Examining the influential factors of consumer purchase intentions for blockchain traceable coffee using the theory of planned behaviour

PurposeGiven the increasing industry interest in blockchain technologies for supply chain management and product traceability, this paper aims to investigate consumer purchasing intentions for blockchain traceable coffee and their psychosocial antecedents, utilising an extended model of the theory of planned behaviour (TPB).Design/methodology/approachAn online questionnaire study of 123 participants was deployed, using two traceability systems (one based on blockchain and one on a more established traceability certification) for organic coffee.FindingsAdding variables such as environmental protections, trust and habits significantly increased the predictive power of TPB. The results suggest that attitude, perceived behavioural control and environmental protections drive intentions to purchase blockchain traceable coffee.Research limitations/implicationsApart from establishing the factors affecting consumer intentions for blockchain traceable coffee, this study validates the TPB as a model of explaining coffee purchasing intentions and provides evidence of new variables that can significantly increase the model's predictive power.Practical implicationsThe proposed format of presenting traceability information along with the significant variables revealed in our study can function as a guide for designing product features and marketing strategies for blockchain traceable organic coffee. Increasing consumer awareness on product traceability will also play a crucial role in the success of these products.Originality/valueThis study is the first to explore consumer purchasing intentions for blockchain traceable coffee and establish the psychosocial variables behind them contributing, in that way, to an understudied area in academic literature as well as providing insights for a more consumer-centric design of such products.

Updated IQ and Well-being Scores for the 50 U.S. States

At the level of the 50 U.S. states, an interconnected nexus of well-being variables exists. These variables have been shown to strongly correlate with estimates of state IQ in interesting ways. But the state IQ estimates (McDaniel 2006) are now more than 16 years old, and the state well-being estimates (Pesta et al., 2010) are over 12 years old. Updated state IQ and well-being estimates are therefore needed. I thus first created new state IQ estimates by analyzing scores from both the Program for the International Assessment of Adult Competency (for adults), and the National Assessment of Educational Progress (for fourth and eighth grade children) exams. I also created new global well-being scores by analyzing state variables from the following four well-being subdomains: crime, income, health, and education. When validating the nexus, several interesting correlations existed among the variables. For example, state IQ most strongly predicted FICO credit scores, alcohol consumption (directly), income inequality, and state temperature. Interestingly, state IQ derived here also correlated .58 with state IQ estimates from over 100 years ago. Global well-being likewise correlated with many old and new variables in the nexus, including a correlation of .80 with IQ. In sum, at the level of the U.S. state, a nexus of important, strongly correlated variables exists. These variables comprise well-being, and state IQ is a central node in this network.

Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.

Adoption of Wearable Technology Devices

With rapid change in technology worldwide, innovative products such as wearable technology devices tend to have an uprising trend. Consumers, however, are not necessarily adaptive in their nature and their perception is shaped by many factors. The aim of this research is to investigate the consumer acceptance of wearable technology devices, specifically smartwatches. The study extends the widely used technology acceptance model with the introduction of new variables. For the purpose of the study, survey data was collected from German and Turkish university students. The overall results provide validation to previous literature while introducing new factors for consumer acceptance of technology products, wearable technology devices, and smartwatches. Importance of this research comes from the innovative and promising nature of the wearable technology devices concept, the lack of work on smartwatches in literature, as well as the cross-cultural nature of the study. The study also has managerial implications for technology companies who chase after growth in their businesses.

Personal and Social Consequences of Psychotropic Substance Use: A Population-Based Internet Survey

Background and objectives: Drug abuse has become a major worldwide health concern among all age groups. The present study analyses substance misuse and its social and personal consequences using a population-based internet survey in Spain. Materials and Methods: Screening for drug abuse (of alcohol, marijuana/hashish and psychostimulants) and its related risks and problems was performed using the Car, Relax, Alone, Forget, Friends, Trouble (CRAFFT) score. Socio-demographic factors, depressive, anxiety and stress symptoms as well as health habits were also evaluated. We used Linear regression methods to compare each variable’s individual contribution so as to determine which one best explains the results. Results: In this population-based study, 1224 people completed and returned the online survey. Of all participants, 57% reported consuming at least one substance based on the CRAFFT scale. While increasing age reduces the probability of personal and social consequences of consumption, people who smoke receive up to three times more (OR = 3.370) recommendations from family and friends to reduce their consumption. As for the type of substance, the consumption of marijuana increases the risk of forgetting (OR = 2.33) and the consumption of other psychostimulant substances almost triples the risk of consuming alone (OR = 2.965). Combining substances can increase the rate of driving a vehicle after consumption by 3.4 times. Conclusions: Although age, smoking and the type of substances used increase the risk of suffering from social and personal consequences of the use or abuse of substances, future studies are needed to determine the influence of new variables as a potential tool for treating and minimizing the adverse consequences of drug abuse.

To Remember or to Forget: Political Repression in the Collective Memory of Descendants

The question of political repression divides society and polarizes public discourse. Understanding political repression through the prism of socio-psychological knowledge is a zone of proximal development for researchers, because the suicidal nature of repression, which A.M. Etkind points out, makes it difficult to understand terror, hinders the work of mechanisms that operate in a society that has come into contact with a catastrophe of such magnitude and duration. The study is devoted to the study of social ideas about repression, as well as emotional reactions towards repression and the repressed among the descendants of the repressed, i.e., their children and grandchildren. A total of 110 people (61.82% - females) aged 44 to 78 years, 93.63% with higher education, participated in the study. The sample included three groups: the generation of children (21 people, M = 59.52 years; SD = 9.04); the generation of grandchildren (63 people, M = 54.71 years; SD = 7.66); and the control group (26 people, M = 53.65 years; SD = 7.72). A survey in the form of a questionnaire was used, followed by a prototypical analysis of associations, which made it possible to identify the structure of ideas about repression in the three groups. To analyze emotional reactions, a factor analysis of scores on 38 scales was carried out, followed by an analysis on the new variables. The characteristics of the structure of social representations (the core and periphery zones) are consistent with the initial hypothesis that the supposedly traumatic event of repression is perceived as a personal one by the descendants but as a social one by the respondents of the control group. There were no differences in the severity of emotional reactions in relation to the category of repressions and repressed between the generations. The combined group of descendants significantly differs from the control group in the greater severity of indicators when assessing the category of repression by the factors of Anxiety, Depression and Grief, and when assessing the category of repressed by the factor of Grief. The results of the study of social ideas about the past allow us to talk about the collective memory of repression in two generations of the descendants of the repressed: in the structure of the inner world of generations of descendants, repression is a personal event of family history, colored by sorrowful feelings of varying degrees of intensity and depth.

Consumer Conformity, Social Ties and EWOM in Digital Marketing

Digital marketing is a marketing technique that is widely discussed in the latest literature. The complexity of digital marketing creates new variables in an effort to increase purchase intention, such as consumer conformity, social ties and EWOM (Electronic Word of Mouth). This study aims to analyze the relationship between consumer conformity, social ties, and EWOM in digital marketing on the Shopee, Lazada, and Bukalapak platforms. Through quantitative methods using Structural Equation Modeling (SEM) analysis using AMOS software on 200 respondents consisting of users of the Shopee, Lazada, and Bukalapak platforms. This study analyzes 5 hypotheses, which is then statistically proven to have a significance value of < 0.05, which means that there is a significant relationship between the variables, thus all hypotheses are supported. The results indicate that social ties and EWOM can have a significant effect on consumer conformity and are also able to directly affect purchase intention. This research also highlights that consumer conformity has a significant effect on purchase intention in digital marketing.

DEVELOPING DATA SENSE: MAKING INFERENCES FROM VARIABILITY IN TSUNAMIS AT PRIMARY SCHOOL

In order to study the manifestation of data sense and identify ways of thinking about variability in authentically realistic problems in a group of Chilean fifth-grade students, a lesson plan was designed and implemented, within the framework of statistical literacy and using the “lesson study” modality, in which students were urged to make inferences based on the analysis of data corresponding to the tsunami that struck the Chilean coast in 2010. This article focuses on the qualitative study of the data representations produced by two groups of students during the implementation of the lesson plan. The analysis of the behavior of the tsunami carried out by the students led them to work simultaneously with nominal qualitative, ordinal qualitative, discrete quantitative, and continuous quantitative variables; to create new variables; to construct representations of data (multiple bar graphs and frequency tables); and to make inferences based on the data. We conclude that the use of an authentic context and the construction of their own representations promoted data sense in students and facilitated the development of their statistical thinking, through which they were able to recognize, describe, and explain the variability of the phenomenon. Abstract: Spanish Con el propósito de estudiar la manifestación del sentido del dato e identificar las formas de razonar la variabilidad en problemas auténticamente realistas en un grupo de estudiantes chilenos de quinto grado de primaria, se diseñó e implementó un plan de clases, en el marco de alfabetización estadística y bajo la modalidad “lesson study”, en el que se instó a los estudiantes a hacer inferencias a partir del análisis de los datos correspondientes al tsunami que azotó la costa de Chile en 2010. Este artículo se centra en el estudio cualitativo de las representaciones de datos producidas por dos grupos de estudiantes durante la implementación del plan de clases. El análisis del comportamiento de los datos del tsunami realizado por los estudiantes los llevó a trabajar simultáneamente con variables cualitativas nominales, cualitativas ordinales, cuantitativas discretas y cuantitativas continuas; crear nuevas variables; elaborar representaciones de datos (gráfico de barras múltiples y tabla de frecuencias); y hacer inferencias basadas en los datos. Se concluye que el uso de un contexto auténtico y la construcción de representaciones propias, promovieron en los estudiantes el sentido del dato y facilitaron el desarrollo de su pensamiento estadístico, pudiendo reconocer, describir y explicar la variabilidad del fenómeno.

On the Quadratization of the Integrals for the Many-Body Problem

A new approach to the construction of difference schemes of any order for the many-body problem that preserves all its algebraic integrals is proposed herein. We introduced additional variables, namely distances and reciprocal distances between bodies, and wrote down a system of differential equations with respect to the coordinates, velocities, and the additional variables. In this case, the system lost its Hamiltonian form, but all the classical integrals of motion of the many-body problem under consideration, as well as new integrals describing the relationship between the coordinates of the bodies and the additional variables are described by linear or quadratic polynomials in these new variables. Therefore, any symplectic Runge–Kutta scheme preserves these integrals exactly. The evidence for the proposed approach is given. To illustrate the theory, the results of numerical experiments for the three-body problem on a plane are presented with the choice of initial data corresponding to the motion of the bodies along a figure of eight (choreographic test).

Evaluation of Childbirth Self-Efficacy in Pregnant Women with Variables: A Systematic Review and Meta-Analysis

Objective : The present study aims to examine childbirth self-efficacy levels with potential moderating variables. Methods: The systematic searches were conducted in nine databases in July 2019. The PRISMA checklist was used. The quality of studies was evaluated by two researchers. The random-effect model was used in the present meta-analysis. The heterogeneity tests and moderator analyses were performed. There were 18 eligible articles. Results: Results indicated that childbirth self-efficacy levels do not change based on parity (Q=0.784, p=0.376 for efficacy expectancy, Q=0.190, p=0.663 for outcome expectancy). The between-study variance was not significant for subdimensions of CBSEI (Qb = 1.531, p = .216), which means no significant difference between OE and EE levels was found. The between-study variance was not significant for OE levels (Qb = 0.333, p = .847), which means no significant difference was found between Outcome-AL, Outcome-SS, and OE-16. The moderator analysis, including Efficacy-AL, Efficacy-SS, and EE-16 presented a higher pooled mean score for EE-16 (111.56; 95% CI = 98.66 to 124.46). However, the between-study variance was not significant for EE levels (Qb = 4.240, p = .120). Despite the moderator analysis, the finding of high heterogeneity suggests the need for further studies which examine the concept of childbirth self-efficacy with additional variables. Conclusion: The study presents that childbirth self-efficacy levels do not change based on parity, stages of labor and subdimensions of CBSEI. Researchers need to examine the concept of childbirth self-efficacy with new variables for further clarify of concept.