Search results

Focusing on the individual unit of analysis, we explore how workplace identification can explain why individuals engage in unethical behavior that benefits an organization (unethical pro-organizational behavior; UPB). Social identity theory (SIT) stipulates that we want membership within high status organizations and, at extreme levels, may put the organization’s needs above all else. In taking a holistic approach to identification, we investigated how a strong occupational identification can mitigate this desire to unethically help an organization; occupations are a separate identity source and contain codes of conduct that guide ethical behavior. Utilizing a sample of 236 accountants and financial professionals, results indicated that organizational identification and occupational identification alone did not significantly predict UPB, however, the interaction of these identities did. More specifically, organizational identification significantly positively predicted UPB only when occupational identification was extremely low in strength. This effect was found after controlling for relevant personality and cognitive mechanisms related to unethical behavior. Implications for a multidimensional identification view of unethical behavior are discussed.

This article explores the cultural politics of civic pride through Hull's year as UK City of Culture (UKCoC) in 2017. It unpicks some of the socio-political meanings and values of civic pride in Hull and critiques the ways in which pride, as an indicator of identity and belonging, was mobilised by UKCoC organisers, funders and city leaders. It argues for more nuanced and critical approaches to the consideration and evaluation of pride through cultural mega events (CMEs) that can take account of pride's multiple forms, meanings and temporalities.

A multidimensional, mixed methods approach is taken, incorporating the critical analysis of Hull2017 promotional materials and events and original interviews with a range of stakeholders.

The desire for socio-economic change and renewed identity has dominated Hull's post-industrial sense of self and is often expressed through the language of pride. This article argues that UKCoC organisers, cognisant of this, crafted and tightly controlled a singular pride narrative to create the feeling of change and legitimise the entrepreneurial re-branding of the city. At the same time, UKCoC organisers overlooked the opportunity to engage with and potentially reactivate the political culture of Hull, which like other “left behind” or “structurally disadvantaged” places, is becoming increasingly anti-political.

Through the case study of a relatively unresearched and under-represented city, this paper contributes to cultural policy literatures concerned with critically assessing the benefits and shortcomings of Cultural Mega Events and to a more specific field concerning Cities of Culture and the political cultures of their host cities. This paper also contributes to an emerging literature on the centrality of pride through the UK's post-Brexit Levelling Up agenda, suggesting that pride in place is becoming figured as a “universal theme” of the neoliberal city script.

The study utilized the consumption value theory to explore the motivational factors that define and differentiate the users and nonusers of fashion rental services

A focus group was conducted to generate an initial list of measurement items. These items were refined through a pretest and then used in a self-administered online questionnaire to collect data from a total of 300 users and 300 nonusers. The collected data were analyzed using factor analysis to identify the factors that define users and nonusers. A MANOVA was then conducted to explore the differences in the identified factors between users and nonusers.

Using factor analysis, nine factors were extracted across the five consumption values (functional, social, emotional, conditional and epistemic). MANOVA revealed a significant difference between users and nonusers across all factors. Further analyses suggested that the most differentiating factors are two emotional value factors and one social value factor.

Despite existing studies of fashion rental services, it is debatable whether the phenomenon is fully understood since previous studies primarily focus on consumers who engage in fashion renting services – there is a lack of focus on nonusers. This study provides unique contributions by exploring the phenomenon from both the user's and the nonuser's perspective.

Despite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require identifying who are the households that judge their income is insufficient to meet their basic needs, and what factors are associated with subjective poverty.

Households report the income level they judge is sufficient to make ends meet. Then, they are classified as being subjectively poor if their own monetary income is inferior to the level they indicated. Second, the study compares the performance of three machine learning algorithms, the random forest, support vector machines and least absolute shrinkage and selection operator (LASSO) regression, applied to a set of socioeconomic variables to predict subjective poverty status.

The random forest generates 85.29% of correct predictions using a range of income and non-income predictors, closely followed by the other two techniques. For the middle-income group, the LASSO regression outperforms random forest. Subjective poverty is mostly associated with monetary income for low-income households. However, a combination of low income, low endowment (land, consumption assets) and unusual large expenditure (medical, gifts) constitutes the key predictors of feeling poor for the middle-income households.

To reduce the feeling of poverty, policy intervention should continue to focus on increasing incomes. However, improvements in nonincome domains such as health expenditure, education and family demographics can also relieve the feeling of income inadequacy. Methodologically, better performance of either algorithm depends on the data at hand.

For the first time, the authors show that prediction techniques are reliable to identify subjective poverty prevalence, with example from rural China. The analysis offers specific attention to the modest-income households, who may feel poor but not be identified as such by objective poverty lines, and is relevant when policy-makers seek to address the “next step” after ending extreme poverty. Prediction performance and mechanisms for three machine learning algorithms are compared.

This paper deals with analyzing the relationships between the domains of the Sustainable Development Goals (SDGs) and the Missions envisaged by the National Recovery and Resilience Plan (NRRP). Specifically, the authors refer to Mission 2 (Green revolution and ecological transition) and to the statistical indicators related to the M2C1 component (Circular economy and sustainable agriculture).

The numerous data available were analyzed at a regional level using multivariate statistical methodologies (Totally Fuzzy and Relative method) capable of summarizing the various information to evaluate the current situation relating to the “circular economy and sustainable agriculture” component. The presence of multiple updated data allows for the development of a holistic approach to the evaluation of the local government policies in place and to be able to monitor the progress of the subsequent intervention policies of the Italian government.

No Findings.

NRRP represents an opportunity for development for the area, providing for reforms and substantial investments for the promotion of circular economy solutions, the improvement of the capacity for efficient and sustainable waste management, the strengthening of the infrastructures for treatment of waste and separate collection, the reduction of the North/South gap.

This paper aims to examine the mismatch between multidimensional deprivation and monetary poverty in identifying the poor in Egypt and investigates their determinants empirically.

The paper uses the Alkire-Foster multidimensional poverty measurement method using data from Egypt’s 2017/2018 Household Income, Expenditure and Consumption Survey (HIECS 2017/2018). Using a logistic regression model, the paper assesses the empirical relationship between multidimensional and monetary poverty and their determinants at the aggregate level and by dimension.

The paper demonstrates a significant mismatch between multidimensional and monetary poverty measures, underscoring their complementary nature. Statistics indicate that both measures overlap in classifying 35.81% of Egyptians, whereas monetary poverty ignores 63.12% of multidimensionally poor in at least one dimension. Regression estimates show a significant moderate negative association between expenditure per capita and multidimensional poverty and its dimensions. Moreover, they show that household head’s gender, age, education attainment, marital status, job proficiency, household size and location affect poverty mismatch and match in Egypt.

This paper offers Egyptian policymakers the multidimensional poverty index that enables more efficient designing and targeting of poverty alleviation programs and assessing current poverty alleviation programs to modify them if needed.

To the best of the authors’ knowledge, this study is the first to examine the mismatch between both poverty measures in Egypt, using the recent full data set of HIECS 2017/2018. This paper confirms that depending only on monetary measures can send inaccurate insights for crafting effective social policies. Also, it offers policymakers a comprehensive insight into the country’s poverty landscape, which enable more efficient design, targeting of poverty alleviation programs and monitoring their effectiveness.