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This study aims to compare the rating dynamics of the same hotels in two online review platforms (Booking.com and Trip Advisor), which mainly differ in requiring or not requiring proof of prior reservation before posting a review (respectively, a verified vs a non-verified platform).

A verified system, by definition, cannot host fake reviews. Should also the non-verified system be free from “ambiguous” reviews, the structure of ratings (valence, variability, dynamics) for the same items should also be similar. Any detected structural difference, on the contrary, might be linked to a possible review bias.

Travelers’ scores in the non-verified platform are higher and much more volatile than ratings in the verified platform. Additionally, the verified review system presents a faster convergence of ratings towards the long-term scores of individual hotels, whereas the non-verified system shows much more discordance in the early phases of the review window.

The paper offers insights into how to detect suspicious reviews. Non-verified platforms should add indices of scores’ dispersion to existing information available in websites and mobile apps. Moreover, they can use time windows to delete older (and more likely biased) reviews. Findings also ring a warning bell to tourists about the reliability of ratings, particularly when only a few reviews are posted online.

The across-platform comparison of single items (in terms of ratings’ dynamics and speed of convergence) is a novel contribution that calls for extending the analysis to different destinations and types of platform.

The purpose of this paper is to examine the accuracy of the US Department of Labor's Occupational Information Network (O*NET), which, replacing the Dictionary of Occupational Titles, analyzes jobs via a hierarchical taxonomy of work in which all task‐level activities are summarized into a 42‐construct taxonomy of first‐order generalized work activities (GWAs).

This study examined the degree of convergence between ratings made using the holistic‐judgment process in the O*NET (which directly rates GWAs using single‐item scales) vs traditional decomposed‐judgment methods which statistically combine ratings of multiple activity items for each GWA.

Analysis of holistic O*NET general work activity ratings with decomposed common‐metric questionnaire (CMQ) ratings revealed poor convergence between holistic vs decomposed methods, low interrater agreement and a tendency for incumbents to rate higher than job analysts.

It is believed that these results raise significant questions regarding the O*NET's plan to rely on unverified holistic ratings obtained from relatively small, volunteer samples of job incumbents to maintain its database over time. There is a clear risk of producing a database of uncertain quality and comparability with the existing analyst ratings. It is concluded that the criteria of quality, accuracy and verifiability should be paramount in efforts to develop a national database of occupational information.

This study is the only empirical analysis of the degree of convergence between ratings made using the holistic‐judgment O*NET and a traditional decomposed‐judgment job analysis. Because job analysis forms the foundation for many human resources functions, effectively setting the standards that drive recruiting efforts, establishing the criteria that are used in hiring, promoting, evaluating, and equitably compensating employees, and forming the basis for many employee training programs, it is absolutely essential that any data source utilized for these purposes should be both accurate and verifiable. This study not only furthers efforts to tests the validity of the O*NET, it also provides empirical evidence of its potential shortcomings.

This paper discusses the nominal and real convergence regarding Greece being a country-member of the European Union (EU), and of the Economic and Monetary Union (EMU). We argued that nominal convergence is relative to Maastricht criteria when real convergence has been investigated through six different axes: (1) the five Maastricht Criteria, (2) the GDP per capita in PPP prices, (3) the real GDP growth rates, (4) the minimum wages, (5) the HDI index development, and (6) the unemployment rates. We concluded for the case of Greece that by utilizing alternative indicators, such as the Maastricht criteria, and the above criteria only nominal convergence exists while real convergence appears to be a long-term target with many obstacles. In particular, Greece has managed to achieve the criteria proposed by the EMU (Maastricht Criteria) for membership, decisively different levels of unemployment, wages, and GDP growth rate/GDP per capita in PPP prices, and different human development indexes appear for the case of Greece.

The purpose of this paper is to assign topic‐specific ratings to web pages.

The paper uses power iteration to assign topic‐specific rating values (called relevance) to web pages, creating a ranking or partial order among these pages for each topic. This approach depends on a set of pages that are initially assumed to be relevant for a specific topic; the spatial link structure of the web pages; and a net‐specific decay factor designated ξ.

The paper finds that this approach exhibits desirable properties such as fast convergence, stability and yields relevant answer sets. The first property will be shown using theoretical proofs, while the others are evaluated through stability experiments and assessments of real world data in comparison with already established algorithms.

In the assessment, all pages that a web spider was able to find in the Nordic countries were used. It is also important to note that entities that use domains outside the Nordic countries (e.g..com or.org) are not present in the paper's datasets even though they reside logically within one or more of the Nordic countries. This is quite a large dataset, but still small in comparison with the entire worldwide web. Moreover, the execution speed of some of the algorithms unfortunately prohibited the use of a large test dataset in the stability tests.

It is not only possible, but also reasonable, to perform ranking of web pages without using Markov chain approaches. This means that the work of generating answer sets for complex questions could (at least in theory) be divided into smaller parts that are later summed up to give the final answer.

This paper contributes to the research on internet search engines.

Assessment of African financial development dynamic convergences in money, credit, efficiency and size. The paper aims to discuss these issues.

The empirical evidence is premised on 11 homogenous panels based on regions (Sub-Saharan and North Africa), income-levels (low, middle, lower-middle and upper-middle), legal-origins (English common-law and French civil-law) and religious dominations (Christianity and Islam). The paper examines convergence in financial intermediary dynamics of depth, efficiency, activity and size.

Findings suggest that countries with small-sized financial intermediary depth, efficiency, activity and size are catching-up countries with large-sized financial intermediary depth, efficiency, activity and size, respectively. The paper also provide the speeds of convergence and time necessary to achieve a full (100 percent) convergence.

The presence of strong links among African banking sectors may present little opportunity for portfolio diversification. The convergence patterns show positive steps toward regional integration. As a policy implication, African governments should not relent in structural and institutional reforms.

It is the first critical assessment of convergence in financial intermediary development dynamics in the African continent.

This paper reviews one of the crucial issues in the recent growth literature concerning the hypothesis of cross country convergence of levels and growth rates of income per capita implied by the neo‐classical growth model, both in the Solow‐Swan and Rampsey‐Cass‐Koopmans versions. The alternative endogenous growth models, consistent with permanent income inequality, are considered. Convergence to a common income level versus divergence is discussed from a theoretical point of view. Then, empirical tests of the convergence property are presented. What emerges is that Barro type regressions and their findings about “conditional” convergence are questionable and cannot be used to give a definitive response on this issue.

Self-report questionnaires are the predominant method used in human resource management (HRM) research to assess employees’ work-related psychological constructs (e.g., processes, states, and attributes). However, this method is associated with significant shortcomings, including the introduction of self-serving bias and common method variance when used exclusively. In this chapter, the authors challenge the assumption that individuals themselves are the only accurate source of the self-focused information collected in HRM research. Instead, the authors propose that other-ratings – ratings of a target individual that are provided by a workplace observer, such as a coworker, supervisor, or subordinate – can accurately assess commonly measured work-related psychological constructs. The authors begin by explaining the advantages of other-ratings for HRM research and practice, reviewing the history of other-ratings and how they emerged in the personality and person-perception literature, and outlining how they have been used in HRM research to date. Then, the authors build upon Funder’s (1995) realistic accuracy model to develop a theoretical argument detailing why workplace others should be able to accurately judge how another employee thinks and feels about work. Next, the authors highlight existing evidence in the literature on the accuracy of other-ratings and present the results of a preliminary meta-analysis on the ability of other-ratings to predict self-ratings of work-related psychological constructs. Finally, the authors discuss potential moderators of other-rating accuracy and reflect on a number of practical considerations for researchers looking to use other-ratings in their own work. The authors intend for this chapter to meaningfully contribute to the larger conversation on HRM research methods. Other-ratings are a simple, yet powerful, addition to the methodological toolkit of HRM researchers that can increase flexibility in research design and improve the overall quality of research.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

The cyclical behavior of US crime rates reflects the dynamics of crime in the country. This paper aims to investigate the US's club convergence of crime rates to provide insights into whether the crime rates increased or decreased over time. The paper also analyzes the factors influencing the probability of states converging to a particular convergence club of crime.

The analysis is based on balanced panel data from all 50 states and the district of Columbia on violent and property crime rates covering 1976–2019. This yields a cross-state panel of 2,244 observations with 55 time periods and 51 groups. In addition, the author used a club clustering procedure to investigate the convergence hypothesis in the study.

The empirical results support population convergence of violent crime rates. However, the evidence that supports population convergence of property crime rates in the study is not found. Further analysis using the club clustering procedure shows that property crime rates converge into three clubs. The existence of club convergence in property crime rates means that the variation in the property crime rates tends to narrow among the states within each of the clubs identified in the study. Analysis based on an ordered probit model identifies economic, geographic and human capital factors that significantly drive the state's convergence club membership.

The central policy insight from these results is that crime rates grow slowly over time, as evident by the convergence of violent crime and club convergence of property crime in the study. Moreover, the existence of club convergence of property crime is an indication that policies to mitigate property crime might need to target states within each club. This includes the efforts to use state rather than national crime-fighting policies.

As crimes are committed at the local level, this study's primary limitation is the lack of community-level data on crime and other factors considered. Analysis based on community-level data might provide a better representation of crime dynamics. However, the author hopes to consider this as less aggregated data are available to use in future research.

The paper provides new insights into the convergence of crime rates using the club convergence procedure in the USA. This is considered an improvement to the methods used in the previous studies.

The purpose of this paper is to estimate the impact of the COVID-19 pandemic on the patterns of convergence/divergence among the districts in India. Specifically, this paper investigates if the impact is heterogeneous among different cohorts of districts (based on income distribution). The differential impact may lead to heterogeneous long-run growth paths, resulting in unbalanced development across regions within the country. A study of convergence can ascertain the possible trajectory of such development across regions. Investigation of this phenomenon is the primary aim of this study.

This paper uses the panel regression method for estimation. This paper uses high-frequency nighttime light intensity data as a proxy for aggregate output.

The authors observe a significant reduction in the convergence rate as a result of the pandemic. Across the cluster of districts, the drop in ß-convergence rate, compared to the pre-pandemic period, varied from approximately 33% for the poorer districts to close to zero for the richest group of districts. These findings suggest that the pandemic may lead to a wider disparity among different regions within the country.

This paper contributes to the literature in the following ways. First, to the best of the authors’ knowledge, this is the first paper to investigate the impact of COVID-19 on the convergence rate. A detailed look into the possible disparity in convergence among various regions is critical because a larger drop in convergence, especially among the poorer regions, may call for policy attention to attain long-term equitable development. The authors perform this exercise by dividing the districts into four quantile groups based on the distribution of night-light intensity. Second, while previous studies on convergence using nighttime light data have used a cross-sectional approach, this study is possibly the first attempt to use the panel regression method on this data. The application of this method can be useful in tackling district-level omitted variables bias. Finally, the heterogeneity analysis using different quantiles of the distribution of night-light intensity may help in designing targeted policies to mitigate the disparity across districts due to the shock.