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We survey banks to construct national estimates of total noncash payments by type, payments fraud and related information. The survey is designed to create aggregate total estimates of all payments in the United States using data from responses returned by a representative, random sample. In 2016, the number of questions in the survey doubled compared with the previous survey, raising serious concerns of smaller bank nonparticipation. To obtain sufficient response data for all questions from smaller banks, we administered a modified survey design which, in addition to randomly sampling banks, also randomly assigned one of several survey forms, subsets of the full survey. This case study illustrates that while several other factors influenced response outcomes, the approach helped ensure sufficient response for smaller banks. Using such an approach may be especially important in an optional-participation survey, when reducing costs to respondents may affect success, or when imputation of unplanned missing items is already needed for estimation. While a variety of factors affected the outcome, we find that the planned missing data approach improved response outcomes for smaller banks. The planned missing item design should be considered as a way of reducing survey burden or increasing unit-level and item-level responses for individual respondents without reducing the full set of survey items collected.

In applied psychology research settings, such as criminal psychology, missing data are to be expected. Missing data can cause problems with both biased estimates and lack of statistical power. The paper aims to discuss these issues.

Recently, sophisticated methods for appropriately dealing with missing data, so as to minimize bias and to maximize power have been developed. In this paper the authors use an artificial data set to demonstrate the problems that can arise with missing data, and make naïve attempts to handle data sets where some data are missing.

With the artificial data set, and a data set comprising of the results of a survey investigating prices paid for recreational and medical marijuana, the authors demonstrate the use of multiple imputation and maximum likelihood estimation for obtaining appropriate estimates and standard errors when data are missing.

Missing data are ubiquitous in applied research. This paper demonstrates that techniques for handling missing data are accessible and should be employed by researchers.

The purpose of this paper is to focus on the case where – by design – one needs to impute cross-country cross-survey (CCCS) data (situation typical for example among multinational firms who are confronted with the need to carry out comparative marketing surveys with respondents located in several countries). Importantly, while some work demonstrates approaches for single-item direct measures, no prior research has examined the common situation in international marketing where the researcher needs to use multi-item scales of latent constructs. The paper presents problem areas related to the choices international marketers have to make when doing cross-country/cross-survey research and provides guidance for future research.

Multi-country sample of real data is used as an example of cross-sample imputation (292 New Zealand exporters and 302 Finnish ones) the international entrepreneurial orientation (IEO) data. Three variations of the input data are tested: first, imputation based on all the data available for the measurement model; second, imputation based on the set of items based on the invariance structure of the joint items shared across the two groups; and third, imputation based both on examination of the invariance structures of the joint items and the performance of the measurement model in the group where the full data was originally available.

Based on distribution comparisons imputation for New Zealand after completing the measurement model with Finnish data (Model C) gave the most promising results. Consequently, using knowledge on between country measurement qualities may improve the imputation results, but this benefit comes with a downside since it simultaneously reduces the amount of data used for imputation. None of the imputation models leads to the same statistical inferences about covariances between latent constructs than as the original full data, however.

Considering multiple imputation, the present exploratory study suggests that there are several concerns and issues that should be taken into account when planning CCCSs (or split questionnaire or sub-sampling designs). Even if there are several advantages available for well-implemented CCCS designs such as shorter questionnaires and improved response rates, these concerns lead us to question the appropriateness of the CCCS approach in general, due to the need to impute across the samples.

The combination of cross-country and cross-survey approaches is novel to international marketing, and it is not known how the different procedures utilized in imputation affect the results and their validity and reliability. The authors demonstrate the consequences of the various imputation strategy choices taken by using a real example of a two-country sample. The exploration may have significant implications to international marketing researchers and the paper offers stimulus for further research in the area.

Accounting and decision making rely heavily on forecasts. For several reasons, we should expect ongoing increases in forecasting accuracy. The purpose of this paper is to test the hypothesis of improved forecasts over time.

The paper analyzes original monthly sales plans and current data for three different car models in six different countries over 15 years and over several product life cycles (PLCs). Forecasting accuracy is calculated as one minus forecasting error. Forecasting error is measured with MAD/MEAN for periods of years or relative deviations per month. The hypothesis of decreasing forecasting errors is tested with the non‐parametric Mann/Kendall trend test. Additional interviews with managers were conducted to elicit details of internal forecasting organization and instruments.

The paper finds no evidence of increased forecasting accuracy in general over 15 years or over subsequent PLCs. This seems surprising, given improved statistical methods and software in general, and experience and learning effects of the organization itself. However, there is evidence from the case, that the reason lies in environmental uncertainty and volatility and not in internal factors within the control of the company.

Evidence from one case study is limited in its external validity. Future studies should analyze the forecasts of more companies, more industries and different forecasting objects, the latter including consumer, industrial goods and services. In the absence of further research, the results seem to negate the common assumption, that companies are generally able to make accurate forecasts, including those for accounting purposes. This hypothesis is clearly confuted.

The paper describes a methodology for companies to analyze their own forecasting accuracy and to identify possible reasons for a lack of accuracy, or basic approaches to increasing it.

Most studies on forecasting accuracy rely on interviews and questionnaires, entailing bias that is difficult to control. Few studies analyze archival data in order to measure forecasting accuracy; so that our study avoids much of the bias mentioned above. Despite the inevitable limitations of case studies, a study such as the present one at least allows us to dispute a common hypothesis about forecasting accuracy in practice.

Urban built-up has been increasing exponentially in the world. Urban population growth and migration are depleting the land resources and creating thermal discomfort. Cities all around the world are facing urban heat island effects and increased temperatures. This study aims to map land cover and formulate local climate zones for enhancing urban resilience against disaster and climate risks.

This study uses exploratory research to identify local climate zones for Lahore, Pakistan. Landsat 8 imagery was used to develop a land use land cover map. For mapping local climate zones, the standard World Urban and Access Portal Tool procedure was used.

Results have revealed that Lahore has grown exponentially. Compact low rise and open low rise were the two most common local climate zones prevalent in the city. In contrast, the outer regions of the city consisted of LCZ D (low plants) and LCZ F (bare soil).

This study highlights the need to consider local climate zones in future development plans and policies for ensuring sustainable, resilient and climate-friendly cities.

Local climate zone studies are missing in Pakistan. This study has empirically analyzed the ground situation of local climate zones for Lahore metropolitan city. This study will provide baseline support for future studies on urban heat island and climate change adaptation planning.

This study investigates when and why negative organization-related career shocks affect career optimism, which is a positive career-planning attitude. The indirect effect of negative organization-related career shocks on career optimism via job insecurity and the role of perceived organizational career support as a first-stage moderator were investigated.

Three-wave time-lagged data from a sample of 728 employees in Switzerland was used. Time-lagged correlations, an indirect effect model and a conditional indirect effect model with bootstrapping were used to test the hypotheses.

First, this study showed a significant negative correlation between negative organization-related career shocks (T1) and career optimism (T3), a positive correlation between negative organization-related career shocks (T1) and job insecurity (T2) and a negative correlation between job insecurity (T2) and career optimism (T3). Second, findings revealed that negative organization-related career shocks (T1) have a negative indirect effect on career optimism (T3) via job insecurity (T2). Third, perceived organizational career support (T1) buffers the indirect effect of negative organization-related career shocks (T1) on career optimism (T3).

This study provides an initial examination of the relationship between negative organization-related career shocks and career optimism by applying assumptions from the JD-R model and Conservation of Resources theory. Implications about how to deal with negative career shocks in HRM and career counseling are discussed.

Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

This three-wave longitudinal study explored the relation between discrimination-related stress and behavioral engagement among urban African-American and Latino adolescents, and the moderating effect of school-based social support.

A sample of 270 African-American and Hispanic/Latino adolescents attending urban public high schools completed three annual surveys starting with 10th grade.

Growth curve analysis revealed that discrimination-related stress was associated with decreased behavioral engagement over time.

School-based social support moderated this effect in that discrimination-related stress had less of an impact on behavioral engagement as level of school-based social support increased.

School-based supportive relationships serve as a protective factor for urban African-American and Latino youth, helping them remain engaged in school as they deal with the negative effects of discrimination-related stress.

The findings reveal that the development of positive, supportive relationships in school seems to be a malleable variable that interventionists and educational advocates can focus on in an effort to bolster academic achievement among academically stigmatized youth.