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This study aims to examine student perceptions regarding the mid-semester transition from face-to-face to online delivery in an accounting course during spring 2020.

Due to the COVID-19 pandemic, numerous universities and colleges worldwide transitioned from face-to-face instruction to online delivery during spring 2020. We find some evidence in line with prior literature that COVID-19 affected student learning experience from various aspects.

Thanks in part to effective teaching techniques implemented by the instructor during the transition, including online lecture videos recorded by the instructor, online class materials, early posting of answer keys, frequent communication through emails and bonus points for watching lecture videos, students still perceived their learning outcomes positively in general.

These teaching techniques can be used to enhance student learning experience and satisfaction during class modality transitions in unforeseen circumstances, for both hybrid and online business courses.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

The main purpose of this study is to examine the influence of two characteristics of supply chain management (SCM) (resilience and integration) on new product performance (NPP) via the mediation of marketing innovation orientation.

This study was designed by the quantitative method, and the research model was developed based on the resource-based view (RBV) theory of 211 managers from Israeli firms using SmartPls3 software.

The main finding that emerges from this study is that marketing innovation orientation serves as a full mediator in the relationship between supply chain resilience (SCR) and NPP as well as in the relationship between supply chain integration (SCI) and NPP. Thus, companies that wish to achieve a competitive advantage over their rivals should improve and strengthen their marketing innovation orientation. By doing so, they enhance the relationship between SCM and NPP.

The findings provide an applicable guideline for marketing managers. Managers should be ready to adapt to customers’ demands, environmental changes and, most importantly, disruptive events in a dynamic environment.

The current study sheds light on the mechanism for NPP via integrating suppliers, customers and the organization. So, managers should adopt SCR and integration to strengthen their marketing innovation orientation in order to achieve NPP.

The complexity and uncertainty of healthcare operations increasingly require agility to safeguard a high quality of care. Using a microfoundations of dynamic capabilities perspective, this study investigates the effects of nurses' implicit voice theories (IVTs) on the behaviors that influence their individual agility.

This research uses quantitative survey data collected from 2,552 Canadian nurses during the fourth wave of the Covid-19 pandemic in the fall of 2021. Structural equation modeling is used to test a conceptual model that hypothesizes the effects of three different IVTs on nurses' creativity, spontaneity, agility and the quality of care they deliver to patients.

The results reveal that voice-inhibiting cognitions (like “suggestions are criticisms for higher-ups”, “I first need a solution or solid data”, and “speaking up has negative repercussions”) negatively impact nurses' creativity and spontaneity in crafting solutions to problems they face daily. In turn, this affects nurses' individual agility as they attempt to adapt to changing circumstances and, ultimately, the quality of care they provide to their patients.

Even if organizations have little control over employees' pre-held beliefs regarding voice, they can still reverse them by developing and nurturing a voice-welcoming culture to boost their workers' agility.

This study combines two theoretical frameworks, voice theory and dynamic capabilities theory, to study how individual-level factors (cognitions and behaviors) contribute to nurses' individual agility and the quality of care they provide to their patients. It answers the recent calls of scholars to study the mechanisms through which healthcare operations can develop and sustain dynamic capabilities, such as agility, and better face the “new normal”.

This study examines the relationship between multi-layer supply chain flexibility (MSCF) and Supply chain resilience (SCR). Further, it looks at the moderating effect of environmental dynamism (ED) and supply chain risks (SCRI) on the relationship between MSCF and SCR.

Executives from the pharmaceutical, agri-food, electronics, automobile and textile industries were invited to complete a self-administered questionnaire. We received feedback from a total of 302 participants. Prior to conducting the primary analysis, we addressed the potential for nonresponse bias and verified the assumptions of homoscedasticity and normal distribution of the data. The reliability and validity of the constructs were established through confirmatory factor analysis. Structural equation modelling is employed for the purpose of conducting hypothesis testing.

The results demonstrate a notable influence of MSCF on SCR, particularly in settings characterized by high levels of ED and SCRI. The study highlights the importance of flexibility in multiple aspects of the supply chain to build resilience against a range of disruptions and uncertainties.

The study presents the fundamental role of Multi-Layer Flexibility in building up SCR. The results of this study reinforce the existing literature and offers empirical evidence for how ED, SCRI moderates the influence between MSCF to SCR. These results offer valuable information to both supply chain specialists and researchers for building comprehensive strategy to bring resilience in supply chains.

Grounded in dynamic capabilities theory, this study aims to examine how dynamic capabilities and a transactive memory system (TMS) can build the resilience of service organizations and improve their financial performance. Limited studies examine the link between a TMS and organizational resilience.

The authors test a theoretical model on a sample of 350 UK service firms that were impacted by the COVID-19 pandemic and analyze the data using partial least square structural equation modeling.

Results highlight the positive effects of a TMS and dynamic capabilities on organizational resilience. Only a TMS and organizational resilience have direct positive effects on financial performance.

To the best of the authors’ knowledge, this is the first study to ascertain the influence of a TMS on organizational resilience in service firms following adversity.

This study explored one form of remote working – work from home – to understand the impact of work disruptions on workers and human resource management (HRM) practice and therefore how to adapt to future crises. Specifically, the purpose is to understand the impact of events on employees when they are forced to move back and forth (yo-yoing) between work from home and a central office, using the case of Perth, Western Australia (WA).

Thirty-nine interviews with leaders, managers and co-workers working in private and public organisations suggest that forced changes to work arrangements affect job content and demands, well-being and potentially career trajectories.

The authors critically applied and developed the tenets of event system theory (EST) (novelty, disruption and criticality) by analysing an ongoing or “long” event rather than a discrete or time-limited one. The study found that the work-from-home experience influenced the individual perception of whether remote working would suit employees in the future, potentially influencing career paths. In addition, the selection of locations to live, preferred properties (e.g. home office space) and access to services (e.g. broadband infrastructure) has also become dominant features of work decisions.

While the study generated rich data, it is not without limitations. The participants were from one Australian state which may not reflect the COVID-19 experiences of other jurisdictions. In addition, the participants were mostly female so this may offer a different perspective than a more gender-balanced sample. The study was limited to the perspectives of employees and middle managers.

The authors note three areas of implications for practitioners. First, the findings suggest that strong organisational and HRM support assists employees to cope well with change. Second, the importance of technological and social preparedness in improving employee experiences highlights the role of HRM in job design. Third, it is key that salient events are recognised as potential determinants of career pathways.

The evidence from this research broadens the application of EST showing that forced flexibility has an influence on work arrangements by influencing a series of changes in features of work and experiences of employees. As such, this impacts employee's well-being and potentially future career decisions.

The purpose of this study is to assess the significance of employee work flexibility and the policy of the organization for survival during any crisis. This study also investigates the moderating role of leadership support (LS) during such turbulent conditions.

This study has used literature from the fields of organization performance, human resources and organization policy (OP), along with the theories of resource-based view (RBV) and dynamic capability view (DCV) to develop a conceptual model. Later, the conceptual model is validated using the structural equation modeling technique. The study used a survey method with a sample of 311 participants. These participants are employed as human resource managers (HRM) and other supportive workforce at different levels in the organizations.

The study shows that innovativeness and employee flexibility (EFL) are critical toward organizations’ survival during any crisis. Also, the study highlights the importance of OP and LS for the survival of organizations during and after any turbulent condition.

This study provides valuable inputs to the leadership teams of organizations, especially HRM. This research also provides food for thought for policymakers and researchers in the field of organizational performance. This study also contributes to the overall body of literature on organization analysis and extends the literature on RBV and DCV.

The study adds value to the overall body of literature on organization performance and capabilities along with human resource management. Few studies have nurtured issues on EFL during turbulent conditions. Also, there are limited studies in the areas of OP such as favorable and unfavorable policies toward employees. Thus, this study can be considered unique. Moreover, the study investigates the moderating role of LS which adds value toward the body of literature on organizational leadership capability.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

This study aims to understand the socioeconomic impact of flood events on households, especially household welfare in terms of changes in consumption and coping strategies to deal with flood risk. This study is based on Bihar, one of the most frequently flood-affected, most populous and economically backward states in India.

Primary data were collected from 700 households in the seven most frequently flood-affected districts in Bihar. A total of 100 individuals from each district were randomly selected from flood-affected villages. Based on a detailed literature review, an econometric (probit) model was developed to test the null hypothesis of the availability of consumption insurance, and the multivariate probability approach was used to analyze the various coping strategies of these households.

The results of this study suggest that flood-affected households maintain their consumption by overcoming various losses, including income, house damage and livestock loss. Households depend on financial transfers, borrowings and relief, and migrate to overcome losses. Borrowing could be an extra burden as the government compensates for house damage and crop loss late to the affected households. Again, there is no compensation to overcome livelihood loss and deal with occurrences of post-flood diseases, which further emphasizes the policy implications of strengthening the health infrastructure in the state and generating alternative livelihood opportunities.

This study discusses flood risk in terms of changes in household welfare, identifies the most effective risk-coping capabilities of rural communities and contributes to the shortcomings of the government insurance and relief model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0569