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Despite the present focus on improving the resilience of homes to flooding in UK flood risk management policy and strategy, a general measurement framework for determining levels of flood resilience in UK homes does not exist. In light of this, the aim of this study was to develop a means to evaluate the levels of resilience in flood-prone homes from the perspective of homeowners'.

A quantitative research methodology was employed, with empirical data obtained through a postal survey of homeowners who had experienced flooding. The responses received were then analysed using a combination of statistical techniques including agreement/reliability tests and multiple regression to develop a model of flood resilience.

A predictive model was developed that allows the resilience of a property to be quantified and measured as perceived by homeowners. The findings indicate that the main factors found to influence the level of flood resilience were: property type (PT), presence of cellar/basement (C/B), property wall type (PWT), property ground floor type (PGFT), kitchen unit type (KU), flood experience (FE), flood source (FS) and flood risk level (FRL).

The resulting model provides unique insights into resilience levels to the benefit of a range of stakeholders including policy makers (such as Defra/Environment Agency), Local Authority flood teams, property professionals, housing associations and homeowners. As a result, homeowners will be in a better position to determine which interventions should be prioritised to ensure better flood protection.

This is the first study of its kind to have rigorously quantified the level of flood resilience for individual homes. This study has quantified the effectiveness of individual resilience measures to derive the first reliable means to measure the overall levels of resilience at the individual property level. This is regarded as a significant contribution to the study of flood risk management through the quantification of resilience within individual UK homes, enabling the prioritisation of interventions and the overall monitoring of resilience.

Understanding the technical and socioeconomic dimensions to resilience is core to making a business case for property-level flood risk adaptation. The study investigates the socioeconomic factors that impact the technical dimensions to property-level flood risk adaptation, and whether there is a typology of households adopting similar adaptation measures in Port Harcourt.

Exploratory and inferential statistical analysis of data collated from 407 questionnaires was carried out. Using chi-square analysis, significant bivariate associations were sought between the level of uptake of different categories of property level adaptation and the socioeconomic characteristics of households. A two-step cluster analysis was used to explore discernible patterns of households implementing similar adaptation measures. Logistic regression analysis was further used to evaluate the extent to which socioeconomic parameters impact residents' willingness to undertake adaptation measures given the option of relocation.

The chi-square analysis highlighted a lack of significant association between some socioeconomic parameters and the uptake of individual adaptation measures. The regression analysis however showed that the socioeconomic parameters exert varying degrees of influence on the residents' willingness to undertake adaptation measures. Two homogeneous groups of residents with similar socioeconomic characteristics were identified via the cluster analysis but did not translate into strongly discernible adaptation differences/patterns.

The study shows that although socioeconomic parameters to some extent underlie the technical dimensions to flood resilience, there is no distinctive typology of households in Port Harcourt adopting a similar combination of measures.

The study offers insights into understanding property-level flood risk adaptation responses within the context of the developing world.

This study presents a model for assessing the effects of employee ICT training on organizations’ results. It also introduces digital transformation as a mediator between the two concepts and studies the role of organizational commitment and human capital in terms of digital transformation.

Surveys were completed by the CEOs of 184 Spanish companies, and their responses were analyzed with Partial Least Squares.

The results empirically analyze the proposed theoretical model and highlight the fact that human capital and organizational commitment partially mediate the link between ICT training and digital transformation. Furthermore, there is a direct relationship between ICT training and company performance.

Directors and managers should invest more resources in the human capital of their company through ICT training. In fact, it can improve organizational commitment, encouraging employees to adopt innovative behaviors, thus allowing for the necessary digital transformation.

Despite heavy theoretical emphasis on the study of the conditions necessary for the digital transformation of companies, few studies have empirically analyzed the effects of adopting certain practices for its implementation. This paper focuses on analyzing the effect of ICT training, which is configured as a tool capable of improving staff knowledge and increasing employee commitment. This is essential for adopting organizational change such as digital transformation.

The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).

Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.

The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.

The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.

The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.