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The study aims to explore the discrepancy between the subjective and objective debt burdens across various household socio-demographic and debt characteristics. Additionally, it seeks to establish an optimal debt service-to-income ratio (DSTI) threshold for identifying over-indebtedness.

This study utilized a sample of 1,004 respondents from a nationwide survey conducted among Polish indebted households. A discrepancy ratio (DR) measure was proposed to evaluate the divergence between subjective and objective over-indebtedness. Binary logistic regression was employed to estimate the probability of being subjectively and objectively over-indebted, as well as the discrepancy between the two measures of over-indebtedness. The study also employed numerical simulations to determine the optimal DSTI threshold for identifying over-indebted households in general and based on their socio-economic characteristics.

The study established a debt service-to-income ratio (DSTI) threshold of 20% to minimize the discrepancy between subjective and objective debt burden, which is lower than thresholds found in other studies aimed at identifying over-indebted households. Age, number of loans, self-perceived needs satisfaction and type of debt were identified as significant socio-economic and debt-related determinants of over-indebtedness. Household socio-economic and debt-related characteristics significantly influence the threshold for identifying over-indebtedness using DSTI. It can vary widely, ranging from as low as 11% for well-educated women with multiple loan commitments to 43.7% for young males with vocational education, high incomes and originating from households with four or more members.

The paper proposes a more comprehensive approach to debt burden analysis by introducing a new methodology for determining a debt service-to-income (DSTI) threshold that could serve as a measure of over-indebtedness based on the discrepancy between subjective and objective over-indebtedness. It also emphasizes the significance of socio-economic and debt-related factors in evaluating subjective and objective over-indebtedness.

Everyday users of professional networks such as LinkedIn are flooded by posts presenting the achievements of their connections (e.g. I got a new job/award). The present research takes a self-discrepancy perspective to examine the mixed-emotional and behavioral consequences of viewing such idealized self-promotional content on professional networks.

The emotional and behavioral consequences following viewership of idealized self-promotional content on LinkedIn are explored through one pilot study (N = 109) and one online experiment (N = 714), which is evaluated using structural equation modeling.

Viewership of idealized self-promotional content on professional social networking sites acts as an emotional double-edged sword for LinkedIn users. Users feel both dejection and symhedonia (i.e. happiness for others), dependent on their reported career-based self-discrepancy. We find the experience of symhedonia to be bound by the relational closeness of the poster (acquaintance vs close friend). Furthermore, we show how resultant emotions drive self-regulatory compensatory IT-use behaviors (i.e. direct resolution, fluid compensation, dissociation, and escapism).

We offer four distinct contributions. Firstly, we disentangle inconsistent findings of mixed emotions by introducing symhedonia to IT literature. Secondly, we investigate the boundary condition of relational closeness. Thirdly, we extend our findings by investigating compensatory-consumption behaviors that stem from mixed-affective outcomes. Finally, we do so in the context of professional networks, which are greatly understudied and are distinctive from personal networks. Practical implications are discussed.

The purpose of this paper is to explore the underlying structure of employees' justice perceptions in the context of their organizations' performance appraisal practices.

Ten multi‐item scales were designed to measure the perceived fairness of performance appraisal practices. A nested confirmatory factor analysis of employee responses (n=188) compared the four justice dimensions (i.e. procedural, distributive, interpersonal, informational) to five plausible alternatives. Construct validity was demonstrated through a structural equation model of matched employee and supervisor responses (n=117).

The confirmatory factor analysis showed evidence of four distinct but highly correlated justice constructs. Results supported hypothesized relationships between procedural justice and helpful behaviors toward the organization via appraisal system satisfaction; distributive justice with appraisal satisfaction; and interpersonal and informational justice and helpful behaviors toward the supervisor via supervisor satisfaction.

This study underscores the importance of fostering perceptions of justice in the context of performance appraisal. The scales developed in this study could be used to isolate potential problems with an organization's performance appraisal practices. Originality/value – The paper integrates prior research concerning the positive effects of procedural, distributive, interpersonal, and informational justice on affective and behavioral responses towards performance appraisals.

Correlates discrepancies between the importance attached to particular cultural values and their presence within a large professional services firm. Considers three types of correlates: personal demographics, work setting factors and affective responses to work. Data were collected from 1,608 women and men using anonymously completed questionnaires. Cultural values were rated at significantly higher levels of importance than they existed in the firm in all but one case. There was no correlation between their perceived importance and actual existence. Women and men reporting greater value discrepancies described the work setting in more negative ways and reported both less satisfaction with the firm and lower quality service and products provided by the firm.

When managers set aspirations for their firms, they typically compare their own firms' performance to past aspirations as well as to the performance of social reference groups. The authors explore how firm generic strategy affects managers' adaptation of firm aspirations in response to feedback from three social reference groups that vary in terms of breadth (population average, strategic group, and one direct rival).

The authors propose that firm generic strategy (low-cost or differentiation) functions as an organizational information filter through with managers interpret performance feedback. The authors test for whether generic strategy has a moderating effect on the influence of performance feedback from social reference groups.

Based on a longitudinal sample of US airlines, the study shows that all firms are influenced most strongly by their strategic groups. Low-cost and differentiation generic strategies differ in terms of which social reference group motivates a larger reaction when overperforming: low-cost firms are more influenced by the population average which is contributed to by the entire industry than are differentiating firms, while differentiating firms are more swayed by the narrow focus of their direct rivals than are low-cost firms.

Although firm strategy represents a core decision at the firm level, to the best of the authors’ knowledge, performance feedback research, surprisingly, has not yet integrated generic strategy into its models.

The purpose of the paper is to examine the influence of consumers' perceived knowledge, knowledge discrepancy and confusion on the intention to purchase farmed fish (FF) via a survey design regarding perceptions, buying and consumption practices of urban households in Chittagong, Bangladesh.

The samples of 498 households were selected from a stratified cluster from the Chittagong city and were interviewed using a structured questionnaire. The data have been analysed using exploratory factor analysis and structural equation modelling.

The results show that consumers' subjective knowledge (SK) is significant for purchase intention whereas objective knowledge (OK) is not. Again, consumers' SK, OK, knowledge discrepancy and confusion have no influence in forming consumers' attitude towards FF. However, consumers who overestimate their actual level of knowledge hold negative attitude towards FF and vice versa. Furthermore, consumers' OK affects their confusion inversely although it does not influence the purchase intention significantly.

If the marketers can frame a more engaging means of communication and knowledge enhancement plan, consumers' attitude and purchase intention regarding FF will be signified.

This is the first study that fundamentally contributes to the scientific research in that it measures the knowledge discrepancy of consumers regarding FF. In addition, this study substantiates that low objective knowledge leads to confusing consumers at the time of purchasing. The effect of overestimating the level of knowledge as well as underestimating the level of knowledge in explaining the purchase intention of FF would be a supplementary addition.

Pesaran and Smith (2011) concluded that Dynamic Stochastic General Equilibrium (DSGE) models were sometimes a straitjacket which hampered the ability to match certain features of the data. In this chapter, the authors look at how one might assess the fit of these models using a variety of measures, rather than what seems to be an increasingly common device – the Marginal Data Density. The authors apply these in the context of models by Christiano, Motto, and Rostagno (2014) and Ireland (2004), finding they fail to make a match by a large margin. Both of these models feature more shocks than observed variables, resulting in the empirical shocks having a singular density, and so making them correlated. When correlated one can neither interpret impulse responses nor perform variance decompositions. Against this, there is a strong argument for having a straitjacket, as it enforces some desirable behavior on models and makes researchers think about how to account for any non-stationarity in the data. The authors illustrate this with examples drawn from the SVAR literature and also more eclectic models such as Holston, Laubach, and Williams (2017) for extracting an estimate of the real natural rate.

Field survey studies undertaken in Nigeria, Korea, China and India explored the way inner‐age satisfaction is experienced in those culturally diverse societies. Chronologically 20 to 59 year old respondents’ inner‐age satisfaction was gauged as the average difference between feel, look, do, and interest cognitive (self‐perceived) and desired (ideal) inner‐age dimensions. Analyses of covariance (with chronological age factored out) across the four nations showed Nigeria to differ significantly in terms of inner‐age satisfaction from each Asian population, contrary to the Asian societies where no differences were found across samples (except between Korea and India where inner‐age satisfaction differed at a p .05). High levels of satisfaction with inner‐age (coming about when cognitive and desired ages are equal) commonly transpired: 31.4 per cent of Indian, 36.9 per cent of Nigerian, 44.3 per cent of Chinese, and 44.9 per cent of Korean respondents. Age dissatisfaction in an elder direction (ideal age older than self‐perceived age) was atypical and happened most often among Nigerian (23.4 per cent) and least among Korean subjects (10.7 per cent). In contrast, wishing for a younger innerage was a commonplace phenomenon in India (50.6 per cent of the sample), as well as in China where it occurred the least (36.6 per cent). The study’s findings imply the universal nature of the way human beings (irrespective of culture) perceive and feel about inner‐age, as well as the potential of an inner‐age satisfaction psychographic as a relevant consumer behavior segmentation trait for marketing planners of age‐sensitive products and services who seek to standardize their global branding and distribution.

This paper aims to examine the accuracy of the trade statistics between the People’s Republic of China and 20 Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela.

This paper contrasts the mirror trade statistics between China and 20 Latin American countries during 2009-2014, after adding to the Chinese side the trade figures corresponding to Hong Kong and adjusting for some valuation issues. Using the resulting panel data, the paper then explores some of the possible explanatory variables, in the case of Latin America, which can account for the significant trade misinvoicing that is found among most of the countries involved.

Trade misinvoicing, be that from the part of China or of its partners, varies substantially across Latin America. It is quite large in the case of some countries such as Bolivia, Costa Rica, Mexico, Panama and Paraguay, and, on the opposite side, relatively small in the case of other countries such as Argentina, Brazil, Chile, Guatemala and Venezuela. It is found that, from a Latin American perspective, trade misinvoicing is positively related to the countries’ lack of statistical capacity and their degree of financial openness.

This is the first empirical paper that examines the mirror trade statistics between China and Latin American.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.