基于职业能力的网络营销课程教学研究

网络营销是电子商务专业的核心课程。从职业角度看,网络营销是电子商务行业的重要岗位之一。网络营销是与教学内容和职业能力紧密相关的一门课程。在教学过程中,教师应根据网络营销的岗位要求完善授课内容和教学方法。网络营销课程应通过教学理念的创新、校企合作的深化、任课教师专业能力的提升、实践环节的加强、网络学习环境的构建、把握考核的灵活性等提升教学效果,更好地培养学生的网络营销职业能力。     

Responding to catastrophe: A case study of learning from perinatal death in midwifery practice

Problem/BackgroundPerinatal death has far reaching emotional effects for all involved in this devastating event. The opportunity for learning as a result of this catastrophe, however, remains unexplored.AimTo explore midwives’ experiences of caring through, and learning from, perinatal death, to better inform the effective planning and delivery of education that optimises both midwifery and self-care.MethodsA naturalistic interpretive multiple case study design. Seventeen midwives, located in Australia, participated in an online group activity hosted as a blog, followed by telephonic focus groups and in-depth email interviews.FindingsThematic data analysis revealed seven major themes: Grappling with the reality of perinatal death; Struggling with personal and professional heartache; Seeking the space to grieve as a professional; Being with the woman and her family; Finding a new purpose; Strengthened through support; and Developing the courage to care.DiscussionThe initial turmoil and impact of loss reflected the catastrophic nature of perinatal death. Midwives uncovered a journey to acceptance and learning, realising a determination to enhance expertise and discovering value in experiential knowledge. Insecurity regarding competence and confidence to manage perinatal death and bereavement care was highlighted. However, sharing their stories revealed professional fulfilment, personal strength, and solidarity amongst midwives who have endured similar experiences.ConclusionA coordinated approach to support and the dissemination of experiential knowledge and learning could be developed within an online model of narrative sharing and discussion. Debriefing, support and sharing of expertise in this way may foster engagement within and beyond the workplace.     

Media use predictors of online political efficacy among Internet users in five Arab countries

Informed by research on the uses and gratifications of the Internet for political activity, this study examines predictors of online political efficacy among Internet users in five Arab countries (N?=?4029): Saudi Arabia, Tunisia, Lebanon, Qatar, and the U.A.E. As predicted, variables presumed to be associated with political dissidence in Arab countries – unemployment, distrust of mainstream media, young age, and feeling country is on the wrong track – were not positively associated with online political efficacy. Counter to what was hypothesized, however, Internet reliance and social media use were not strongly or consistently associated with online political efficacy in the five countries. Rather, the strongest predictors of efficacy were a belief in news media credibility, print media use (newspapers, magazines, and books), belief in the reliability of online information, and tolerance of free speech online. Findings align with some prior research on predictors of online political efficacy, but not with common presumptions of the role the Internet plays among Arab publics.     

Online learning for quantile regression and support vector regression

We consider for quantile regression and support vector regression a kernel-based online learning algorithm associated with a sequence of insensitive pinball loss functions. Our error analysis and derived learning rates show quantitatively that the statistical performance of the learning algorithm may vary with the quantile parameter τ$\tau$. In our analysis we overcome the technical difficulty caused by the varying insensitive parameter introduced with a motivation of sparsity.     

The Impact of the Online Channel on Retailers' Performances: An Empirical Evaluation

The use of the Internet as an additional sales channel offers traditional retailers opportunities to reach expanded markets while improving the efficiency of their operations. Although the potential benefits of the online channel are clear, there are significant variations in the scope and depth of online channel use among retailers. Drawing from data on more than 100 publicly traded companies, this study examines the impact of online‐channel use on retailers’ performance. The results suggest that the online channel provides significant improvements in sales, cost, inventory, and return on investments. In addition, we find that the timing of online‐channel adoption does not play a significant role in performance improvement, but having a local store presence does.     

Selective responsiveness: Online public demands and government responsiveness in authoritarian China

The widespread use of information and communication technology (ICT) has reshaped the public sphere in the digital era, making online forums a new channel for political participation. Using big data analytics of full records of citizen-government interactions from 2008 to early 2014 on a nationwide political forum, we find that authoritarian China is considerably responsive to citizens' demands with a rapid growth of response rate; however, government responsiveness is highly selective, conditioning on actors' social identities and the policy domains of their online demands. Results from logistic and duration models suggest that requests which made by local citizens, expressed collectively, focused on the single task issue, and are closely related to economic growth are more likely to be responded to. These strategies adopted by Chinese provincial leaders reveal the scope and selectivity of authoritarian responsiveness.     

A general modeling approach to online optimization with lookahead

A vast number of real world problems are coined by an information release over time and the related need for repetitive decision making over time. Optimization problems arising in this context are called online since decisions have to be made although not all data is known. Due to technological advances, algorithms may also resort to a limited preview (lookahead) on future events. We first embed the paradigm of online optimization with lookahead into the theory of optimization and develop a concise understanding of lookahead. We further find that the effect of lookahead can be decomposed into an informational and a processual component. Based on analogies to discrete event systems, we then formulate a generic modeling framework for online optimization with lookahead and derive a classification scheme which facilitates a thorough categorization of different lookahead concepts. After an assessment of performance measurement approaches with relevance to practical needs, we conduct a series of computational experiments which illustrate how the general concept of lookahead applies to specific instantiations and how a knowledge pool on lookahead effects in applications can be built up using the general classification scheme.     

Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis

Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as cross-sectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction's price evolution and associated price dynamics. Specifically, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic, we mean that the model can predict the price of an auction “in progress” and can update its prediction based on newly arriving information. Forecasting price in online auctions is challenging because traditional forecasting methods cannot adequately account for two features of online auction data: (1) the unequal spacing of bids and (2) the changing dynamics of price and bidding throughout the auction. Our dynamic forecasting model accounts for these special features by using modern functional data analysis techniques. Specifically, we estimate an auction's price velocity and acceleration and use these dynamics, together with other auction-related information, to develop a dynamic functional forecasting model. We also use the functional context to systematically describe the empirical regularities of auction dynamics. We apply our method to a novel set of Harry Potter and Microsoft Xbox data and show that our forecasting model outperforms traditional methods.     

A Comparison of the Effects of a Linear and an Exponential Performance Pay Function on Work Productivity

This study examined how work productivity was affected by the way in which individual monetary incentives were related to performance. Two types of relationships, or performance pay functions, were compared: a linear function in which a specific per piece incentive was provided for each piece completed in excess of a performance standard and an exponential function in which the amount of the per piece incentive accelerated as productivity increased. Forty college subjects were randomly assigned to one of the two pay conditions. Each subject participated in 15 forty-five minute sessions. Subjects performed a computerized work task that simulated the job of a proof operator at a bank, entering the cash values of simulated bank checks using a computer keyboard. The dependent variable was the number of correctly complcted checks. Productivity was comparable for subjects exposed to the linear and exponential performance pay functions, even though subjects exposed to the exponential function earned significantly more money than the subjects exposed to the linear function. The results suggest that, within certain parameters that have yet to be determined, differences in the way in which monetary incentives are related to performance may not differentially affect performance.