demand intermittency, and uncertainty, which prevent any single model from capturing the diverse demand patterns. To address these challenges, this study proposes a hybrid clustering framework that integrates rule-based (Syntetos–Boylan Classification) and machine learning (ML) approaches, combining time-series embeddings with unsupervised learning to segment products by demand structure. Building on this framework, forecasting is conducted through a two-phase methodology: selecting optimal baseline algorithms per cluster (Phase 1), then enhancing them with embedding-based hybrid models (Phase 2). The effectiveness of this approach is demonstrated using a large-scale real-world dataset comprising over 3.8 million weekly sales records from 12,661 products across 691 stores. Results show that the proposed method improves forecasting accuracy by approximately 5–15% compared to conventional models. Furthermore, model performance varies with demand volatility, as different model–embedding combinations perform best under different conditions. Finally, the proposed diagnostic heuristic reduces experimental effort by 25–50%. Comparative analysis reveals that ML-based clustering outperforms rule-based methods under stable demand, whereas rule-based clustering is superior under high demand uncertainty, confirming that no single clustering paradigm is universally optimal. These findings demonstrate the practical value of adaptive hybrid frameworks for FMCGs demand forecasting. Full article

The adoption of disruptive technologies has become increasingly critical for organizations, particularly following the global shifts prompted by the COVID-19 pandemic. Despite the potential benefits, many organizations, including those in the Fast-Moving Consumer Goods (FMCG) industry, face significant hurdles in this transition. Consequently, this study aims to understand the primary challenges and enabling factors influencing the adoption of disruptive technologies for sustainable digital transformation within the South African FMCG sector. A quantitative methodology was employed, utilizing a questionnaire for data collection. Data from 102 respondents were analyzed using SPSS version 28, involving descriptive statistics (mean item score) to rank factors and exploratory factor analysis (EFA) to identify underlying constructs, and a reliability test was carried out with a score of 0.7. Key challenges identified include high initial costs and poor collaboration. Prominent enabling factors include top management commitment and operational cost reduction. The EFA revealed significant underlying challenge dimensions such as “Infrastructural and Resources Constraints” and “Human Factors Constraints,” and enabling dimensions including “Organizational Commitment and Strategy” and “Leadership.” The study concludes with key implications for promoting successful adoption. The adoption of disruptive technologies has become a strategic imperative for sustainable digital transformation (SDT), particularly in emerging markets such as South Africa’s FMCG sector. This study investigates the key challenges and enabling factors shaping technology adoption within this context. A quantitative methodology was employed, using a structured questionnaire distributed to 102 professionals across FMCG organizations in Gauteng. Exploratory factor analysis (EFA) revealed latent dimensions within both challenges and enablers, which were then interpreted through the lens of Rogers’ Diffusion of Innovation (DOI) theory. To enhance analytical clarity, a matrix model was developed linking factor dimensions to DOI attributes such as relative advantage, complexity, compatibility, trialability, and observability. The study found that high initial costs, poor collaboration, and human capability gaps significantly impede adoption, while strong leadership, strategic alignment, and operational cost savings facilitate it. The findings underscore the need for systemic interventions that address not only technical readiness but also leadership, organizational culture, and structural alignment. Practical implications are outlined for both policy and management, particularly in leveraging DOI attributes to accelerate digital transformation, as well optimize innovation diffusion within resource-constrained environments. For the future, the study proposed a hybrid methodology incorporating qualitative interviews to enhance depth and suggests longitudinal tracking to capture temporal shifts in transformation maturity. Full article

Background: Globally, in the Fast-Moving Consumer Goods (FMCG) industry, excess inventory results from the bullwhip effect. Earlier, barcode-based two-bin systems were limited by manual scanning; hence, a more responsive system is needed to align the inventory with real-time demand. Prior studies have predominantly concentrated on mitigating demand fluctuations and employed comparatively low-efficiency systems, hindering excess inventory (EI) reduction. Methods: This study proposes identifying research gaps, considering the distributor-manufacturer relationship, and developing an RFID-based modified two-bin system and mathematical model to reduce EI and control over manufacturers’ excessive cost. Results: This study tested through Python-based simulation using historical data from an FMCG manufacturer, and the proposed model achieved a reduction in 67% EI and 73% month-wise holding costs. Moreover, the integration of the Artificial Bee Colony algorithm optimizes rework rates within budget, including reworking shop-floor and holding costs, contributing to a monthly excessive cost reduction of 34–48%, alongside a corresponding 41–44% cumulative excessive cost reduction. Conclusions: Bringing significant implications on digitalized SCM, this study offers a practical and scalable solution for perishable FMCG items facing demand variability and budget constraints. Collectively, this novel perspective bridges research gaps and motivates future research for embedding trend-aligned parameters, enhancing the model’s performance through diverse SCM contexts like safety stock and backorder cost optimization. Full article

This paper presents a hybrid fabrication method for producing anti-counterfeit optical elements on plastic products and surfaces targeting multidiscipline applications such as food, pharmaceuticals, luxury goods, and electronics industry. Our proposition combines the design flexibility and rapid prototyping capabilities of stereolithography three-dimensional (SLA 3D) printing with nanoimprint lithography (NIL) to create unique optical security tags onto plastic surfaces. The proposed approach is cost-effective, scalable, and tailored for mass production, addressing the increasing demand for secure and reliable authentication solutions. NIL is substrate agnostic, offering material selection versatility and realization of security tags onto polymer surfaces, which are widely used across various sectors such as packaging industry, medical devices, and flexible electronics. This enables integration into a wide range of materials, further enhancing applicability on flat and 3D shape surfaces. An evaluation method based on digital reconstruction has been used to ensure robust performance and verification of the produced optical security features. The results demonstrate that this hybrid approach provides a reproducible and technically feasible path for the development of optical anti-counterfeiting tags suitable for large-scale implementation, particularly within fast-moving consumer goods (FMCG). Full article

Energy-intensive sectors in emerging nations have the simultaneous difficulties of trying to diminish greenhouse gas emissions while maintaining a stable and cost-effective energy supply. Rooftop solar photovoltaic (PV) systems offer a viable solution, especially in tropical areas like Indonesia that have elevated solar irradiance. This study employs a comprehensive methodology to evaluate the structural, economic, and safety viability of rooftop photovoltaic adoption in the Fast-Moving Consumer Goods (FMCG) sector. Structural analysis utilizing the PMM Ratio verified that industrial rooftops can support a 599 kWp photovoltaic system with minimal reinforcements. The economic assessment revealed substantial feasibility, featuring a Levelized Cost of Energy (LCOE) of Rp 261.40/kWh (about USD 0.016/kWh), yearly savings of Rp 1.36 billion (approximately USD 89,000), a Return on Investment (ROI) of 570%, and a payback duration of 3.73 years. The safety evaluation utilizing the Hazard Identification and Risk evaluation (HIRA) technique found significant hazards—working at height, electrical faults, and fire risks—and recommended mitigation measures in accordance with IEC and Indonesian standards. The findings establish a replicable paradigm for assessing rooftop photovoltaic systems in energy-intensive sectors and furnish actionable recommendations for policymakers and industry executives to expedite the adoption of renewable energy in tropical emerging economies. Full article

Category management (CM) is crucial for optimising retailer–supplier partnerships via technological integration and data sharing. However, the role of CM in Saudi Arabia’s unique fast-moving consumer goods sector (FMCG) remains underexplored. This study aimed to answer the following research question: How do cloud-based inventory platforms and real-time data sharing improve forecasting accuracy and inventory turnover for retailer–supplier CM partnerships in Saudi Arabia’s FMCG sector? A systematic review of 87 studies from the Web of Science and Scopus databases was conducted, followed by thematic analysis. The findings indicate that CM improves demand forecasting, inventory optimisation, and collaborative decision-making. Key implementation barriers include cultural resistance to data sharing, high technology costs for small and medium-sized enterprises, and infrastructural limitations. Success relies on phased technology adoption, relational data governance, and trust building that aligns with Saudi cultural norms. The study concludes that CM is essential for leveraging technology and data capabilities, and it offers a contextualised framework to overcome local barriers and support the achievement of Vision 2030 objectives. This study provides practical strategies for sector stakeholders to adopt high-impact, low-cost technology and a basis for future comparative studies in Gulf Cooperation Council markets. Full article

This study adopts a dynamic Expectancy–Disconfirmation framework to investigate the evolving nature of user satisfaction across three product categories: durable goods, fast-moving consumer goods (FMCG), and digital products. A 25-day longitudinal experiment involving 128 participants was conducted, during which users engaged with their most recently purchased products and provided repeated subjective evaluations over time. The findings reveal dynamic changes in the influence of expectations and perceived performance on satisfaction throughout the product usage cycle. For durable goods and FMCG, both expectations and perceived performance gradually declined, accompanied by a weakening effect of expectations on satisfaction. In contrast, digital products exhibited greater volatility, lacking a stable experiential baseline and resulting in greater fluctuations in satisfaction trajectories. Moreover, external contextual and emotional factors were found to play a more significant role in shaping satisfaction with physical products, beyond the scope of the traditional expectancy–performance model. These insights offer theoretical and managerial implications for sustainable product and experience design. In particular, they highlight the importance of implementing experience-stabilizing strategies in digital consumption contexts to support user well-being and enhance continuous product utilization, thereby maximizing product potential and reducing waste. Full article

Although product environmental information serves as an effective tool for promoting green consumption which is a critical lever for advancing broader sustainability goals, its varied impacts across product categories (durable goods vs. fast-moving consumer goods) and the underlying mechanisms remain unexplored. Grounded in the theory of consumption values (TCV), this study investigated the heterogeneous effects and mediating pathways of such information through a comparative analysis of representative products: organic milk (fast-moving consumer goods, FMCGs) and energy-efficient air conditioners (durable goods). The results show the following: (1) epistemic value, which exhibits the strongest association with product environmental information, demonstrates significantly different influence patterns between purchases of green durable goods and green FMCGs across both online and offline channels; (2) in the e-commerce context, green FMCG consumption is mainly driven by product environmental information through the mediating effect of conditional value. For green durable goods, product environmental information influences green consumption through multiple pathways including functional value, conditional value, and epistemic value. This study extends the classic theory of consumption values, and the results suggest that differentiated information strategies of emphasizing conditional value for FMCGs and integrating multi-dimensional values for durables can optimize green consumption promotion. Such strategies hold substantial potential to strengthen the green development of the omnichannel retailing sector, reinforcing its contribution to reaching sustainability objectives. Full article

Packaging waste is a major environmental issue, making the transition to sustainable solutions imperative. This article proposes the concept of Shared Producer Responsibility (SPR) as a key approach to advancing sustainable packaging in the fast-moving consumer goods (FMCG) sector. The study explores how the United Nations Sustainable Development Goals (SDGs), environmental, social, and governance (ESG) reporting, and stakeholder engagement converge to support this transition. The research identifies current trends, challenges, and gaps in sustainable packaging practices through a systematic literature review (SLR) and analysis of sustainability and ESG reports from leading FMCG and packaging companies. The findings highlight the need for standardised reporting frameworks and improved stakeholder cooperation to enhance transparency and accountability in sustainability efforts. This study proposes a conceptual framework for accelerating sustainable packaging adoption through combining strategies like consumer education, regulatory incentives, and clear product labelling. The proposal to implement the concept of Shared Producer Responsibility emphasises the shared accountability of FMCG companies and packaging manufacturers in managing the full environmental lifecycle of packaging materials. This approach is crucial for achieving SDG 12 (responsible consumption and production) and SDG 13 (climate action) and driving more effective and sustainable packaging practices across the FMCG industry. Full article

Cable-driven actuators (CDAs) are extensively used in the rehabilitation field because of advantages such as low moment of inertia, fast movement response, and intrinsic flexibility. Accurate control of cable force is essential for achieving precise movement control, especially when the movement is generated by multiple CDAs. However, velocity-induced disturbances pose challenges to accurate force control during dynamic movements. Several strategies for direct force control have been investigated in the literature, but time-consuming tests are often required. The aim of this study was to develop a force feedback controller and a speed feedforward compensator for a CDA with a convenient experiment-based approach. The CDA consisted of a motor with a gearbox, a cable drum, and a force sensor. The transfer function between motor torque and cable force was estimated through an open-loop test. A PI force feedback controller was developed and evaluated in a static test. Subsequently, a dynamic test with a reference force of 100 N was conducted, during which the cable was pulled to move at different speeds. The relationship between the motor speed and the cable force was determined, which facilitated further development of a speed feedforward compensator. The controller and compensator were evaluated in dynamic tests at various speeds. Additionally, the system dynamics were simulated in MATLAB/Simulink. The static test showed that the PI force controller produced a mean force control error of 4.7 N, which was deemed very good force-tracking accuracy. The simulated force output was very similar to the experiment (RMSE error of 4.0 N). During the dynamic test, the PI force controller alone produced a force control error of 9.0 N. Inclusion of the speed feedforward compensator improved the force control accuracy, resulting in a mean error at various speeds of 5.6 N. The combined force feedback controller and speed feedforward compensator produced a satisfactory degree of accuracy in force control during dynamic tests of the CDA across varying speeds. Additionally, the accuracy level was comparable to that reported in the literature. The convenient experiment-based design of the force control strategy exhibits potential as a general control approach for CDAs, laying the solid foundation for precise movement control. Future work will include the integration of the speed compensator into better feedback algorithms for more accurate force control. Full article

The primary objective of this empirical study is to investigate the Indian equity market network by analyzing its topological properties using the disparity filtering technique, and a minimum spanning tree. It investigates the backbone structure of the reduced weighted equity network and highlights the sector-based cluster formation. This study also examines the relative importance of each sector by utilizing different key network metrics, with comparative analysis against other emerging markets. It observes a high sector-specific dominance, power imbalance, disparity, and risk concentration in the healthcare and technology sectors. It also finds that fast-moving consumer goods and the healthcare sector can play important roles in maintaining economic stability, public health, and social wellbeing. The findings of this study are highly useful in understanding the market structure, risk management, and investment decisions in the emerging market context of India. Full article

Supply chain resilience (SRES) is essential for firms aiming to alleviate the impact of interruptions and maintain operational continuity and sustainability in performance. In the context of the FMCG industries of Pakistan and Saudi Arabia, characterized by intricate and vulnerable supply chains, there exists an urgent necessity for solutions that bolster resilience. This study examines the influence of critical factors resilient suppliers (RS), transportation capacity (TC), flexibility (FLEX), network complexity (NC), and supply chain dynamism (SPD) on SRES. A quantitative methodology was utilized, examining survey data from 611 participants with Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). The results indicate that RS and TC are essential for directly improving SRES, whereas FLEX and SPD facilitate increased adaptability and reactivity. The NCA emphasizes the need to control NC to avert bottlenecks that may impede resilience. This study indicates that SRES emerges from the dynamic interplay of several elements, rather than from separate enhancements. Although NC exerts a negligible direct influence, adeptly managing complexity is crucial for reducing disruptions and inefficiencies. The results underscore that fortifying RS, TC, and FLEX in unison improves resilience and adaptation to market volatility and disturbances. This study provides various theoretical and managerial insights. A systems theory approach highlights the interdependence of supply chain components, whereas the Theory of Constraints (TOC) posits that excessive NC can hinder resilience. Management should concentrate on maximizing RS and TC until declining returns are evident, thereafter redirecting efforts towards improving FLEX and minimizing NC. Furthermore, optimizing processes and facilitating swift decision-making are essential for maintaining resilience. Full article

In the changing field of sustainable packaging, companies are confronted with the challenge of balancing sustainability with brand differentiation. The move toward standardized, reusable packaging is beneficial for the environment but restricts the use of custom designs. This study explores how standardized, reusable packaging affects consumer perception in the fast-moving consumer goods (FMCG) sector. It focuses on the evolving role of brands to maintain brand differentiation. This research is centered around two case studies. The first examines 219 tomato products to understand the factors driving packaging diversity. Data was collected from three Dutch supermarket websites to analyze packaging types, materials, and size. The second case study investigates consumer responses to single-use versus standardized reusable packaging across eight brands in both food and non-food categories. An online survey was used to assess perceived quality (PQ), willingness to buy (WTB), and brand perception. The results indicate that standardization has a limited effect on perceived quality (the impression of excellence that a consumer experiences), suggesting that it may encourage more brands to adopt reusable packaging. Willingness to buy findings, indicating whether consumers have the intention to buy a product, were mixed. A decrease was observed in food products and an increase noted in non-food. Brand perception most often showed a decrease, indicating challenges in maintaining brand differentiation. Three strategic approaches for brands to align with a sustainability-driven market while preserving value are presented. These are focusing on visual and verbal differentiation, collaborating with competitors to adopt a common archetypal packaging, or shifting marketing away from physical packaging towards digital and authentic communication. However, the new role of marketers will need further exploration, with a focus on authentically communicating the real content and its added value. Full article