External pressure or internal culture? An innovation diffusion theory account of small retail businesses’ social media use

COVID-19 put unprecedented external pressure on small businesses to adopt or increase use of social media while not all small businesses are internally ready for this rapid change. This study investigated the roles of external pressures and organizational culture of openness and learning in driving small retail business owners'/managers' social media use decisions by impacting their perceived usefulness and barriers, based on the innovation-decision process model from the diffusion of innovations theory and the theory of reasoned action. An online survey with structured measurements was administered to 411 U.S. small retail business owners/managers. Results from structural equation modeling revealed that external pressures positively influenced small business owners'/managers’ perceived usefulness and barriers and social media use intention. In addition, the culture of openness and learning positively influenced the perceived usefulness while mitigating the perceived barriers, thereby directly and indirectly influencing the social media use intention. Theoretical and managerial implications are discussed.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Uncertainty,story-telling and transformative learning: An instructor’s experience of TEFI’s Walking Workshop in Nepal

ABSTRACT

This article recounts three stories from TEFI’s walking workshop in Nepal: the construction of a road through what was once a trekking path; a dance-floor encounter at a Himalayan party; and the arrival of one participant, fatigued by jet lag and disoriented by the new surroundings. These stories of confusion, discomfort and fear are linked by one common theme: the potential of uncertainty to foster deep reflection and nuanced conclusions. The premise that uncertainty is to be valued and even cultivated has been explored in educational theory, spiritual traditions, and research on transformative learning. These sources affirm the role of uncertainty in the process of knowledge creation. However, accepting this role can be challenging for educators because it requires they assume a new identity, one which they may perceive as being at odds with their status as “teacher” the identity of learner. One way for the educator to address this challenge may be through recounting their own stories of uncertainty. Stories of being immersed in unfamiliar situations that challenge, confuse and even frighten – stories, in other words of being a tourist – can foster reflection on an intellectual, emotional and spiritual level, engaging the “whole” person, and thus initiating the educator/learner’s transformative journey.     

The Right Choice at the Right Time: A Herding Experiment in Endogenous Time

Herding describes the phenomenon in decision-making where an economic agent disregards his own private information to follow the actions of his predecessors as in Banerjee (1992). With later decision-makers simply copying earlier decisions their private information cannot be inferred by other decision-makers and will be forever lost. There is some experimental evidence on simple sequential herding of this type in the literature, notably Anderson and Holt (1997). This paper differs by allowing subjects to delay their decision-making in order to benefit from observing others' actions as in more recent herding models such as Chamley and Gale (1994). The results in this paper suggest that subjects will indeed delay when their private information is not sufficiently strong. Despite this ability to wait, as predicted in the theoretical literature, cascades remained ubiquitous and more worrying still, reverse-cascades occurred in which incorrect decisions made by early decision-makers produced informational cascades on the wrong action. In an alternative design, informing subjects that they had made incorrect choices only made matters worse as subjects moved further away from rational behavior.     

Learning in Design and Technology: The Impact of Social and Cultural Influences on Modelling

The paper introduces the highly problematic nature of modelling in design and technology education and examines the relationship between cognitive and concrete modelling. Its aim is to gain insight into what learners do, rather than what others say they ought to do in their learning activities. The variety of purposes that educators have for learners’ modelling are discussed through examining the contested curriculum justification for design and technology education itself. The paper proposes that learners’ modelling cannot be extracted from the social milieu in which they act and it provides some insights of these social influences through the analysis of two case studies. Their settings are a girls’ secondary school and a college of higher education. Each case study is presented independently but organised with a common format to consider a) the impact of assessment on learning intentions and outcomes; b) cultural influences on learning and modelling; c) social influences on learning and modelling. A discussion of the emergent themes considers implications for teachers. This revised version was published online in July 2006 with corrections to the Cover Date.     

Finding Pareto Optimal Insurance Contracts

This paper deals with on-line computation—or step-wise learning—of Pareto optimal insurance contracts. Our approach tolerates that the loss distribution might be unknown, intractable, or not well specified. Thus we accommodate fairly inexperienced parties. Losses are here simulated or observed, one at a time, and they cause iterated revisions of the premium. The mechanical and global nature of probability calculus thereby yields to more tentative, myopic procedures, possibly closer to how humans operate or reason in face of risk. Sequential revisions may also reduce the expense of insurers' time and money in seeking sufficient statistics. Emphasized below is the remarkable simplicity and stability of the resulting adaptive procedures. Special attention goes to catastrophic risks, and to subsidized or competitive insurance.     

工作本位学习理论的系统分析

工作本位学习是基于从学校到工作岗位(school-to-work)的理念下提出来的,试图整合学习策略和工作实践,促进学习者的学习,以更好适应工作的需要。工作本位课程理论是在上述理念基础上开发出来的,对我国高职教育提供有益的启示。     

论英语教学与学生的非智力因素培养

目前，英语学习中智力和非智力因素是影响学生学业成绩的两个重要因素。人的智力包括注意力、观察力、思维和记忆能力；而非智力是指人的意志、兴趣、动机和情感。本文就教师如何在英语教学中培养学生的非智力因素，促进他们消除学习方法上存在的某些障碍作了一些分析，以便帮助他们提高学习成绩。     

Implementing reverse e-auctions: A learning process

Reverse e-auctions, which enable suppliers to compete on-line in real-time, are changing the way organizations select their suppliers. We explore how five large firms in different industries learned to use e-auctions, and how e-auctions were integrated into their purchasing processes. To successfully implement e-auctions, organizations should: (1) build e-auction competencies; (2) organize for knowledge management; (3) create a holistic sourcing process; (4) focus on the total cost of ownership; and (5) experiment with e-auction designs. Key observations are drawn from the case studies, and implications for supply managers are presented.