Research on the Impact of Digital Literacy on Farmer Households' Green Cooking Energy Consumption: Evidence from Rural China

In the era of the digital economy, farmers' digital literacy has a profound impact on household green cooking energy consumption. Based on data from the China Family Panel Studies (CFPS) in 2018, this paper constructs a digital literacy index using an entropy method and employs the Probit model regression and mediation effect model test to study the impact effect and theoretical mechanism of individual digital literacy on household green cooking energy consumption. The research results show that the improvement of digital literacy can effectively promote the consumption of green cooking energy in households. After using the IV-Probit model and CMP model to solve the endogeneity, this conclusion remains valid; The heterogeneity analysis shows that the impact of digital literacy on green cooking energy consumption of households in different regions and different income stages is different. This performance is specific to the eastern and western regions and low-income households. The improvement in digital literacy can significantly promote green cooking energy consumption in rural households; however, in the central region and high-income households the improvements were insignificant. Mechanism analysis shows that digital literacy has a significant positive impact on household green cooking energy consumption through non-agricultural employment and information acquisition. Based on this, it is suggested that the construction of a digital countryside should not only consider the construction of digital infrastructure, but also reasonably guide the cultivation of the internal digital literacy of the construction subject. Moreover, the cultivation of digital literacy should not only focus on regional differences, but also focus on key subjects and implement precise cultivation. We should give full play to the synergistic effect of digital literacy, and pay attention to the non-agricultural employment of farmers and information elements.

Pandemics and technology engagement: New evidence from m-Health intervention during COVID-19 in India

Information provision for social welfare via cheap technological media is now a widely available tool used by policymakers. Often, however, an ample supply of information does not translate into high consumption of information due to various frictions in demand, possibly stemming from the pecuniary and non-pecuniary cost of engagement, along with institutional factors. We test this hypothesis in the Indian context using a unique data set comprising 2 million call records of enrolled users of ARMMAN, a Mumbai-based nongovernmental organization that sends timely informational calls to mobile phones of less-privileged pregnant women. The strict lockdown induced by COVID-19 in India was an unexpected shock on engagement with m-Health technology, in terms of both reductions in market wages and increased time availability at home. Using a difference-in-differences design on unique calls tracked at the user-time level with fine-grained time-stamps on calls, we find that during the lockdown period, the call durations increased by 1.53 percentage points. However, technology engagement behavior exhibited demographic heterogeneity increasing relatively after the lockdown for women who had to borrow the phones vis-à-vis phone owners, for those enrolled in direct outreach programs vis-à-vis self-registered women, and for those who belonged to the low-income group vis-à-vis high-income group. These findings are robust with coarsened exact matching and with a placebo test for a 2017-2018 sample. Our results have policy implications around demand-side frictions for technology engagement in developing economies and maternal health.

How Does the Change of Information Source Affect Residents' Risk Attitudes?

Using data from the China Family Panel Studies (CFPS), this paper investigates the effects of Internet use on residents' risk attitudes. Both Generalized Ordered Logit Model and Logit model are used to identify the effects of Internet use. The results reveal an association between Internet use and increases in both subjective and objective risk preferences that remains even after we adjust for possible endogeneity. The heterogeneity analysis also reveals that these impacts are different among groups with different reasons for Internet use and different personal characteristics. Our study expands the research on the effects of Internet on people's concepts from the micro perspective and suggests that while promoting the application of information technology we should also pay attention to the individual characteristics of residents so that we can better share the "digital dividend" brought by the popularization of information technology in the whole society.

Toward a Theory of Visual Information Acquisition in Driving

OBJECTIVE

The aim of this study is to describe information acquisition theory, explaining how drivers acquire and represent the information they need.

BACKGROUND

While questions of what drivers are aware of underlie many questions in driver behavior, existing theories do not directly address how drivers in particular and observers in general acquire visual information. Understanding the mechanisms of information acquisition is necessary to build predictive models of drivers' representation of the world and can be applied beyond driving to a wide variety of visual tasks.

METHOD

We describe our theory of information acquisition, looking to questions in driver behavior and results from vision science research that speak to its constituent elements. We focus on the intersection of peripheral vision, visual attention, and eye movement planning and identify how an understanding of these visual mechanisms and processes in the context of information acquisition can inform more complete models of driver knowledge and state.

RESULTS

We set forth our theory of information acquisition, describing the gap in understanding that it fills and how existing questions in this space can be better understood using it.

CONCLUSION

Information acquisition theory provides a new and powerful way to study, model, and predict what drivers know about the world, reflecting our current understanding of visual mechanisms and enabling new theories, models, and applications.

APPLICATION

Using information acquisition theory to understand how drivers acquire, lose, and update their representation of the environment will aid development of driver assistance systems, semiautonomous vehicles, and road safety overall.

Promoting COVID-19 Vaccination Using the Health Belief Model: Does Information Acquisition from Divergent Sources Make a Difference?

As a promising approach to stop the escalation of the pandemic, COVID-19 vaccine promotion is becoming a challenging task for authorities worldwide. The purpose of this study was to identify the effective sources for disseminating information on the COVID-19 vaccine to promote individuals' behavioral intention to take the vaccine. Based on the Health Belief Model (HBM), this study illustrated the mechanism of how COVID-19 information acquisition from different sources was transformed into vaccination intentions via health beliefs. Using an online survey in China, the structural equation model results revealed that perceived benefits and cues to action were positively associated with COVID-19 vaccination intentions, and perceived barriers were negatively related to the intentions. However, perceived susceptibility and perceived severity had no significant relationships with the intentions. Moreover, the findings unveiled differences in the effects of acquiring information via multiple sources among traditional media, new media, and interpersonal interactions. Notably, new media and interpersonal interactions were more salient in promoting vaccination intention via health beliefs, compared with traditional media. The findings from this study will benefit health officials in terms of utilizing different information sources in vaccine programs.

The importance of personally relevant knowledge for pandemic risk prevention behavior: A multimethod analysis and two-country validation

Pandemics threaten world stability; however, spread is mitigated with prevention behaviors. We introduce "personally relevant knowledge" to explain the knowledge-behavior gap (i.e., objective and subjective knowledge on information acquisition and behavioral change). Hypotheses are derived from prior knowledge literature, economic psychology, and relevance theory. Multimethod analysis (survey data, partial least squares structural equation path modeling [PLS-SEM], and an asymmetric information theoretic statistical analysis) is applied to H1N1 data from the USA and Australia. Personally relevant knowledge is an important addition to prior knowledge conceptualizations, and information theory uncovers asymmetric variable relationships concerning the knowledge-behavior gap, not captured by PLS-SEM.

An Information Entropy-Based Modeling Method for the Measurement System

Measurement is a key method to obtain information from the real world and is widely used in human life. A unified model of measurement systems is critical to the design and optimization of measurement systems. However, the existing models of measurement systems are too abstract. To a certain extent, this makes it difficult to have a clear overall understanding of measurement systems and how to implement information acquisition. Meanwhile, this also leads to limitations in the application of these models. Information entropy is a measure of information or uncertainty of a random variable and has strong representation ability. In this paper, an information entropy-based modeling method for measurement system is proposed. First, a modeling idea based on the viewpoint of information and uncertainty is described. Second, an entropy balance equation based on the chain rule for entropy is proposed for system modeling. Then, the entropy balance equation is used to establish the information entropy-based model of the measurement system. Finally, three cases of typical measurement units or processes are analyzed using the proposed method. Compared with the existing modeling approaches, the proposed method considers the modeling problem from the perspective of information and uncertainty. It focuses on the information loss of the measurand in the transmission process and the characterization of the specific role of the measurement unit. The proposed model can intuitively describe the processing and changes of information in the measurement system. It does not conflict with the existing models of the measurement system, but can complement the existing models of measurement systems, thus further enriching the existing measurement theory.

Human Foragers: Searchers by Nature and Experience

Diverse studies of human foraging have revealed behavioral strategies that may have evolved as adaptations for foraging. Here, we used an outdoor experimental search task to explore the effect of three sources of information on participants' performance: (i) information obtained directly from performing a search, (ii) information obtained prior to testing in the form of a distilled snippet of knowledge intended to experimentally simulate information acquired culturally about the environment, and (iii) information obtained from experience of foraging for natural resources for economic gain. We found that (i) immediate searching experience improved performance from the beginning to the end of the short, 2-min task, (ii) information priming improved performance notably from the very beginning of the task, and (iii) natural resource foraging experience improved performance to a lesser extent. Our results highlight the role of culturally transmitted information as well as the presence of mechanisms to rapidly integrate and implement new information into searching choices, which ultimately influence performance in a foraging task.

Knowledgeable Lemurs Become More Central in Social Networks

Strong relationships exist between social connections and information transmission [1-9], where individuals' network position plays a key role in whether or not they acquire novel information [2, 3, 5, 6]. The relationships between social connections and information acquisition may be bidirectional if learning novel information, in addition to being influenced by it, influences network position. Individuals who acquire information quickly and use it frequently may receive more affiliative behaviors [10, 11] and may thus have a central network position. However, the potential influence of learning on network centrality has not been theoretically or empirically addressed. To bridge this epistemic gap, we investigated whether ring-tailed lemurs' (Lemur catta) centrality in affiliation networks changed after they learned how to solve a novel foraging task. Lemurs who had frequently initiated interactions and approached conspecifics before the learning experiment were more likely to observe and learn the task solution. Comparing social networks before and after the learning experiment revealed that the frequently observed lemurs received more affiliative behaviors than they did before-they became more central after the experiment. This change persisted even after the task was removed and was not caused by the observed lemurs initiating more affiliative behaviors. Consequently, quantifying received and initiated interactions separately provides unique insights into the relationships between learning and centrality. While the factors that influence network position are not fully understood, our results suggest that individual differences in learning and becoming successful can play a major role in social centrality, especially when learning from others is advantageous.

A Self-Assessment Stereo Capture Model Applicable to the Internet of Things

The realization of the Internet of Things greatly depends on the information communication among physical terminal devices and informationalized platforms, such as smart sensors, embedded systems and intelligent networks. Playing an important role in information acquisition, sensors for stereo capture have gained extensive attention in various fields. In this paper, we concentrate on promoting such sensors in an intelligent system with self-assessment capability to deal with the distortion and impairment in long-distance shooting applications. The core design is the establishment of the objective evaluation criteria that can reliably predict shooting quality with different camera configurations. Two types of stereo capture systems—toed-in camera configuration and parallel camera configuration—are taken into consideration respectively. The experimental results show that the proposed evaluation criteria can effectively predict the visual perception of stereo capture quality for long-distance shooting.

Temporal dynamics of hypothesis generation: the influences of data serial order, data consistency, and elicitation timing

The pre-decisional process of hypothesis generation is a ubiquitous cognitive faculty that we continually employ in an effort to understand our environment and thereby support appropriate judgments and decisions. Although we are beginning to understand the fundamental processes underlying hypothesis generation, little is known about how various temporal dynamics, inherent in real world generation tasks, influence the retrieval of hypotheses from long-term memory. This paper presents two experiments investigating three data acquisition dynamics in a simulated medical diagnosis task. The results indicate that the mere serial order of data, data consistency (with previously generated hypotheses), and mode of responding influence the hypothesis generation process. An extension of the HyGene computational model endowed with dynamic data acquisition processes is forwarded and explored to provide an account of the present data.