مقاله انگلیسی رایگان در مورد جمع سپاری در مقابل روش دلفی
|عنوان مقاله||Finding the future: Crowdsourcing versus the Delphi technique|
|ترجمه عنوان مقاله||یافتن آینده: جمع سپاری در مقابل روش دلفی|
|تعداد صفحات مقاله||۸ صفحه|
|رشته های مرتبط||مدیریت|
|گرایش های مرتبط||مدیریت فناوری اطلاعات|
|مجله||افق های تجارت – Business Horizons|
|دانشگاه||بخش بازاریابی صنعتی، دانشگاه صنعتی شریف، سوئد|
|کلمات کلیدی||پیش بینی تجارت، تکنیک دلفی، جمع سپاری، بازار پیش بینی، مسابقه نوآوری|
|تعداد کلمات||۴۷۸۰ کلمه|
|لینک مقاله در سایت مرجع||لینک این مقاله در سایت الزویر (ساینس دایرکت) Sciencedirect – Elsevier|
|وضعیت ترجمه مقاله||ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.|
|دانلود رایگان مقاله||دانلود رایگان مقاله انگلیسی|
|سفارش ترجمه این مقاله||سفارش ترجمه این مقاله|
|بخشی از متن مقاله:|
|۱٫ Finding the future: Crowdsourcing versus
the Delphi technique The source of the expression, ‘‘It’s difficult to make predictions, especially about the future,’’ is uncertain. This saying has been attributed variously to Niels Bohr, Samuel Goldwyn, Yogi Berra, and Mark Twain. Its uncertainty notwithstanding, the maxim is undoubtedly true. Forecasting or predicting what will happen in the future is one of the most difficult tasks humans attempt to accomplish. Yet forecasting is an integral part of a manager’s role and executives are called upon constantly to make decisions and take positions now that will only come to fruition at some time in the future. Will markets develop and grow or wilt and decline? Will a new product succeed or fail? What effects will an advertising campaign have on customer perceptions? What will the best name for a new product be? Will an investment now pay off in 5 or 10 years? What are the most important issues that an organization should focus on and how should these be prioritized?
Some forecasting methods extrapolate data from the past to predict what that same data will be like in the future. Time series analysis relies on various statistical techniques ranging from moving averages and simple linear regression to more sophisticated tools, such as the Box-Jenkins method (Box & Jenkins, 1970). Users of these methods presume that data patterns of the past will extend beyond the present. Whether they will remain stationary, fluctuate, increase, decline, or maintain can be determined by mathematical manipulation. When hard data is unavailable or when environmental conditions are very dynamic, forecasting resorts to opinions and judgments. Traditionally, experts have been sought after for these opinions. After all, who better to foretell the future within a particular domain than those who have truly experienced its past and live in its present? One of the best known and most widely used tools for polling the opinions of experts is the Delphi technique. However, more recently it has been contended that, in some contexts, a very large group of lay people without recognised expertise are collectively as adept as many experts at coming up with insightful answers. This approach, variously known as the wisdom of crowds (Surowiecki, 2005) or crowdsourcing (Howe, 2006), has recently gained prominence because large crowds of individuals can be easily accessed through technology. Within the broad concept of crowdsourcing, the subcategories of prediction markets (Graefe, Luckner, & Weinhardt, 2010; Wolfers & Zitzewitz, 2004) and innovation contests (Boudreau & Lakhani, 2013) provide familiar tools that countless organizations have used profitably. A simple request on a website to provide suggestions or answers, often fueled by tweets or reminders on a Facebook page, can rapidly lead to hundreds or even thousands of responses.
In this article, I describe and compare the Delphi technique and crowdsourcing activities, noting where they are similar and where they differ. I also discuss instances and opportunities where they might be used as forecasting tools and provide a decision tool for managers to aid their choice between these two useful techniques.