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Blown up theory is very important in modern forecasting science, and will result in revolution not only in forecasting theories but also in applied theories and applied methods. Moreover, the blown‐up theory will involve re‐thinking and re‐formulation of some concepts in traditional theories. This article is a record of dialogue between Professor OuYang and the author on some important issues. It is believed that this record will not only benefit us greatly, but also be inductive for young generations in developing their way of thinking and research directions.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many firms use to try to predict budgets and to provide guidance as to the direction the business is headed. This chapter focuses on forecasting and how well business schools match the requirements of industry professionals. Considering its importance to achieving successful business outcomes, forecasting is increasingly becoming a more complex endeavor. Firms must be able to forecast accurately to gain an understanding of the direction the business is taking and to prevent potential setbacks before they occur. Our results suggest that, although techniques vary, in large part business schools are introducing students to the forecasting tools that graduates will need to be successful in an industry setting. The balance of our chapter explores the forecasting tools used by business schools and firms, and the challenge of aligning the software learning curve between business school curriculum and industry expectations.

There is a growing interest in fuzzy time series (FTS) forecasting, and several improvements are presented in the last few decades. Among these improvements, the development of causal models (i.e., multiple factor FTS) has sparked a particular literature dealing with the causal inference and its integration in the FTS framework. However, causality among variables is usually introduced as a subjective assumption rather than empirical evidence. As a result of arbitrary causal modeling, the existing multiple factor FTS models are developed with implicit forecasting failure. Since post-sample control (unknown future, as in the business practice) is usually ignored, the spurious accuracy gain through increasing factors is not identified by scholars. This paper discloses the use of causality in the FTS method, and investigates the spurious causal inference problem in the literature with a justification approach. It invalidates the contribution of dozens of previously published papers while justifying its claim with illustrative examples and a comprehensive set of experiments with random data, as well as real business data from maritime transportation (Baltic Dry Index).

This paper aims to document indigenous knowledge systems (IKS) used for short- and long-range rainfall prediction by small holder farmers in three communities of Guruve District, in north-eastern Zimbabwe. The study also investigated farmers’ perceptions of contemporary forecasts and the reliability of both IKS and contemporary forecasts.

Data were collected among small holder farmers in Guruve District using household interviews and focus group discussions in three wards in the district, grouped according to their agro-climate into high and low rainfall areas. To get an expert view of the issues, key informant interviews were held with key agricultural extension personnel and traditional leaders.

Results obtained showed show high dependence on IKS-based forecasts in the district. Over 80 per cent of the farmers used at least one form of IKS for short- and long-range forecasting, as they are easily understood and applicable to their local situations. Tree phenology, migration and behaviour of some bird species and insects, and observation of atmospheric phenomena were the common indicators used. Tree phenology was the most common with over 80 per cent of farmers using this indicator. While some respondents (60 per cent) viewed forecasts derived from IKS as more reliable than science-based forecasts, 69 per cent preferred an integration of the two methods.

The simplicity and location specificity of IKS-based forecasts makes them potentially useful to smallholder farmers, climate scientists and policymakers in tracking change in these areas for more effective climate change response strategies and policymaking.

New technologies are posing new challenges to social science. Their very novelty also challenges the established methods that social research institutions have used to define their priorities. The UK’s Economic and Social research Council (ESRC) confronted these challenges, in part, by commissioning a futures study. It engaged the Institute for Alternative Futures (IAF) and the Centre for Research on Innovation and Competition (CRIC), to develop quickly a process for informing the choice of social science research priorities related to genomics. Four major reports were developed as background inputs to a scenario workshop process. As well as outlining a set of scenarios for the development of the genomics field, reports covered genomic applications, forecasts for drivers shaping genomics, and how the ESRC’s “thematic priorities” might relate to developments in genomics in the coming years. With this input and using advanced “groupware”, the scenario workshop identified five priority areas focused on how research should be conducted and 11 priority topics for what research is needed.

This article presents an introduction to the Delphi method and review of Delphi studies published in the literature of library and information science (LIS).

A review of Delphi studies published between the years of 1971 and 2019 is performed, using studies retrieved from the Library and Information Science Source database. A total of 122 articles were retrieved and evaluated based on the population studied, means of identifying experts, number of participants for each study round, type of Delphi, and type of findings.

General librarians (any type), academic librarians, and information science researchers are the most common populations in LIS Delphi studies. On average (middle 50 percent of studies), 14–36 experts are used in the first round of LIS Delphi studies (median n = 23). Employment in a specific role and publications in scholarly journals are the most common means of identifying experts. Variants of the e-Delphi (online survey/email) method are increasingly common, particularly in LIS Delphi studies that focus on general information science, rather than library, topics. Though LIS Delphi studies are relatively few in number, they have a consistent record of being published in some of the most prestigious LIS journals.

This paper provides an introduction to the Delphi method for LIS research and presents an overview of existing literature in LIS that utilizes the research method. No overview of this extent exists in the LIS literature, and, thus, this paper may serve as an important information source about the method for LIS researchers.

The purpose of this paper is to investigate and clarify “irrationality” problem through the maritime industry practices and leading incentives behind common investors.

This paper includes a review of broader business and economics literature; review of shipping business practices and detection of institutional pathways and misleading mechanisms behind the irrational preferences; investigation of data (for some arguments); and introduction of a theoretical approach.

There are several industry practices and norms well established and followed by decision makers, which may cause and initiate illogical and irrational (long-run) preferences. Short-termism is an erroneous habit of common shipping investors, which is embedded and forced through traditional financial math (i.e. discounted cash flow), financial system (e.g. initial public offerings with high-frequency transactions, interest rate governance and asset valuation mechanism) or flawed contracting tradition (i.e. commission bias).

Both shipping business and financial institutions need to redesign their working mechanisms, evaluation systems, risk detection and assessment procedures. As discussed in Section 4.7, commission-based (float) services must be converted to regular flat rate payments with long-term contracts to protect investors from rational choices of intermediaries in the short-run which encourages investor’s irrationality. Having a long-term service contract will also improve sustainability of intermediaries and lower their business risk (win-win).

The impact of this paper is two-fold. First, it raises critical questions about professional decay and drawbacks of some traditional instruments in the shipping business. For the first time, this paper emphasises on various challenges which deteriorate credibility of the industry and causes ill-defined investments. Some arguments have extreme priority for strengthening the foundations of the industry. Second, this paper establishes a new stream of scholarly research highlighting weaknesses of conventional economic approach and demand for outsourcing other schools of economics (e.g. institutional and behavioural) into the shipping business.

The study aims to build up knowledge for collateral exploration of the classical Delphi survey method for assessing the industrial sustainability-related correction factor using a real field study in Nigeria.

The paper elicits the character and the operational approaches using an example study to provide a critical review of the method. It estimates the correction factor for appraisal purposes by transforming expert opinion into a valid group consensus.

The work considers the specific parameters of the method, design and analysis for interpretation to prove the reliability and the validity for the research results. Moreover, it emphasises that the validity of the traditional Delphi research demands cautious theoretical and practical applications by the coordinating researcher. The paper establishes the current validity and effectiveness of the classical Delphi method of foresight and streamlines their efficient implementation for theory building despite its numerous weaknesses.

It explores desirable futures for the method while analysing what is possible and probable.