Predictors Of Intention Research Articles

The approach of Norm Balance in predicting pharmacists' intention to collaborate with physicians to improve medication therapy.

Norm Balance is an approach under the Theory of Planned Behavior (TPB) where subjective norm is weighted by the relative importance of others and self-identity is weighted by the relative importance of self. The relative importance was measured previously by a trade-off measure. In this study, we developed separate measures for the relative importance. The study objectives were to: 1) assess the construct validity of the separate measures; 2) examine the approach of Norm Balance in predicting pharmacists' intention to collaborate with physicians to improve medication therapy; and 3) establish a modified TPB. We selected a random sample of 750 Iowa pharmacists and conducted two surveys. The first survey was to examine intention prediction, and the second survey was to examine behavior prediction by measuring behavior among respondents to the first survey. The relative importance was measured by both the trade-off measure and the separate measures. Exploratory factor analyses were performed for the relative importance of others (separate measures) and subjective norm, and for the relative importance of self (separate measures) and self-identity. Regressions for intention prediction were conducted for TPB with self-identity and the approach of Norm Balance. The same regressions were also conducted for three subgroups according to the median of the relative importance of others (trade-off measure). Moreover, another regression was conducted for behavior prediction. 239 practicing pharmacists responded to the first survey, and 188 responded to the second survey. The separate measures had cross factor loadings, whereas the trade-off measure had low correlations with other constructs. Both regressions for intention prediction explained 75% of the variance, with self-efficacy and attitude being strong predictors. Self-identity was not a predictor in the TPB with self-identify, but self-identity weighted by the relative importance of self was a significant predictor in the approach of Norm Balance. Regression coefficients of subjective norm and self-identify varied across subgroups. The regression for behavior prediction explained 30% of the variance, with intention and self-efficacy being two predictors. The trade-off measure was better than separate measures. The approach of Norm Balance appears to be a better model than the TPB with self-identity to examine pharmacist-physician collaboration.

Inequality threat increases laypeople's, but not judges', acceptance of algorithmic decision making in court.

Algorithmic decision making (ADM) takes on increasingly complex tasks in the criminal justice system. Whereas new developments in machine learning could help to improve the quality of judicial decisions, there are legal and ethical concerns that thwart the widespread use of algorithms. Against the backdrop of current efforts to promote the digitization of the German judicial system, this research investigates motivational factors (pragmatic motives, fairness concerns, and self-image-related considerations) that drive or impede the acceptance of ADM in court. We tested two hypotheses: (1) Perceived threat of inequality in legal judgments increases ADM acceptance, and (2) experts (judges) are more skeptical toward technological innovation than novices (general population). We conducted a preregistered experiment with 298 participants from the German general population and 267 judges at regional courts in Bavaria to study how inequality threat (vs. control) relates to ADM acceptance in court, usage intentions, and attitudes. In partial support of the first prediction, inequality threat increased ADM acceptance, effect size d = 0.24, 95% confidence interval (CI) [0.01, 0.47], and usage intentions (d = 0.23, 95% CI [0.00, 0.46]) of laypeople. Unexpectedly, however, this was not the case for experts. Moreover, ADM attitudes remained unaffected by the experimental manipulation in both groups. As predicted, judges held more negative attitudes toward ADM than the general population (d = -0.71, 95% CI [-0.88, -0.54]). Exploratory analysis suggested that generalized attitudes emerged as the strongest predictor of judges' intentions to use ADM in their own court proceedings. These findings elucidate the motivational forces that drive algorithm aversion and acceptance in a criminal justice context and inform the ongoing debate about perceptions of fairness in human-computer interaction. Implications for judicial praxis and the regulation of ADM in the German legal framework are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

От чего зависят удовлетворенность аспирантурой и намерения ее завершения у современных аспирантов?

<p>The article focuses on the role of motivational factors in the successful completion and satisfaction with postgraduate programs. PhD students (N=155, M=26,8, SD=4,2, 63% women) participated in the study. The theoretical framework is based on self-determination theory, which provides an understanding of the sources of autonomous and controlled motivation for research activity. A battery of questionnaires was used to assess autonomous and controlled motivation for postgraduate programs (the Universal Perceived Locus of Causality scales, UPLOC, and the Doctorate-related Need Satisfaction Scale, D-N2S), publication activity, intentions to complete the postgraduate program, and satisfaction with it. The results of the study demonstrated that autonomy need satisfaction was a predictor of autonomous motivation and satisfaction with the postgraduate program, whereas competence and relatedness needs satisfaction were predictors of intentions to complete the postgraduate program. Path models also showed that autonomy need satisfaction is a predictor of low levels of controlled motivation, which in turn is a negative predictor of intentions to complete the postgraduate program and satisfaction with it. It is concluded that it is important to study and monitor motivational factors, considering their predictive power in relation to PhD students' performance. Recommendations are offered to supervisors and faculty to support PhD students' basic psychological needs for autonomy, competence, and relatedness to foster their voluntary engagement and success in the research process.</p>

Predictors of Intention to use Health Literacy Strategies in Patient Education among Health Care Professionals at Public Hospitals of Bahir Dar City: Application of Theory of Planned Behavior.

Health literacy strategies were using plain language, using visual aids, using the teach-back method, limiting the number of items provided, and providing culture-sensitive care. This study aimed to assess predictors of intention to use health literacy strategies in patient education among healthcare professionals. An institutional-based cross-sectional study, theory of planned behavior. Bahir Dar, Northwest Ethiopia, from March 10 to April 10, 2023. 422 healthcare professionals. Six items with a response range from 1 = strongly disagree to 5 = strongly agree each item was summed on a five-point Likert scale and its score range (6-30). Linear regression, SPSS version 25. A total of 389 healthcare professionals participated with a response rate of 92.18%. The overall mean intention towards health literacy strategies score was 24.02 with (SD ± 3.84). Subjective norm [(β, .231; 95% CI: .129, .333)], attitude [β, .162; 95% CI: .072, .252], perceived behavioral control [(β, .121; 95% CI: .022, .221)], having got training [(β, .125; 95% CI: .348, .530)] and knowing health literacy [(β, .251; 95% CI: .131, .371)] were factors. The mean score of intention to use health literacy strategies was very low. Attitude, subjective norm, perceived behavioral control, training, and knowledge of health literacy strategies were predictors. Therefore, different strategies should be applied to increase the utilization of health literacy strategies.

Factors Influencing Nurses’ Intentions to Protect Patient Privacy: A Survey-Based Study Using the Theory of Planned Behavior

Background and Purpose:Patient privacy and confidentiality are fundamental ethical principles in healthcare. Protecting patient privacy, which is accepted as a patient’s right, is one of the responsibilities of nurses. Few studies on patient privacy among nurses have used social cognitive approaches. The purpose of this study is to examine nurses’ intentions to protect patient privacy using the theory of planned behavior (TPB).Methods:This is a cross-sectional and correlational design study. The study sample consisted of 202 nurses working in the emergency departments, operating rooms, inpatient wards, and intensive care units of the hospitals. Research data were collected using a face-to-face questionnaire that included TPB components on patient privacy. The proposed research model was tested using structural equation modeling.Results:Attitude (β= .238,p< .05), subjective norm (β= .295,p< .05), and moral norm (β= .337,p< .05) toward patient privacy are positive predictors of intention. The moral norm is the most effective component of intention. Perceived behavioral control is not a significant predictor of intention (β= .049,p> .05).Implications for Practice:Norms that create a sense of moral obligation in nurses are a significant determinant in increasing the intention to protect patient privacy. Interventions that improve moral norms, attitudes, and subjective norms will increase the intention to protect privacy. Nurse managers should provide nurses with adequate skills, resources, and an appropriate work environment to ensure perceived behavioral control regarding patient privacy.

Deep scene understanding with extended text description for human object interaction detection

Human–object interaction (HOI) detection plays a pivotal role in scene understanding, enabling the identification, localization, and behavioral intention prediction of humans and objects within a visual scene. Conventional approaches, such as graph-based networks, have demonstrated effectiveness in capturing spatio-temporal interaction cues. However, relying solely on visual information within these networks limits their ability to comprehensively grasp the intricate aspects of human interactions. To address this shortcoming, we propose a novel deep scene understanding graph network that harnesses the power of extended text descriptions to represent and interpret interactions between humans and objects effectively. Text serves as a rich source of information, directly conveying the nature of interactions within a visual scene. Our model seamlessly integrates text descriptions with visual features extracted from the entire video sequence, enabling it to capture the context and nuances of human–object interactions. This approach significantly enhances the model’s ability to accurately predict ambiguous human actions and anticipate future interactions. Extensive experiments on two benchmark datasets, CAD-120, and Something-Else, demonstrate that our proposed method achieves remarkable improvements in the F1-Score for both HOI detection and anticipation tasks. This work paves the way for more accurate and comprehensive HOI understanding in various real-world scenarios, highlighting the potential of text-augmented graph networks for effective interaction modeling.

Turnover Intention among Staff Who Support Older Adults Living Alone in Japan: A Cross-Sectional Study

Older adults in Japan traditionally rely on family members for care. However, the growing number of older people living alone has increased staff burden in community general support centers (CGSCs), which provide care for older people in Japan. This study aimed to identify factors linked to turnover intention among CGSC staff. Of 1002 staff invited to participate, 283 completed a survey (response rate: 28.2%). Logistic regression analysis of 183 valid questionnaires examined demographic, job-related, and attitudinal factors associated with turnover intention. The results showed that the perceived difficulty of supporting older adults living alone was the main predictor of turnover intention. Staff reporting high perceived difficulty levels were more likely to consider leaving. Negative attitudes toward supporting this population also increased turnover risk. These findings highlight a pressing need for targeted interventions to strengthen the ability of CGSC staff to manage the challenges of caring for isolated older individuals. Addressing staff perceptions and providing essential training and resources could reduce turnover rates and increase the quality of care for this vulnerable group.

Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement

In this study of burnout and engagement, we address three major themes. First, we offer a review of prior studies of burnout among IT professionals and link these studies to the Job Demands-Resources (JD-R) model. Informed by the JD-R model, we identify three factors that are organizational job resources, and posit that these (a) increase engagement, and (b) decrease burnout. Second, we extend the JD-R by considering software professionals’ intention to stay as a consequence of these two affective states, burnout and engagement. Third, we focus on the importance of factors for intention to stay, and actual retention behavior. We use a unique dataset of over 13,000 respondents at one global IT organization, enriched with employment status 90 days after the initial survey. Leveraging partial least squares structural equation modeling and machine learning, we find that the data mostly support our theoretical model, with some variation across different subgroups of respondents. An importance-performance map analysis suggests that managers may wish to focus on interventions regarding burnout as a predictor of intention to leave. The Machine Learning model suggests that engagement and opportunities to learn are the top two most important factors that explain whether software professionals leave an organization.

A Pilot Study of E-Commerce Consumers' Green Purchasing Intention in China: TPB Perspective

The frequent occurrence of environmental accidents and the heightened awareness of environmental protection among citizens have led to the emergence of green consumption as a social phenomenon. As a major global e-commerce market, Chinese consumers' green purchasing intention contributes significantly to environmental protection. The present study sought to develop pertinent scales utilizing the Theory of Planned Behaviour (TPB), with questionnaires employed to assess the influence of attitudes (A), subjective norms (SN), and perceived behavioral control (PBC) on Chinese e-commerce consumers' green purchasing intention (GPI). A total of 88 valid questionnaires were collected for the pilot study. The results indicated that the kurtosis's magnitude for all latent variables is less than 2, while the magnitude of the skewness is less than 1. This signifies that the data results have successfully passed the normality test. Furthermore, the Cronbach's alpha value and KMO for all variables exceeded 0.8, indicating high internal consistency. The total variance explained (TVE) surpassed 50%, demonstrating a substantial amount of variability accounted for. Additionally, the composite reliability (CR) was higher than the threshold. The value was higher than 0.8, and the average variance extracted (AVE) exceeded 0.5. This demonstrates that the questionnaire successfully underwent reliability and validity assessments. The findings of the pilot study have contributed to the advancement of research in the field of green consumption, offering insights into the prediction of green purchase intentions among Chinese e-commerce consumers.

How to enhance the entrepreneurial intentions of the young population in rural areas: An approach from personal values and the socioeconomic environment

This study investigates the determinants of entrepreneurial intentions among university students in the rural region of Comarca Sierra Sur, Andalusia, Spain. Using the Theory of Planned Behavior, the analysis reveals that attitudes toward entrepreneurship and perceived behavioral control are significant predictors of entrepreneurial intentions. Positive attitudes and a strong sense of control are closely associated with entrepreneurial intentions, while subjective norms show no significant effect, suggesting that social expectations may function differently in rural areas. Additionally, personal values such as openness to change and self-enhancement positively influence entrepreneurial intentions, whereas conservatism is negatively associated. This research fills a gap in the literature by extending the Theory of Planned Behavior to a rural context, where prior studies have been limited. The findings highlight the need to tailor entrepreneurship policies to the unique characteristics of rural areas and emphasize the importance of personal values and perceived control in fostering entrepreneurship. These insights are particularly relevant for policymakers aiming to stimulate economic development and entrepreneurship in rural regions.

Hierarchical Human Motion Intention Prediction for Increasing Efficacy of Human-Robot Collaboration

Hierarchical Human Motion Intention Prediction for Increasing Efficacy of Human-Robot Collaboration

Reservoir Computing for Drone Trajectory Intent Prediction: A Physics Informed Approach.

The design of accurate trajectory prediction algorithms is crucial to implement adequate countermeasures against drones with anomalous performances. Wrong predictions may cause high-false-positives that compromise safety in national infrastructures. In this article, a physics informed reservoir computing (PIRC) scheme for drone trajectory prediction is proposed. The approach is comprised of two main complementary learning algorithms that enhance the prediction and generalization capabilities: 1) a standard reservoir computing scheme for high-dimensional encoding exploitation and 2) a nonlinear control scheme that gives a physical feedback to the reservoir weights to ensure the prediction error is minimized. The nonlinear control scheme is modeled by the prediction error dynamics and a feedback linearization controller. Two different PIRC schemes are proposed which preserve the reservoir properties and enhance the prediction robustness. Lyapunov stability theory is used to verify the boundedness and convergence of the proposed algorithms. Simulation studies and comparisons are given to verify the proposed approach.

Pedestrian Crossing Intention Prediction Based on Cross-Modal Transformer and Uncertainty-Aware Multi-Task Learning for Autonomous Driving

Pedestrian Crossing Intention Prediction Based on Cross-Modal Transformer and Uncertainty-Aware Multi-Task Learning for Autonomous Driving

Exploring Determinants of Nurse Retention: The Influence of Personal and Professional Values, and Work Environment in Predicting Intent to Leave.

Nurse retention remains a pivotal issue, necessitating urgent strategies aimed at the recruitment and retention of highly qualified nurses. However, there is limited knowledge regarding the influence of nurses' personal and professional values, along with work environment, on predicting intent to leave an organization. Utilizing a secondary analysis approach, this study employed logistic regression to predict intent to leave among registered nurses (n=671) using results from the Short Schwartz's Value Survey, the Nurses Professional Values Scale-3, and the Practice Environment Scale of the Nursing Work Index. Regression analysis indicates that the work environment including "Participation in Hospital Affairs" (B=-.665, odds ratio=.514), "Nurse Manager Ability, Leadership, and Support" (B=-.448, odds ratio=.639), and "Staffing and Resource Adequacy" (B=-.589, odds ratio=.555) are significant predictors of nurses' intentions to leave. Establishing and maintaining supportive work environments is essential for retention, highlighting the importance of effective leadership.

Developing and testing a theoretical model of food donation behavior

The global challenges of food insecurity, coupled with the large scale of food waste generated by the restaurant industry, necessitate collaborative efforts from both individuals and businesses. Drawing upon the behavioral reasoning theory, this study develops a model investigating the influence of both “reasons for” (“altruism”, “social norms”, “egoism”, and “tax incentives”) and “reasons against” (“financial burdens”, “inconvenience”, “insufficient information”, and “liability concerns”) on the food donation behavior of restaurant managers. The model is tested on a sample of 300 restaurant managers in South Korea using structural equation modeling. The findings suggest that “altruism” and “social norms” exhibit a positive influence on both attitudes and intentions toward food donation, while “inconvenience” has a negative impact on both. Moreover, “tax incentives” and “insufficient information” emerge as significant predictors of intentions to donate food. In addition, “trust in food banks” significantly mitigates the negative impact of “reasons against” on food donation intentions, although its effect on attitudes is not significant. These findings provide novel implications to advance the existing knowledge and develop more effective strategies for promoting restaurant food donation practices.

Predictors of child restraint usage among Chinese parents

Objective: To investigate psychological predictors of the use of child restraints among Chinese parents following passage of national law requiring their use. Methods: Grounded in the Prototype Willingness Model (PWM) and guidance from previous research, we developed and administered a questionnaire to assess child restraint usage and understand psychological factors that predict usage. 413 parents with child(ren) aged 0–8 years completed the questionnaire through the WenJuanXing online survey platform. Results: Among the 413 parents, 321 (77.7%) used child restraints regularly when taking their children on car trips, with the highest use reported among parents with children under 1 year (83.8%). Among the psychological predictors, willingness to use a restraint was the strongest predictor of intention and self-reported behavior to use restraints, but the predictive effect of intention on behavior was not significant. Descriptive norms – a belief that trusted friends and family use restraints – was the strongest predictor of behavior to use child restraints, and prototype favorability – the belief that prototypical parents of similar age and gender use restraints – also showed significant effects in predicting behavior. Conclusions: Following the passage of national laws requiring child restraints in China, it is becoming more common for parents to use restraints when their children travel by car. Parents’ use of child restraints for children follows a socially reactive (through willingness) decision-making path rather than a reasoned (through intention) decision-making pattern. The influence of the behavior of others, both trusted friends and family (descriptive norms) and prototypical similar parents (prototype favorability) are strong predictive factors for use of child restraints, suggesting prevention programs might leverage behavior of others to impact parents and encourage use of child restraints.

Lessons from a longitudinal community-based investigation of adherence to guidelines and intent to vaccinate during the COVID-19 pandemic

BackgroundThe COVID-19 pandemic is a major public health challenge. The US Centers for Disease Control published guidelines early in the pandemic emphasizing practicing good hygiene and staying at home, which were later modified. PurposeUsing a community sample of 2152 participants in the state of Florida who responded to a series of online surveys, we tested a prediction model of adherence to guidelines and intent to vaccinate during the COVID-19 pandemic. MethodsParticipants were assessed in May 2020, June 2020, and January 2021. Predictors included sociodemographic and psychological variables. ResultsA slight decrease in adherence was reported over time. In multivariate models, older age, female sex, having health insurance, greater knowledge about COVID-19, more worry, less loneliness, and greater confidence and trust in COVID-19 information were all significantly and consistently associated with greater adherence to guidelines. Significant predictors of intent to vaccinate were male sex, greater knowledge, higher socioeconomic status, identifying as White, and greater guideline adherence (p's < 0.05). ConclusionsOur findings highlight a number of significant predictors, including knowledge, loneliness, and confidence/trust. Critically these variables are modifiable and could therefore serve as targets in public health interventions to improve adherence to pandemic guidelines in the general population, as well as certain demographic characteristics that may influence intent to vaccinate. COVID-19 knowledge appears to play a central role in both adherence to guidelines and intent to vaccinate suggesting that having accurate information is critical for appropriate behavior.

Physicochemical Characterization of Carao Honey Flour (Cassia grandis) and Its Effects on the Sensory Attributes in a Cookie

Carao honey is a potential functional ingredient that can generate added value to food products enriched by reducing waste. In many countries, carao has been utilized for therapeutic purposes since this type of plant extract can inhibit the growth against the most common dermatophytes, a pathogenic fungus that grows on the skin, mucous membranes, hair, nails, feathers, and other body surfaces, causing ringworm and related diseases. The physicochemical characteristics of the cookies were also investigated, which included aW, proximate analysis, hardness, and color. A sensory study was carried out to determine the rejection threshold, acceptability, purchase intent, and general taste of 90 consumers. The gluten-free cookies were prepared with carao honey as a partial substitute for rice flour in different percentages (0%, 2.5%, 5%, and 10%). The carao honey proximate composition, rheological properties, and pasting properties were analyzed. It was emphasized that incorporating carao honey into cookies improves the ingredient’s sustainability. The impact of carao on the physicochemical and sensory properties of cookies was evident in color, flavor, and smell. According to the results, only samples with 2.5% carao were accepted regarding flavor and smell. Overall, liking scores, age, and flavor were the most significant predictors of purchase intent. The information given to panelists did not significantly (p &lt; 0.05) influence liking scores only for cookies. According to physicochemical analysis, carao honey flour was high in carbohydrates (88%). The incorporation of carao produced cookies with darker colors and a softer texture. The study demonstrated that carao flour could be included to produce sensorial accepted cookies at a 2.5% addition level as an alternative in the food industry that benefits from environmental sustainability and, at the nutritional level, improves the availability of nutrients, mainly sugars and proteins.

Determining behavioral intention and its predictors toward preconception care use among reproductive age women in Arba Minch town, Southern Ethiopia, 2022: a cross-sectional study based on the theory of planned behavior

BackgroundPreconception care is a highly effective, evidence-based intervention aimed at promoting the health of reproductive-age women and reducing adverse pregnancy-related outcomes. The Ethiopian Ministry of Health plans to integrate preconception care services into the country’s existing healthcare system. However, women’s preferences may be influenced by their values and customs. Therefore, this study used the theory of planned behavior to assess women’s intention toward preconception care use and its predictors among reproductive-age women in Arba Minch town, southern Ethiopia.MethodsA community-based cross-sectional study was conducted in Arba Minch town from May 1 to 30, 2022. A simple random sampling technique was employed to select 415 study participants for data collection. Data were collected through a face-to-face interview using a pretested, structured questionnaire. Epi Data version 4.6 and SPSS version 26 were used for the entry and analysis of data, respectively. Multiple linear regression was performed to identify independent predictors of intention to use preconception care. The standardized β-coefficient was used as a measure of association. A P value of less than 0.05 was used to declare statistical significance.ResultsThis study had 415 participants, giving a response rate of 98.3%. The mean age of the participants was 28.4 (SD 5.18). The mean intention to use preconception care was 21.43 (SD 2.47). Direct perceived behavioral control (β = 0.263, p < 0.001), direct attitude (β = 0.201, p = 0.001), direct subjective norm (β = 0.158, p = 0.006), and age (β=-0.115, p = 0.023) were significant predictors of women’s behavioral intention to use preconception care.ConclusionThe study identified perceived behavioral control as the strongest predictor, followed by attitude and subjective norms, influencing the intention to use preconception care. These findings underscore the importance of integrating these predictors into health intervention programs aimed at promoting the implementation of preconception care services.

The Role of Habit and Perceived Usefulness on the Continuance Intention of Subscription Video on Demand Services in Indonesia

In the midst of the rising popularity of Subscription Video On Demand (SVOD), the industry faces significant challenges. The high churn rate and the abundance of competitors in the SVOD industry necessitate that service providers develop strategies to maintain consumer interest. Consumers who significantly contribute to the high churn rate are Generation Y and Generation Z, also known as digital natives. This study aims to analyze how factors within the Expectancy Confirmation Model can influence the Continuance Intention of SVOD users. This descriptive study employs a cross- sectional design. The respondents of this study consist of 159 active SVOD users. The findings reveal that confirmation and individual mobility significantly influence perceived usefulness. Additionally, confirmation, perceived usefulness, and perceived enjoyment significantly affect satisfaction. Furthermore, satisfaction, perceived usefulness, and habit are significant predictors of continuance intention. However, the influence of individual mobility and perceived enjoyment on continuance intention is not significant. Practically, this study provides insights for SVOD service providers to develop marketing strategies that enhance consumer habit and satisfaction. Academically, this study combines two models from previous researchs making the model and variables used more comprehensive in explaining the predictors of continuance intention.