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A significant amount of research in pricing has focused on price as a unidimensional construct – one consisting of a single number (e.g. $1.99). However, the evolving marketing environment, combined with notable growth in services and goods categories that require the communication of complex price information, has led to the use of multi‐dimensional prices. Multi‐dimensional prices consist of multiple numbers (e.g. $199 a month for 36 months) and as a result require the consumer to carry out specific mental computations to determine the cost of the offer. In this paper, empirical evidence on consumer difficulty in evaluating multi‐dimensional prices is examined. Then the strategic impact of such difficulties for pricing managers as well as regulators is examined. The paper concludes with a discussion of the implications of multi‐dimensional pricing on past research findings, and reflects on existing understanding of consumer response to price.

The purpose of this paper is to analyze the effect of the different components of off-farm income on multi-dimensional poverty. Furthermore, the study aims to measure multi-dimensional poverty and also identify the determinants of multi-dimensional poverty in Nigeria. The paper reveals the different contributions of the dimensions of education, health and living standard.

The study focuses on rural farm households in Nigeria. Data are obtained from the Nigeria General Household Survey, 2013. The survey covers both urban and rural areas of the 36 states of Nigeria. Owing to the interest of this study in the rural farm household’s sub-sector, a nationally representative sample of 836 rural farm households are selected for the study after the data merging process. Rural farm households in this paper earn 50 percent of their total income from crop and livestock production. The paper employs the Multi-dimensional Poverty Index (MPI) to measure multi-dimensional poverty across the six different geographical zones of Nigeria. The probit regression model is used to estimate and analyze the effect of off-farm income components on multi-dimensional poverty and also to identify the determinants of multi-dimensional poverty.

The results of the study show that among the off-farm income components, the non-farm wage income and non-farm self-employment income have negative association with multi-dimensional poverty. Findings show that multi-dimensional poverty is high in Nigeria with deprivations in health contributing the most. Northern Regions have a higher estimate. Results reveal that sex, age, number of adults, formal credit access, access to extension services and location characteristics are key determinants of multi-dimensional poverty. The MPI for Nigeria averaged 47 percent. Across regions, deprivation in the health dimension contributes about 44 percent to multi-dimensional poverty. Deprivation in living standards contributes 40.5 percent, while deprivation in education contributes 15.5 percent to multi-dimensional poverty.

Due to the nature of the data used, the health indicators (nutrition and child mortality) are absent but proxies are used instead. Future research could introduce gender dimensions.

Improving the involvement of rural farm households in non-farm self-employment sector could improve their livelihoods and prevent migration to urban centers, especially among the youths.

Improving the quality of health, education and living standards will lead to lower poverty levels in Nigeria. Farmers can best reduce their multi-dimensional poverty by engaging in more off-farm jobs.

This paper provides information to policy makers on the effect of different components of income from the off-farm sector on multi-dimensional poverty alongside with the determinants of multi-dimensional poverty at a national level for the rural farm households. By using MPI, the contribution of the different dimensions used in computing the MPI across the six geographical regions within the country is revealed. This provides policy makers with more information for development purposes.

The purpose of this study is to examine Chinese international students' narrative stories, experiences and racial dynamics while studying in the United States to argue that Chinese international students navigate multi-dimensional transitions and experiences in different stages. This study uses an AsianCrit lens to address the gap in existing research focusing on Chinese international students' narratives and experiences.

Narrative inquiry with a social constructivist paradigm was used to provide an in-depth exploration of Chinese international students' navigation and negotiation in multi-dimensional experiences. Three phases of semi-structured interviews and journal entries were utilized to examine participants' experiences and struggles while studying in the United States. Descriptive coding, deductive coding and restorying were used to analyze and feather narrators' voices and stories for interpretation.

The findings in this qualitative study demonstrate that Chinese international students have unique backgrounds, and their backgrounds shape their multi-dimensional transitions and experiences in the present and the future. The findings address students' nuanced experiences in academic transitions and non-academic transitions with an AsianCrit lens.

The study calls for higher education institutions to promote intercultural and international training for faculty and staff to better understand and support the unique needs of international students.

Using Multiple and Multi-dimensional Transitions theory with an AsianCrit lens helps make sense of the intersection of international student status, geopolitical tensions, racial dynamics and international student experiences.

Transformational leadership, flexibility and visibility improves project responsiveness to highly unpredictable and impactful events referred as the ‘black swans’ in mega projects (Bloch et al., 2012; Raziq et al., 2018; Zailani et al., 2016). However, these concepts have never been empirically tested in a single framework to determine their significant impact on multi-dimensional project success. The purpose of this paper is to investigate the interactional effects of project flexibility and project visibility on the relationship between transformational leadership and “multi-dimensions” of project success including meeting design goals; impact on customers and benefits to project-based organization.

Empirical data derived from cross-sectional survey of 160 project managers from telecom intensive companies in Pakistan were used to test the conceptual framework developed from recent literature. Partial least squares-structural equation modeling (PLS-SEM) provided detailed analysis of the measurement and structural model. The most recent reflective–formative PLS-SEM approach for higher-order constructs has been introduced.

The results indicate that project managers’ transformational leadership (β = 0.348, p < 0.01), project flexibility (β = 0.221, p < 0.01) and project visibility (β = 0.366, p < 0.01) are positively related with the multi-dimensional project success (second-order formative) construct. Interestingly, the relationship between transformational leadership and project success is influenced by significantly negative moderations established through project flexibility (β = −0.100, p < 0.01) and project visibility (β = −0.093, p < 0.05).

This study in the telecom sector examined the interactional effects of risk mitigating strategies (i.e. project flexibility and project visibility) on the relationship between transformational leadership and multi-dimensional project success. This study creates a basis for future investigations extending to various project types and relevant to different industries especially those involving higher-order (formative) assessments of project success.

The study findings assist project leaders to meet their escalating commitments in achieving project success from a multi-dimensional standpoint. Additionally, this study underscores a renewed perspective of transformational leadership and project outcomes. Despite prevailing understanding developed through prior research, transformational leadership may become less favorable for project success in conditions of increased flexibility and visibility in projects.

Earlier studies have overlooked the multi-dimensional nature of project success (second-order formative) construct, despite several attempts to examine the interplay between transformational leadership and project success. Based on the knowledge gap and non-existence of empirical evidence, the authors introduced and empirically tested the moderating role of project flexibility and project visibility in the relationship between transformational leadership and multi-dimensional project success.

Research in marketing indicates that consumers may be sensitive to the final digits of prices. For example, despite being substantively equivalent, a price such as $199 may create more favorable price perceptions than $200. However, existing research has primarily focused on the effects of price endings in the context of uni‐dimensional prices – prices consisting of a single number. Advertised prices in the marketplace are often multi‐dimensional, consisting of numerous price dimensions. In such pricing contexts, price endings may influence consumers’ ability to conduct the arithmetic required to compute the total advertised price. Examines the effect of various price ending strategies on consumers’ computational efforts. The findings indicate that the more commonly exercised price ending strategies tend to result in prices that are the most difficult for consumers to evaluate.

This paper aims to present a multi‐dimensional measure of poverty. The proposed method has been applied to the Panel Set of Belgian Households dataset for Belgium for the years between 1994 and 2000.

First, a common model is decided upon by exploratory factor analysis, and applied by confirmatory factor analysis. Cluster analysis (CA) is then used to separate the multi‐dimensional poor. Finally, the possible causes of multi‐dimensional poverty are surfaced by estimating a discrete duration model.

The proposed method reveals three dimensions of poverty: “material deprivation”, “social deprivation” and “psychological health”. Between 9 and 11 per cent of the representative sample of Belgian individuals are poor. The paper also identifies causes of poverty, including not having a job, not having the Belgian nationality, having a poor health or a disability, being lower educated, experiencing financial poverty, being divorced or widowed, living in the Walloon or Brussels regions, and having a bad psychological health.

Research implications include the use of polychoric and tetrachoric correlations as a starting point of factor analysis, as well as the combination of factor analysis and CA.

The paper proposes an alternative multi‐dimensional measure of poverty. It argues that previous measures may suffer from categorisation errors and suggests a solution to this problem. The advantages of the proposed method are that all information is used to disentangle the poor from the non‐poor and that dimensions of poverty are defined using the correlations between deprivations. Finally, the paper identifies “psychological health” as one of the dimensions of poverty.

Through an empirical study, this research proposes a multi-dimensional assessment method for Sustainable Building Design (SBD). This approach is adopted to investigate and evaluate the current practices of SBD and to provide a basis for refining such processes while reducing existing shortfalls. In doing so, a holistic sustainable framework for rating the sustainable performance of buildings is proposed.

To achieve the aforementioned purpose, this research (1) investigates the most current trends in SBD including the use of Building Information Modelling (BIM); (2) examines the practical issues of SBD; (3) proposes a multi-dimensional assessment method; and (4) compares 18 separate case studies in the three countries of Australia, United Kingdom and USA, as part of the SBD implementation. To compare these case studies, an additional SBD tool, Green Building Rating and Certification System (GBELS) was carefully selected and applied. Five core values of SBD were identified based on functionality, accessibility and productivity, which align with the GBELS outcomes.

This research found that, for the 18 examined buildings, the main issues in SBD in the three countries concerned environmental and ecological issues. These included the impacts of buildings on the environment, as well as issues concerning the buildings' life cycle analysis and assessment. It was also determined that energy usage, pollution reduction and climate change concerns were important inclusions in establishing these buildings, particularly in the USA.

To support the research aim, this paper explores the most innovative trends in SBD assessments including their Structural Health Monitoring (SHM), grade (stars) classification, Relative Weight (RW) and particularly GBELS. GBELS was selected for this research, since it is relatively new and there is little available literature discussing its adaptation. Accordingly, this research further evaluates the application of GBELS as a part of versatility in SBD multi-dimensional assessment method. As the basis of these tests, a total of 18 separate case studies are closely evaluated.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

The purpose of this study was threefold: (1) to measure farmers' health poverty, (2) to examine the effect of health vulnerability on health poverty and (3) to identify countermeasures that may alleviate health poverty in rural China.

This study built a health poverty measurement model based on the multi-dimensional poverty framework to evaluate farmers' health vulnerability. Further, this paper used an econometric model to assess the impact of health vulnerability on health poverty. The sample for this study comprised 1,115 rice farmers from Hubei province, China.

The medical affordability poverty ratio was 17.95%, where farmers in the low-income group faced severe medical affordability poverty (27.46%). Results from the multi-dimensional analysis showed that, the health poverty ratios were 17.95 and 30.50%, respectively. Our results indicated that climate change vulnerabilities, living habits, medical facilities and medical accessibility were positively related to health poverty, whereas the regular physical examinations reduced mental health poverty.

Based on this study's findings, we proposed that: (1) to address illness-induced poverty among members of the agricultural community, national and provincial strategies and programs grounded on a multi-dimensional health poverty framework ought to be formulated and implemented, (2) mechanisms of health knowledge exchange may facilitate the improvement of farmers' health status, (3) robust and comprehensive metrics should be employed to understand and improve farmers' ability to absorb and mitigate the negative health impacts and (4) the improvement in both quality and quantity for medical facilities and medical affordability in the rural areas should be key priorities in governmental-wide initiatives.

Existing studies for alleviating poverty caused by disease mainly focus on medical service support to those economic vulnerabilities after a disease happens. However, few studies have focused on the root causes of poverty caused by disease, particularly from the preventive perspective of health vulnerability. To fill this gap, this study, therefore, proposes the health poverty index and analyzes the impact of health vulnerability on health poverty.

This paper aims to identify religiosity scale usage in academic marketing articles and compare the effectiveness of different religiosity scales in predicting marketing and consumer behavior outcomes.

Articles (n = 397) in the top 20 marketing journals are reviewed and a follow-up study is conducted that compares 22 religiosity scales in predicting 18 marketing variables.

Most scales are from preexisting sources (64.3%), only 20% are multi-dimensional and over 58% are used in only one journal article. Only 22.5% of possible regressions in the follow-up study predicting marketing variables from religiosity scales were significant.

This research is limited by the journals and dependent variables chosen. Implications include diversify research topics, expand publication outlets, decrease use of author-generated scales, increase use of multi-item and multi-dimensional measures, replicate findings methodologically and conceptually and make cultural context adaptations.

Marketers would benefit from using preexisting scales, ensuring that religiosity is measured using a multi-item measure that contains appropriate items for the dominant religious beliefs of the sample, as well as consider multi-dimensional measures to best guide marketing strategy decisions, such as target market definition.

This is the first research study to compare the use of religiosity scales in marketing. This offers key value to the marketing literature by highlighting tactics to take to improve consistency in research practices to increase the comparability and accuracy of findings.