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Surveys that collect data regarding behavior estimates are found in many fields including, but not limited to, those that conduct consumer research, health studies, sexual behavior, drug use, political polls, and many types of education studies. These studies typically use either vague behavioral quantifiers as the response set, or enumerated response sets where the respondent needs to select or tally the target behavior, or a combination of both types. This study aimed to investigate the relationship between these two methods of estimating educationally related behaviors.

Data for this study was taken from the 2010 administration of Beginning College Survey of Student Engagement (BCSSE), which is administered to incoming first‐year students. Respondents included 30,964 first‐year entering students from 81 higher education institutions in the USA. Data analysis was then carried out.

This study found that the more frequent the behavior, the shorter the time frame the respondent uses when estimating the behavior using enumerated strategies. In addition, this study showed that for many educationally relevant behaviors vague quantifiers are associated with increasing enumerated responses for the same behavior showing that two behavioral estimates are providing consistent estimations of the same behavior. Another equally important finding is that there were few meaningful group differences regarding these estimates.

Overall, the results from this study shed new light on interpreting behavior estimations using vague and enumerated responses.

The purpose of the paper is to assess the roots of stockpiling behaviors and to give a quantitative assessment of shopping frequency changes for emergency supplies during the coronavirus disease 2019 (COVID-19) pandemic. In addition, the authors aim to determine the sources that influenced emergency supply purchases during the COVID-19 outbreak.

The study used a polling or survey process implementation to collect the data on shopping patterns and to determine the drivers of stockpiling behaviors for the assessment. The polling was conducted using a snowball technique, and descriptive and regression analyses were used to define the roots of the stockpiling behaviors and the shopping frequency changes.

It was determined that 88.0% of end-consumers increased their shopping volumes for emergency supplies. An almost twofold increase in the average duration of usage for stockpiled goods (from 11 to 21 days) was also determined. Also revealed was a reduction in shopping frequency from an average of seven (pre-COVID-19 period) to five (first wave of COVID-19 pandemic) days. Such disproportional increases in purchase volumes along with a slight reduction in shopping frequency indicate the strong stockpile patterns that occurred during the pandemic.

The research is based on data from Ukraine, where the number of COVID-19 cases was low. Despite the comparatively low spread of COVID-19 in large cities in Ukraine in relation to other cities globally, people still revealed panic and stockpiling behaviors. The study's quantitative assessment of shopping behaviors reveals the social and economic determinants of the shopping frequency.

The purpose of this paper is to investigate the impact of the frequency of information acquisition on the frequency of stock trading. The authors also examined if the Big Five personality traits of investor influence the association between information acquisition and stock trading behavior.

The authors adopted NEO Five-Factor Inventory (Costa and McCrae, 1989) inventory to measure the Big Five personality traits of investors and examined the data collected from 541 individual investors of the Chinese stock market. To overcome the potential endogeneity bias, the authors followed two-stage least square method for estimating endogenous covariate by employing instrumental variable analysis. The authors performed probit regression to evaluate the moderating influence of investor personality traits on the association between information acquisition and stock trading behavior. The authors also performed several other tests to check the robustness of the key findings.

This research confirmed the previous findings that the more frequently investors acquire information, the more often they trade in stocks. Moreover, the authors added to the existing literature by providing empirical evidence that the Big Five personality traits moderate the relationship of information acquisition with stock trading behavior. Information acquisition tends to increase stock trading frequency in investors with conscientiousness, extraversion and agreeableness traits. On the other hand, it also has the tendency to decrease the intensity of stock trading in investors with openness and neuroticism traits.

The theoretical model in this study seeks to explain that the psychological factor, namely, investor personality, influences the way an investor interprets signals from information which in turn influences the investor decision to trade in securities. This research suggests that psychological characteristics of investors can be of relevance for policy makers in their attempts to improve their business in the financial services industry.

This study combines both information search literature and behavioral finance literature to investigate whether or not the information acquisition that relates to investors’ asset allocation decisions is influenced by investor personality. The study offers new theoretical insights into investors’ behavior due to the characteristics of the Chinese stock market which are uniquely different from other stock markets in the world. No previous study has been conducted so far in the Chinese stock market to explore variations in the impact of investors’ information acquisition on their stock trading by the Big Five personality and this paper strives to fill this research gap.

Despite the growing availability of scanner-panel data, surveys remain the most common and inexpensive method of gathering marketing metrics. The purpose of this paper is to explore the size, direction and correction of response errors in retrospective reports of category buying.

Self-reported purchase frequency data were validated using British household panel records and the negative binomial distribution (NBD) in six packaged goods categories. The log likelihood theory and the fit of the NBD model were used to test an approach to adjusting the errors post-data collection.

The authors found variations in systematic response errors according to buyer type. Specifically, lighter buyers tend to forward telescope their buying episodes. Heavier buyers tend either to over-use a rate-based estimation of once-a-month buying and over-report purchases at multiples of six or to use round numbers. These errors lead to overestimates of penetration and average purchase frequency. Adjusting the aggregate data for the NBD, however, improves the accuracy of these metrics.

In light of the importance of purchase data for decision making, the authors describe the inaccuracy problem in frequency reports and offer practical suggestions regarding the correction of survey data.

Two novel contributions are offered here: an investigation of errors in different buyer groups and use of the NBD in survey accuracy research.

The current research aims to explore variables that explain the differences in online reviewers' behavior.

The authors' panel dataset is compiled from Amazon.com's book section and uses publicly available information about reviewers in combination with the reviews they wrote. The authors utilize the Pareto/NBD model with time-invariant covariates. The model's parameters are estimated using maximum likelihood estimates (MLE) with MATLAB software.

This study contributes to the literature by exploring how the characteristics of reviews and the reviewers might shape consumer review frequency and continuity. Specifically, the authors' results show that review ratings, comments on a review, and helpful votes have a positive association with review frequency and continuity. Furthermore, the length of the textual review has a positive relationship with review frequency, but a negative relationship with review continuity. Relative to anonymous reviewers, people who write reviews and use their real names post reviews less often, but their review continuity is longer.

This paper is the first to identify empirically variables that explain review frequency and continuity, thus enabling companies to gain a better understanding of their reviewer base and hence to manage online word-of-mouth more efficiently. It is the first study to apply a well-known behavioral model to address online review behavior.

Effective management of shopping centres requires a good understanding of the consumers' behaviour and preferences as well as meeting the demand-side needs. The purpose of this paper is to elucidate the factors affecting the frequency of shopping centres' visits, providing implications for shopping centre managers from the perspective of a Czech consumer.

The study was carried out in one of the Czech shopping centres in Brno due to its potentially standardisable common characteristics derived from the observation in the scatter plot. The standardized questionnaire survey on consumer shopping behaviour was conducted on a sample of more than 1,000 customers. The linear probability model and the logit model were applied to estimate impacts of included sociodemographic variables on the outcome.

The findings on the frequency of visits give an overview of the sociodemographic and spatial determinants increasing the visit regularity. From the managerial point of view, the importance of customers' activities and purposes is highlighted.

The implications can be provided to managers challenging to demand management and considering the investment projects. We come up with certain suggestions in terms of market segmentation, based on sociodemographic characteristics and shopping behaviour.

The paper provides insights into the preferences of the Czech consumers and extends the previous analysis of shopping behaviour by developing the statistical properties and demonstrating the variables and their influence on the frequency of visit.

Self‐reported health (and the extent to which this was associated with partner abuse or psychosocial variables) was investigated in 132 women recruited from a domestic violence service. The survey instrument included abuse disability, life event and daily stress exposure, social support, anger expression style, and perceived health status. The prevalence estimates for this sample were significantly higher than standard estimates across a range of health problems. Regression models demonstrated that whilst the extent of partner abuse predicted the prevalence of three conditions, psychosocial factors were more substantial predictors of health and well‐being in domestic violence victims. Of these, life event frequency and anger expression were the most significant. These findings provide important information about the health of domestic violence victims as they seek support from domestic violence agencies, with relevance for practitioners working with victims who have terminated a violent relationship and for those supporting victims who remain with a violent partner.

The main purpose of this work is to evaluate the long-term effectiveness of a hotel’s chain loyalty program from a behavioral perspective.

A Dirichlet model was estimated to assess purchase frequency and hotel choice within one of the biggest hotel chains in Portugal. The sample comprises hotels where a loyalty program was implemented, with a total of 176,099 reservations. Data were extracted from the customer relationship management (CRM) systems of the hotel group.

The results suggest that instead of being loyal to a certain hotel, customers are loyal to the branded hotel chain. As the hotels are all part of the branded group, this polygamy is not only accepted but also very welcome.

The level of penetration and purchase frequency of CRM was measured. Nevertheless, a thorough understanding of these will be critical for the success of this program.

This research is a step toward assessing hotel chain competitiveness, by improving and suggesting segmented groups of brands/hotels and to induce cross-selling products accepting polygamous loyalty as the only way to sustain long-term relationships with customers.

This is one of the few research studies, if not the only one, to assess loyalty with tangible indicators, such as purchase frequency. Further, the results suggest that loyalty programs are more effective if multiple options are available and as such, cross-selling is perhaps the only way to fix customers.

Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved.

The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency.

A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled.

Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.

Social media has fundamentally changed the leisure travel behavior of Singapore residents. The purpose of this paper is to focus on Facebook and its impact on travel behavior. The study focuses on Singapore residents.

A total of 16 input and five output variables were tested, with a sample of 203 Facebook users residing in Singapore. Primary data modeling was done using ADANCO, a structural equation modeling tool that uses composite modeling approach for hypothesis testing. The analysis performed an estimated structural model and then determined the best model fit by measuring reliability, validity and path analysis and estimating model parameters.

Research findings indicate that Facebook has had a strong behavioral influence on the frequency of travel, itinerary planning and social sharing, while it had no impact during the destination-selection stage of travel planning.

Facebook’s influence on leisure travel behavior confirms the theory of planned behavior proposed by Icek Ajzen. From Facebook’s perspective, the outcome of this study is helpful in recommending the best use of the platform for destination providers.