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The criticality of late deliveries in transportation lies in the threat of considerable multi-level supply chain costs. This study aims to reveal the dynamic capabilities playing a facilitating role in preventing delay, thus providing timely delivery, as well as developing an understanding of how and when those capabilities are activated within the supply chain network.

An exploratory study was conducted involving 16 semi-structured expert interviews with the representatives of logistics service providers and shippers. Following an interpretive phenomenology framework, the prevention phenomenon was explained.

Findings revealed two preventive capability categories in delay prevention: (1) proactive capabilities, referring to the enabling actions planned before departure, and (2) reactive capabilities, referring to actions planned after departure. Findings pinpoint that, in addition to the proactive capabilities, reactive capabilities enabled by innovative problem-solving actions are crucial for adapting to a dynamically changing environment in prevention. Moreover, this study shows that prevention capabilities are characterized by tangible and intangible resources and integration of resources with external links which constitute a delay prevention network within a wider service ecosystem.

This study stands out with its specific focus on delay prevention capabilities and enabling actions from the perspectives of logistics service providers and shippers. The premises of the resource-based view are combined with dynamic capabilities theory, leading to a proposed time-based taxonomy of proactive and reactive capabilities in supply chains, aimed at creating value and strengthening resilience.

Most of the previous studies agree about the significance of user satisfaction in ensuring the endurance of information systems (ISs). Accordingly, it is crucial to investigate the effect of e-Government systems on individual end-user satisfaction as more and more countries adopt and deploy such Government Financial Management Information Systems (GFMIS) in the era of digital transformation. Because of this, the purpose of this study is to investigate the factors that contribute to the success of GFMIS in Jordan and ultimately the satisfaction of its users.

The IS success model developed by DeLone and McLean (2003) serves as the theoretical underpinning for the current research. Adding training quality as a new variable to the proposed model has been found to further increase the satisfaction of GFMIS users. A total of 104 GFMIS users in Jordan provided the data used to verify the model. The partial least squares-structural equation modelling was used to test the hypotheses.

The empirical findings indicated that GFMIS user satisfaction is significantly affected by information quality, service quality and perceived usefulness; meanwhile, system quality is only partially supported. The research also showed that the level of satisfaction among Jordanian GFMIS users was related to the quality of training they received.

This study fills a crucial literature gap by developing a research model that can help improve GFMIS usage towards attaining greater performance amongst government agencies in Jordan.

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

Untimely delivery of goods and services, especially in a post-COVID landscape, is a critical harbinger of end-to-end fulfillment. Existing literature in supplier delivery modeling is focused on penalizing suppliers for late deliveries built into a contractual transaction, which eventually erodes trust. As such, a holistic modeling technique focused on long-term relationship building is missing. This study aims to design a supplier evaluation model that analytically equates supplier delivery performance to cost realization while replicating a core attribute of successful supply chains – alignment, leading to long-term supplier relationships.

The supplier evaluation model designed in this paper uses delivery deviation as a unit of measure as opposed to delivery duration to enhance consistency with enterprise resource planning protocols. A one-sided modified Taguchi-type quality loss function (QLF) models delivery lateness to construct a multinomial probability penalty cost function for untimely delivery. Prescriptive analytics using simulation and optimization of the proposed mathematical model supports buyer–supplier alignment.

The supplier evaluation model designed herein not only optimizes likelihood parameters for early and late deliveries for competing suppliers to enhance total landed cost comparisons for on-shore, near-shore and off-shore suppliers but also allows for the creation of an efficient frontier toward supply base optimization.

At a time of systemic disruptions such as the COVID pandemic, global supply chains are at risk of business continuity. Supplier evaluation models need to focus on long-term relationship modeling as opposed to short-term contractual penalty-based modeling to enhance business continuity. The model offered in this paper is grounded in alignment – a cornerstone of successful supply chain integration, and offers an interesting departure from traditional modeling techniques in this genre.

The results from this analytical approach offer flexibility to a supply manager toward building redundancies in the supply chain using an efficient frontier within the supply landscape, which also helps to manage disruption and maintain end-to-end fulfillment.

The model offered in this paper is grounded in alignment – a cornerstone of successful supply chain integration, and offers an interesting departure from traditional modeling techniques in this genre. The authors offer a rational solution by creating an evaluation model that uses penalty cost modeling as an internal quality measure to rate suppliers and uses the outcome as a yardstick for negotiations instead of imposing penalties within contracts.

This study aims to analyze and understand mutual relationships between lean and agile enabling factors in the context of the construction domain and put forth the most driving and dependent enablers for a hybrid lean–agile system (HLAS).

A threefold research flow is adopted. First, the content analysis of literature stemmed from the identification of preliminary enablers (n = 26). Second, a questionnaire survey (n = 101) is administered to downsize enablers to ten using the relative importance index. Further, in the third phase, interpretive structural modeling (ISM) and the cross-impact matrix multiplication applied to classification analysis were performed amid the ten key enablers through pairwise interactions from subject matter experts (n = 19). Hierarchical representation of enablers has been presented, which facilitates a conceptual model for application in the construction industry.

The ISM model demonstrates a three leveled classification structure that showcases direct and transitive relationships among various enablers. “Use of project management tools” is the driving enabler, followed by “collaborative and common data platform working” and “institution of team training” are critical enablers to HLAS adoption.

A structured way of identification of interdependent among lean–agile is a vital unique contribution to this study. The results serve as a knowledge base for practical adoption of HLAS and may prove to be influential in developing a framework for easier adoption in developing countries.

As host to over one million Syrian refugees, Lebanon continues to experience challenges addressing the needs of refugee families. This research examined the experiences of Syrian families with the refugee support system in Lebanon. The purpose of this study was to better understand the strengths and gaps in existing mechanisms of support for these Syrian families, including informal support from family, neighbors and community and more formalized support provided through entities such as nongovernmental organizations and United Nations agencies.

Data were collected from 46 families displaced by the war and living in Lebanon (N = 351 individuals within 46 families). Collaborative family interviews were conducted with parents, children and often extended family.

The data identified both strengths and gaps in the refugee support system in Lebanon. Gaps in the refugee support system included inadequate housing, a lack of financial and economic support, challenges with a lack of psychosocial support for pregnant women and support for disabled youth. Despite these challenges, families and community workers reported informal community support as a strong mediator of the challenges in Lebanon. Furthermore, the data find that organizations working with Syrian families are utilizing informal community support through capacity building, to create more effective and sustainable support services.

This study provides an overview of strengths and gaps in supports identified by refugees themselves. The research will inform the development and improvement of better support systems in Lebanon and in other refugee–hosting contexts.

The outset of the COVID-19 pandemic caused disruptions of all forms in the supply chain globally for almost two and a half years. This study identifies various challenges in the effective functioning of the existing supply chain during COVID-19. The focus is to see the disruptions impacting the energy storage supply chains.

The procedure entails a thorough analysis of scholarly literature pertaining to various supply chain interruptions, confirmed and verified by experts working in an energy storage company in India. These experts also confirmed the occurrence of more disruptive factors during their interviews and questionnaire survey. Moreover, this process attempts to filter out the relevant causal disruption factors in an energy storage company by using the integrated approach of qualitative and quantitative methodologies.

The results provide practical insights for the company management in planning and devising new strategies to manage supply chain disruptions. Supply chains for companies in other industry sectors can also benefit from the proposed framework and results in making them more robust to counter future disastrous events.

The study provides an easily adaptable decision framework to different industries by closely examining supply chain disruptions and identifying associated causes for building a robust supply chain focused on the energy storage sector. It examines four disruption dimensions and investigates possible outcomes and impacts of disruptions.

The purpose of this study is to examine the role of knowledge management practices, organisational commitment and capacity building on employee performance in the hotel industry. This study also investigated the mediating role of organisational commitment and capacity building between knowledge management practices and employee performance.

A quantitative approach and questionnaire survey were used to collect data from hotel employees from Malaysia. Self-administered questionnaires were distributed to collect data from 291 participants, and partial least squares structural equation modelling was used to analyse the hypotheses.

The results of this study confirm that knowledge management practices positively and significantly affect knowledge-employee performance. Employees achieve this performance through the mediating influence of organisational commitment and capacity building culture.

This study offers several implications for Malaysian practitioners and policymakers regarding learning and knowledge management practices in the hospitality industry. The results suggest that organisations can manage knowledge assets and key processes of the organisational environment to create and use knowledge to improve sustainable employee performance through knowledge management practices.

This study sheds light on the knowledge management literature by examining the effect of knowledge management practices on organisational commitment, particularly in the hospitality industry in Malaysia.