Unpeel the layers of trust! A comparative analysis of crowdfunding platforms and what they do to generate trust

Crowdfunding can prove to be a crucial alternative financing method, especially for microentrepreneurs, new ventures, and nonprofit associations. But trust needs to be built before prospective users decide to use crowdfunding, and we still lack an in-depth understanding of how crowdfunding platforms make users trust them. We analyzed 50 crowdfunding platforms of different types, from investment and lending platforms to reward and donation platforms. Within the sample, we included successful and consolidated platforms, such as Kickstarter, Crowdcube, and Indiegogo, as well as lesser-known and less active platforms, such as Crowdence, Zank, and Fundeen. A comparative analysis of these platforms reveals a diversity of organizational practices that aim to generate trust on four levels: trust toward the platform, toward the project, toward the users, and toward the notion of crowdfunding overall. We suggest guidelines for best practices for crowdfunding platforms and a checklist of points for prospective users searching for the right crowdfunding platform.     

An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews

The amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. Marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. We focus on the fundamental relationship between a consumer’s overall empirical evaluation, and the text-based explanation of their evaluation. We study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. We incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. For generalizability, we analyze 25,241 products in nine product categories, and 260,489 reviews across five review platforms. We find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as Latent Dirichlet Allocation offer deeper diagnostics. However, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. Consequently, future selection of methods to process text reviews is likely to be based on analysts’ goals of prediction versus diagnostics.     

Visual attention to the main image of a hotel website based on its position,type of navigation and belonging to Millennial generation: An eye tracking study

This article focuses on the role played by the images that hotels use on their websites. Under this approach, this study analyses the main image used in a hotel website, considered an area of outstanding interest, which can help to improve the first impression when arriving at the website and, with it, increase the number of bookings. The absence of studies on this matter aroused the interest in it.The proposed experiment allows to check how these images influence the pattern or metrics of visual attention of users as well as their cognitive processing. The eye tracking technology was chosen with the intention of knowing several aspects of this fixation behaviour according to: the position of the image on the website, whether or not it belongs to the Millennial generation and the type of navigation it performs (exploratory vs. goal oriented), in addition to proposing some interaction effects between these classification variables studied.The results achieved revealed that an image in the upper area of a website is more effective, but there are some differences in the visual attention among groups of participants. This attention to it will depend, in part, on the type of task to be carried out within the website and the user age. Specifically, goal-oriented users showed a lower recurrent interest in the main image. The fixation was shorter in the group belonging to the Millennial generation, who do not need, given their greater experience in the web, a longer time to process the cognitive load of the image. Although the hypotheses proposed on the basis of the literature are not entirely satisfactory, the present study allows laying the foundations for more complex future works.     

网络教育培训用户采纳意愿实证研究

随着网络技术和信息技术的迅猛发展,教育培训在社会生活中显得越来越重要,中国的教育培训市场也正在快速发展,据报告显示中国培训业规模正以每年30%的速度递增.在当今需要高水平、高知识层次人才的社会环境下,网络教育培训事业随之蓬勃发展.本研究基于技术接受模型,实证研究个体对网络教育培训服务的采纳行为.第一,基于技术接受模型和信任、网络外部性、感知费用、预期绩效等因素提出研究模型.第二,通过对问卷数据进行信效度和路径分析检验,验证影响因素间的假设,同时采用性别因素对个体使用网络教育培训服务的调节效应进行分析.研究结果显示,信任与网络外部性对感知有用性有直接影响,信任与感知有用性对使用意愿有直接影响,使用意愿对绩效预期有直接影响,而性别对个体使用网络教育培训服务的调节效应并不显著.最后对研究结果和不足进行讨论.     

The Effects of Different Teaching Approaches in Introductory Financial Accounting

Abstract

The purpose of the research is to examine the effect of the two different teaching approaches in the first accounting course on student performance in a subsequent finance course. The study compares 128 accounting and finance students who took introductory financial accounting by either a user approach or a traditional preparer approach to examine their academic performance on a subsequent finance course. The results indicate that there is no significant difference in the finance course grade between students who took the traditional introductory accounting course and students who were taught under the user approach. These findings support the argument that the user approach has no impact on subsequent finance course grade and either method can be used to teach introductory financial accounting. Three factors were found to affect subsequent performance in an introductory finance course: GPA prior to taking the finance course, grades in macroeconomics, and statistics.     

Estimating Capital Inputs for Productivity Measurement: An Overview of U.S. Concepts and Methods

The U.S. Bureau of Labor Statistics (BLS) publishes measures of multifactor productivity, which are patterned after the Solow residual. Inputs of capital must be aggregated with inputs of labor. The theory requires a measure of the capital service flow, a rather abstract notion which is rarely observable. The BLS procedures for capital measurement are summarized, and the rationale for these procedures is explored. Implicit measures of capital services are derived from data on property income and data on historical investments which are detailed as to type of asset.     

Web2.0环境下企业网络公关策略研究

在信息时代,企业的公关活动已经不能局必须直面来自互联网上的新兴媒体力量.Web2.0时代,企业公关活动挑战和机遇并重.通过Web2.0平台,企业能够极大的提升自己的口碑,同样也有可能遭遇自己前所未有的危机.本文针对Web2.0,特别是CGM媒体环境的发展和应用图景进行描述,对未来Web2.0将会主导媒体的大环境预言,在...     

Developing Official Stochastic Population Forecasts at the US Census Bureau

The U.S. Census Bureau is approaching a critical decision regarding a major facet of its methodology for forecasting the United States population. In the past, it has relied on alternative scenarios, low, medium, and high, to reflect varying interpretations of current trends in fertility, mortality, and international migration to forecast population. This approach has limitations that have been noted in the recent literature on population forecasting. Census Bureau researchers are embarking on an attempt to incorporate probabilistic reasoning to forecast prediction intervals around point forecasts to future dates. The current literature offers a choice of approaches to this problem. We are opting to employ formal time series modeling of parameters of fertility, mortality, and international migration, with stochastic renewal processes. The endeavor is complicated by the administrative necessity to produce a large amount of racial and Hispanic origin detail in the population, as well as the ubiquitous cross-categories of age and sex. As official population forecasts must meet user demand, we are faced with the added challenge of presenting and supporting the resulting product in a way that is comprehensible to users, many of whom have little or no comprehension of the technical forecasting literature, and are accustomed to simple, deterministic answers. We may well find a need to modify our strategy, depending on the realities that may emerge from the limitations of data, the administrative requirements of the product, and the diverse needs of our user community.