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The influence of product involvement and emotion on short-term product demand forecasting
Abstract
Sales forecasters in industries like fast-fashion face challenges posed by short and highly volatile sales time series. Computers can produce statistical forecasts, but these are often adjusted judgmentally to take into account factors such as market intelligence. We explore the effects of two potential influences on these adjustments: the forecaster’s involvement with the product category and their emotional reactions to particular products. Two forecasting experiments were conducted using data from a major Italian leather fashion goods producer. The participants’ judgmental adjustments tended to lower the forecast accuracy, but especially when the participants had strong preferences for particular products. This appeared to result from a false consensus effect. The most accurate forecasts were made when the participants had no knowledge of the product and only received time series information, though a high level of involvement with the product category also led to a greater accuracy.
... Therefore, we should consider the characteristics of information receivers when analyzing the impact of UGC on consumers. Product involvement in the communication effect has helped explain consumer behavior in advertising, promotion stimulation, information search, and processing (Zaichkowsky, 1985;Belvedere and Goodwin, 2017). Therefore, this study introduces consumer product involvement as the moderating variable. ...
... Product involvement affects the evaluation and actual purchase of a product by consumers. Product involvement is an important factor for consumer product choice (Belvedere and Goodwin, 2017). When consumers perceive usefulness and trust through UGC interaction, they are involved in the product, and will consider the uncertain consequences of the product, resulting in perceived risk (Rahman, 2017;Habib et al., 2021). ...
User-generated content (UGC) refers to online information created and shared by consumers that can influence other users’ purchase decisions. Due to the rapid development of new technologies in business, marketers’ dependency on UGC is rising. This study aims to explain how the interaction quality of UGC affects its credibility as a source and the usefulness of its information, as well as how it influences consumers’ online purchase intention. In addition, we examine the moderating effect of product involvement. We collected data from 272 users of social media in China to conduct empirical research. The SPSS and Amos were used to analyze the data and test the hypotheses. The results reveal that UGC interaction quality is positively related to purchase intention, and this relationship is mediated by perceived usefulness and trust. Further, consumers’ product involvement negatively moderates the effect of perceived usefulness and trust on purchase intention. Findings from this study are expected to extend the literature on UGC and give benefits to marketers and online business operators.
... Kim et al., 2013;Mahajan et al., 1986). However, there is substantial evidence that experts are biased when forecasting the success of a new product (Belvedere and Goodwin, 2017;Markovitch et al., 2014;Tyebjee, 1987). ...
... The suggested PRB model relies on a freely available source that provides organisations with insights on product standing and competition. Since the data is available with high frequency, shifts in consumer interest can be monitored and associated with the sales expectations (Belvedere and Goodwin, 2017). The lead time over which such predictive PRB information is available would allow decision makers to continuously adjust their marketing and operational planning until release. ...
... Kim et al., 2013;Mahajan et al., 1986). However, there is substantial evidence that experts are biased when forecasting the success of a new product (Belvedere and Goodwin, 2017;Markovitch et al., 2014;Tyebjee, 1987). ...
... The suggested PRB model relies on a freely available source that provides organisations with insights on product standing and competition. Since the data is available with high frequency, shifts in consumer interest can be monitored and associated with the sales expectations (Belvedere and Goodwin, 2017). The lead time over which such predictive PRB information is available would allow decision makers to continuously adjust their marketing and operational planning until release. ...
With increased competition and shorter product life-cycles, forecasting the life-cycle sales of new products prior to launch is becoming increasingly important to marketers and demand planners. Our study contributes to the literature on new product adoption using analogies by augmenting information from previous generations with pre-release search traffic. In contrast to existing research, which relies on pre-release buzz information only for the launch phase, we consider life-cycle sales. First, we propose a model of pre-release buzz and market potential, establishing the connection between the two. Then, we validate this relationship with an empirical experiment on sequential video game sales. Our finding support that pre-release buzz contains predictive information up to 17 weeks prior to release and can increase life-cycle sales forecast accuracy up to 20%. The explanatory power of pre-release buzz varies across product generations. This evolution opens up marketing opportunities and highlights why it is important to manage pre-release buzz.
... Despite the rise of deep learning models in sales forecasting, traditional statistical models [89,90] continue to play a crucial role due to their effectiveness in handling sales data. Among these, the Autoregressive Integrated Moving Average (ARIMA) [91] model and its variant, the Seasonal ARIMA (SARIMA) [92], stand out for their ability to model various types of time series data, including those with seasonal trends. ...
In a dynamic business environment, the accuracy of sales forecasts plays a pivotal role in strategic decision making and resource allocation. This article offers a systematic review of the existing literature on techniques and methodologies used in forecasting, especially in sales forecasting across various domains, aiming to provide a nuanced understanding of the field. Our study examines the literature from 2013 to 2023, identifying key techniques and their evolution over time. The methodology involves a detailed analysis of 516 articles, categorized into classical qualitative approaches, traditional statistical methods, machine learning models, deep learning techniques, and hybrid approaches. The results highlight a significant shift towards advanced methods, with machine learning and deep learning techniques experiencing an explosive increase in adoption. The popularity of these models has surged, as evidenced by a rise from 10 articles in 2013 to over 110 by 2023. This growth underscores their growing prominence and effectiveness in handling complex time series data. Additionally, we explore the challenges and limitations that influence forecasting accuracy, focusing on complex market structures and the benefits of extensive data availability.
... In addition, forecasts can be adjusted because people expect that their own preferences for products will be similar to those of potential consumers in general, irrespective of what the statistical forecast is indicating. When this expectation is unjustified the phenomenon is known as the false consensus effect (Belvedere & Goodwin, 2017). Additionally, recency bias can cause the most recent error associated with a statistical forecast to be particularly influential as forecasters can overreact to it when making a subsequent forecast (Petropoulos, Fildes, & Goodwin, 2016). ...
Forecast value added' (FVA) is a term commonly used to measure the improved accuracy achieved by judgmentally modifying a set of forecasts produced by statistical methods or algorithms. Assessing the factors that prompt such adjustments, and when they are likely to improve accuracy, is important in company demand forecasting and planning but has not been studied sufficiently. The published research has taken various individualistic approaches, both in the questions examined and the data analysis and modelling. In this paper we have collected the publicly available data from these studies, six in total, to analyse them using a common framework. Questions include when do demand planners adjust their statistical forecasts, do adjustments improve accuracy and reduce any bias, does the size of the adjustment signal a more substantive and useful piece of information gathered by the demand planner, and are improvements consistent across companies? These questions are important in practice since the costs of error are substantial, while the process of adjustment is expensive and time consuming, but they are also theoretically interesting raising the question of why consistencies across companies arise and the circumstances when one organization is more effective than another. The key question is how organizations can improve on their current forecasting processes to achieve greater 'forecast value added'.
... This is further amplified by the judgmental processes used for new products (Kahn & Chase, 2018). We see an interesting avenue of future research in how this new kind of marketing intelligence can integrate into decision-making processes, hopefully mitigating biases involved with new products (Belvedere & Goodwin, 2017;Markovitch et al., 2014). ...
In today's competitive market environment, it is vital for companies to gain insight about competitors' new product launches. Past studies have demonstrated the predictive value of pre-release online search traffic (PROST) for new product forecasting. Relying on these findings and the public availability of PROST, we investigate its usefulness for estimating sales of competing products. We propose a model for predicting the success of competitors' product launches, based on own past product sales data and competitor's pre-release Google Trends. We find PROST increases predictive accuracy by more than 18% compared to models that only use internally available sales data and product characteristics of video game sales. We conclude this inexpensive source of competitive intelligence can be helpful when managing the marketing-mix and planning new product releases.
... Third, much research has been done on short-term forecasts of how much a new brand is accepted in the near future (trial plus repeat purchase) (Belvedere and Goodwin 2017;Diron 2008;Hosking et al. 2013; Lyócsa and Todorova 2020) than on long-term forecasts (ultimate trial and repeat rates). In the long-term forecast, the model will inform what proportion of potential customers will eventually try out the new brand and what proportion will convert to repeat buyers. ...
One of the challenges product managers face in formulating strategic decisions is how to effectively forecast the performance of a new product in the marketplace. This paper develops a stochastic model, which is then applied to household purchase data to predict ultimate trial and first repeat purchase rates for a new brand introduced in an existing category of common frequently purchased goods. Previous research directed at the estimation of these two common indicators of new product success, as well as critical components of volume forecasting models, has focused on the elapsed calendar time to purchase or repurchase of the new brand. However, typical scanner panel data also contain information regarding consumer purchasing behavior of other brands in the category. The model developed in this paper utilizes this richer category purchase data from a household scanner data to estimate these two critical forecasting parameters: ultimate trial and first repeat rates. Results from four instances, including two cases involving new products and two cases involving existing brands, are used to demonstrate the value of the category purchase approach in comparison to the elapsed calendar time to purchase approach. Results show that the category purchase approach is more accurate in predicting the percentage of clients who would ever try a new product in the market (i.e., eventual trial) and in predicting the first repeat rate. Results also demonstrate how this projected eventual trial and first repeat rates could be used to develop a better estimate of long-term market share. We demonstrate that this approach provides an improved forecasting tool and recommend that marketers should, when possible, take advantage of this method.
... This can sometimes induce the storage of unsold inventories rather than the promotion of clearance sales, even though this approach requires a more complex warehousing system than other industries (Shan et al., 2018). In FSCs, forecasts are usually more complex than in other contexts (Belvedere & Goodwin, 2017), owing the high uncertainty in anticipating demands . Promotions, seasonality, and volatile customer preferences influence the process and reduce accuracy (Mou et al., 2018). ...
The purpose of this study was to compare the competitive priorities of the slow and fast fashion retail operations of large retailers in an emerging economy. The study embraces the six largest Brazilian companies operating both fast and slow fashion businesses. The methodological path comprises a qualitative phase, in which four experts review the competitive priorities in fashion supply chains. Then follows a quantitative phase, in which the practitioners, supported by AHP, the analytical hierarchy process, weight the fast fashion and slow fashion supply chain priorities. Finally, the study compares the two sets of priorities by considering the uncertainty in the judgment. The most relevant priorities in slow fashion are price and quality, whereas those in fast fashion are customer relationships and flexibility. This study proposes guidelines for simultaneously managing these two strategies. In slow fashion, companies should reduce costs and improve quality by implementing online process controls. In fast fashion, companies should decrease lot sizes, increase the variety of mixes and assortments, and strengthen ties with customers. The main novelty of this study is the evidence that retailers operating fast and slow fashion must simultaneously manage two different strategies to achieve different priorities. In addition, some novel procedures are adopted to mitigate the uncertainty in the judgments.
... Research on adjusting forecasts from statistical models found that adjustments often increase errors (e.g. Belvedere & Goodwin, 2017;Fildes & Goodwin, 2007) or have mixed results (e.g. Franses, 2014;Lin, Goodwin, & Song, 2014)" (Armstrong & Green, 2018a, 2018b. ...
This essay applies the “ultimate broadening of the concept of marketing” for designing and implementing interventions in public laws and policy, national and local regulations, and everyday lives of individuals. The ultimate broadening of the concept of marketing: Marketing is any activity, message, emotion, or behavior by someone, firm, organization, government, community, or brand executed consciously or nonconsciously that may stimulate an observable or non-observable activity, emotion, attitude, belief, or thought by someone else, group, organization, firm or community. The broadening definition applies to the current interventions by national and state/provincial governments as well as healthcare facilities, medical science facilities, firms, and individuals to mitigate and eliminate the impact of the COVID-19 pandemic. Framing interventions as experiments is helpful in improving the quality of their designs, implementing them successfully, and validly interpreting their effectiveness. In January and February 2020, a few nations were exemplars for accurately forecasting the coming disaster of COVID-19 as a cause of illness and death and in designing/implementing effective mitigating strategies: Denmark, Finland, Republic of Korea, New Zealand, Norway, and Vietnam. While the COVID-19 prevention intervention tests now being run for several promising vaccines are true experiments, the researchers analyzing the data from these interventions may need prompting to examine the efficacy of each vaccine tested by modeling demographic subgroups for the members in the treatment and placebo groups in the randomized control trials.
... Since then, research in psychology has continued to support Meehl's findings (see Grove et al. 2000). Research on adjusting forecasts from statistical models found that adjustments often increase errors (e.g., Belvedere and Goodwin 2017;Fildes, Goodwin, Lawrence, and Nikolopoulos 2009) or have mixed results (e.g., Franses 2014;Lin, Goodwin, and Song 2014). ...
Problem
How to help practitioners, academics, and decision makers use experimental research findings to substantially reduce forecast errors for all types of forecasting problems.
Methods
Findings from our review of forecasting experiments were used to identify methods and principles that lead to accurate forecasts. Cited authors were contacted to verify that summaries of their research were correct. Checklists to help forecasters and their clients undertake and commission studies that adhere to principles and use valid methods were developed. Leading researchers were asked to identify errors of omission or commission in the analyses and summaries of research findings.
Findings
Forecast accuracy can be improved by using one of 15 relatively simple evidence-based forecasting methods. One of those methods, knowledge models, provides substantial improvements in accuracy when causal knowledge is good. On the other hand, data models – developed using multiple regression, data mining, neural nets, and “big data analytics” – are unsuited for forecasting.
Originality
Three new checklists for choosing validated methods, developing knowledge models, and assessing uncertainty are presented. A fourth checklist, based on the Golden Rule of Forecasting, was improved.
Usefulness
Combining forecasts within individual methods and across different methods can reduce forecast errors by as much as 50%. Forecasts errors from currently used methods can be reduced by increasing their compliance with the principles of conservatism (Golden Rule of Forecasting) and simplicity (Occam’s Razor). Clients and other interested parties can use the checklists to determine whether forecasts were derived using evidence-based procedures and can, therefore, be trusted for making decisions. Scientists can use the checklists to devise tests of the predictive validity of their findings.
... Since then, research in psychology has continued to support Meehl's findings (see Grove et al. 2000). Research on adjusting forecasts from statistical models found that adjustments often increase errors (e.g., Belvedere and Goodwin 2017;Fildes, Goodwin, Lawrence, and Nikolopoulos 2009) or have mixed results (e.g., Franses 2014;Lin, Goodwin, and Song 2014). ...
Problem: How to help practitioners, academics, and decision makers use experimental research findings to substantially reduce forecast errors for all types of forecasting problems.
Methods: Findings from our review of forecasting experiments were used to identify methods and principles that lead to accurate forecasts. Cited authors were contacted to verify that summaries of their research were correct. Checklists to help forecasters and their clients practice and commission studies that adhere to principles and use valid methods were developed. Leading researchers were asked to identify errors of omission or commission in the analyses and summaries of research findings.
Findings: Forecast accuracy can be improved by using one of 15 relatively simple evidence-based forecasting methods. One of those methods, knowledge models, provides substantial improvements in accuracy when causal knowledge is good. On the other hand, data models—developed using multiple regression, data mining, neural nets, and “big data analytics”—are unsuited for forecasting.
Originality: Three new checklists for choosing validated methods, developing knowledge models, and assessing uncertainty are presented. A fourth checklist, based on the Golden Rule of Forecasting, was improved.
Usefulness: Combining forecasts within individual methods and across different methods can reduce forecast errors by as much as 50%. Forecasts errors from currently used methods can be reduced by increasing their compliance with the principles of conservatism (Golden Rule of Forecasting) and simplicity (Occam’s Razor). Clients and other interested parties can use the checklists to determine whether forecasts were derived using evidence-based procedures and can, therefore, be trusted for making decisions. Scientists can use the checklists to devise tests of the predictive validity of their findings.
Key words: combining forecasts, data models, decomposition, equalizing, expectations, extrapolation, knowledge models, intentions, Occam’s razor, prediction intervals, predictive validity, regression analysis, uncertainty
Product demand forecasting is the core link of an intelligent supply chain. The article discusses the demand characteristics of seasonal fast-moving consumer goods and presents a diversified stacked regression model (RXOEL-X) that combines linear and multi-machine learning models. This model utilizes a model stacking strategy and adopts the ElasticNet model, combined with L1 and L2 regularization to handle complex relationships in the data and prevent overfitting. Empirical evaluation using real data from leading beverage companies demonstrates the model's superiority over other time series forecasting techniques in demand forecasting for smart supply chains.
A previsão de demanda por series temporais colabora com o planejamento e tomada de decisão, assumindo papel estratégico no processo de gestão organizacional. O objetivo deste estudo é realizar uma revisão da literatura sobre trabalhos que fizeram uso de séries temporais para previsão de demanda, identificando as áreas de aplicações, origem e principais resultados encontrados. A pesquisa é de caráter descritivo, exploratório, com abordagem qualitativa. Verificou-se a disseminação do assunto havendo destaque para os anos de 2019 e 2020, também se observou a grande importância da previsão de demanda no meio empresarial, em que diversos trabalhos foram aplicados. Conclui-se que os artigos evidenciam fatores positivos à utilização da previsão de demanda por meio de series temporais, sugerindo a aplicação destes em estudos correlatos.
The apparel industry has become highly competitive and complex as consumers’ demands vary, thus pressuring organisations in this sector to invest in better demand forecasting and planning tools. Apparel supply chains are affected by both external and internal factors which impact on the performance of organisations in the market. These dynamics have become challenges in the forecasting and planning of demand in apparel supply chains. This study explores demand forecasting and planning factors influencing the apparel industry. The significance of this study lies in the importance of identifying the factors that influence planning for future demand in order to accurately estimate supply quantities required to meet consumer needs. The study contributes to the existing body of knowledge as it provides insight into the various factors that influence demand planning for a prominent apparel retailer within the South African industry. The study is exploratory and descriptive. Thematic analysis was used to analyse the data. The findings reveal that there are factors that influence how demand forecasting and planning practices are conducted in the apparel industry. These include competition, economic issues, weather, system issues, poor internal collaboration, supplier issues and social media. It is important that organisations in the apparel industry should take these factors into consideration when planning for demand to ensure consumer needs can be met, thus improving the performance of the apparel industry.
Il paper esamina, mediante un’analisi della letteratura, le difficoltà e, nello stesso tempo, l’importanza della misurazione dell’accuratezza della previsioni di vendita come presupposto per migliorare, sia le attività di previsione, sia i processi di pianificazione delle attività produttive. Per conseguire questo obiettivo vengono analizzate tre tematiche fondamentali e strettamente collegate, costituite dal ruolo delle previsioni di vendita nella pianificazione della produzione, dalla varietà delle tecniche di previsione e dagli aspetti di misurazione delle performance, cioè della “forecast accuracy”. Il lavoro è essenzialmente teorico e consiste in un'accurata analisi dei principali contributi sulla previsione delle vendite, apparsi nella letteratura di management internazionale nell'ultimo decennio. Dallo studio emerge che la misurazione e la condivisione dei dati sull'accuratezza delle previsioni di vendita all'interno dell'organizzazione può accrescere il grado di utilizzo delle previsioni e favorire scelte di allocazione delle risorse produttive, coerentemente con il livello di incertezza delle previsioni utilizzate. Nonostante l'assenza di una ricerca empirica, lo studio contribuisce comunque ad arricchire l'attuale background teorico sugli aspetti gestionali dell'attività di previsione delle vendite nell'impresa, collegando diverse aree di ricerca sull'attività di sales forecasting.
This paper aims to investigate the difficulties and, at the same time, the importance of measuring sales forecast accuracy as a prerequisite for the improvement of both sales forecasting activities and production planning processes. To achieve this objective, three essential and closely related issues are analyzed: the role of sales forecasts in production planning, the variety of forecasting techniques and the main aspects of sales forecast accuracy measurements. This work is essentially theoretical and consists of an accurate review of the main studies on sales forecasting, published in management literature in the last decade. The analysis highlights how the measurement and sharing of data on forecast accuracy can improve the firm's decision-makers' attitude to using the forecasts and to formulating choices on resource allocation, consistent with the forecast uncertainty. Despite the absence of an empirical research, this study enriches the current theoretical background on firm's sales forecasting management, by linking different research areas on sales forecasting.
Betting on the most promising new ideas is key to creativity and innovation in organizations, but predicting the success of novel ideas can be difficult. To select the best ideas, creators and managers must excel at creative forecasting, the skill of predicting the outcomes of new ideas. Using both a field study of 339 professionals in the circus arts industry and a lab experiment, I examine the conditions for accurate creative forecasting, focusing on the effect of creators’ and managers’ roles. In the field study, creators and managers forecasted the success of new circus acts with audiences, and the accuracy of these forecasts was assessed using data from 13,248 audience members. Results suggest that creators were more accurate than managers when forecasting about others’ novel ideas, but not their own. This advantage over managers was undermined when creators previously had poor ideas that were successful in the marketplace anyway. Results from the lab experiment show that creators’ advantage over managers in predicting success may be tied to the emphasis on both divergent thinking (idea generation) and convergent thinking (idea evaluation) in the creator role, while the manager role emphasizes only convergent thinking. These studies highlight that creative forecasting is a critical bridge linking creativity and innovation, shed light on the importance of roles in creative forecasting, and advance theory on why creative success is difficult to sustain over time.
In turbulent markets, agility - the capacity to identify and capture opportunities more quickly than rivals do - is invaluable. Agility comes In three flavors: strategic (capturing game-changing opportunities), portfolio (reallocating resources), and operational (occurring within a focused business model). Downturns provide opportunities both to capitalize on agility and to become more agile. In this article, Donald Sull, a professor of management practice at the London Business School, draws on numerous examples from his research on turbulent markets around the world to suggest a way forward.
Judgmental and statistical forecasts can each bring advantages to the forecasting process. One way forecasters can integrate these methods is to adjust statistical forecasts based on judgment. However, judgmental adjustments can bias forecasts and harm accuracy. Forecasters should consider six principles in deciding when and how to use judgment in adjusting statistical forecasts: (1) Adjust statistical forecasts if there is important domain knowledge; (2) adjust statistical forecasts in situations with a high degree of uncertainty; (3) adjust statistical forecasts when there are known changes in the environment; (4) structure the judgmental adjustment process; (5) document all judgmental adjustments made and periodically relate to forecast accuracy; (6) consider mechanically integrating judgmental and statistical forecasts over adjusting.
Forecast adjustment commonly occurs when organizational forecasters adjust a statistical forecast of demand to take into account factors which are excluded from the statistical calculation. This paper addresses the question of how to measure the accuracy of such adjustments. We show that many existing error measures are generally not suited to the task, due to specific features of the demand data. Alongside the well-known weaknesses of existing measures, a number of additional effects are demonstrated that complicate the interpretation of measurement results and can even lead to false conclusions being drawn. In order to ensure an interpretable and unambiguous evaluation, we recommend the use of a metric based on aggregating performance ratios across time series using the weighted geometric mean. We illustrate that this measure has the advantage of treating over- and under-forecasting even-handedly, has a more symmetric distribution, and is robust.
Empirical analysis using the recommended metric showed that, on average, adjustments yielded improvements under symmetric linear loss, while harming accuracy in terms of some traditional measures. This provides further support to the critical importance of selecting appropriate error measures when evaluating the forecasting accuracy. The general accuracy evaluation scheme recommended in the paper is applicable in a wide range of settings including forecasting for fashion industry.
The successful introduction of new durable products plays an important part in helping companies to stay ahead of their competitors. Decisions relating to these products can be improved by the availability of reliable pre-launch forecasts of their adoption time series. However, producing such forecasts is a difficult, complex and challenging task, mainly because of the non-availability of past time series data relating to the product, and the multiple factors that can affect adoptions, such as customer heterogeneity, macroeconomic conditions following the product launch, and technological developments which may lead to the product’s premature obsolescence. This paper provides a critical review of the literature to examine what it can tell us about the relative effectiveness of three fundamental approaches to filling the data void : (i) management judgment, (ii) the analysis of judgments by potential customers, and (iii) formal models of the diffusion process. It then shows that the task of producing pre-launch time series forecasts of adoption levels involves a set of sub-tasks, which all involve either quantitative estimation or choice, and argues that the different natures of these tasks mean that the forecasts are unlikely to be accurate if a single method is employed. Nevertheless, formal models should be at the core of the forecasting process, rather than unstructured judgment. Gaps in the literature are identified, and the paper concludes by suggesting a research agenda so as to indicate where future research efforts might be employed most profitably.
An Integrated Logistic Support (ILS) service has the objective of improving a system’s efficiency and availability for the life cycle. The system constructor offers the service to the customer, and she becomes the Contractor Logistic Support (CLS). The aim of this paper is to propose an approach to support the CLS in the budget formulation. Specific goals of the model are the provision of the annual cost of ILS activities through a specific cost model and a comprehensive examination of expected benefits, costs and savings under alternative ILS strategies. A simple example derived from an industrial application is also provided to illustrate the idea. Scientific literature is lacking in the topic and documents from the military are just dealing with the issue of performance measurement. Moreover, they are obviously focused on the customer’s perspective. Other scientific papers are general and focused only on maintenance or life cycle management. The model developed in this paper approaches the problem from the perspective of the CLS, and it is specifically tailored on the main issues of an ILS service.
Highlights
► The marginal value of time defines the limits of time‐based advantage in supply chains. ► In supply chains the time value measures the marginal inventory cost of longer lead‐times. ► We show that the time‐value for a make‐to‐stock supply chain is surprisingly small. ► The low time‐value is consistent with the observed growth in global supply chains with long lead‐times.
Over the past two decades the growth in international trade and the offshore migration of US manufacturing have created global supply chains with longer lead‐times and slower response. This suggests that traditional supply chains have encountered limits to time‐based competition in which the cost of faster replenishment exceeds the benefits. This paper explores and quantifies those limits to time‐based competition in make‐to‐stock supply chains for functional products (products with stable demand over relatively long life cycles).
The marginal value of time is used to define the limits of time‐based competition in a supply chain, and we define it as the change in total inventory costs per unit change in supply chain lead‐time. To calculate the value of time, we develop a set of simple analytical expressions that apply to most standard reorder‐point inventory policies under deterministic and variable lead‐times. By not requiring optimal inventory policies and expressing the value of time in terms of the unit cost of the product, we obtain very general results that are essentially product‐free. We validate the analytical expressions using data from actual supply chains to simulate the inventory cost effects of changes in the lead‐time. The results show that the marginal value of time in a supply chain is surprisingly low; it generally falls within a range of 0.4–0.8% of product unit cost per week.
Our analytical models explain why there has been expansive growth in global supply chains for functional products. The key tradeoff in outsourcing is between reduced variable production cost and the increased inventory cost of longer supply chain lead‐times. The models show analytically that the incremental inventory cost is extremely small relative to the cost benefit. The growth in global supply chains with long lead‐times is cost, rather than time, driven.
Forecast adjustment commonly occurs when organizational forecasters adjust a statistical forecast of demand to take into account factors which are excluded from the statistical calculation. This paper addresses the question of how to measure the accuracy of such adjustments. We show that many existing error measures are generally not suited to the task, due to specific features of the demand data. Alongside the well-known weaknesses of existing measures, a number of additional effects are demonstrated that complicate the interpretation of measurement results and can even lead to false conclusions being drawn. In order to ensure an interpretable and unambiguous evaluation, we recommend the use of a metric based on aggregating performance ratios across time series using the weighted geometric mean. We illustrate that this measure has the advantage of treating over- and under-forecasting even-handedly, has a more symmetric distribution, and is robust.Empirical analysis using the recommended metric showed that, on average, adjustments yielded improvements under symmetric linear loss, while harming accuracy in terms of some traditional measures. This provides further support to the critical importance of selecting appropriate error measures when evaluating the forecasting accuracy.
[Correction Notice: An erratum for this article was reported in Vol 103(1) of
Psychological Bulletin (see record
2008-10686-001). The block quotation on page 73 should be attributed to Crocker (1981). The two sentences immediately preceding this quotation should read: 'Friendship groups typically exhibit a high degree of internal similarity with respect to members' beliefs, attitudes, values, and interests. Crocker (1981) reported the following:".] Ten years of research on the false-consensus effect (Ross, Greene, & House, 1977) and related biases in social perception (e.g., assumed similarity and overestimation of consensus) are examined in the light of four general theoretical perspectives: (a) selective exposure and cognitive availability, (b) salience and focus of attention, (c) logical information processing, and (d) motivational processes. The findings indicate that these biases are influenced by a host of variables and that no single explanation can account for the range of data. Instead, each theoretical perspective appears to have its own domain of application, albeit with some degree of overlap into other domains. The data further suggest that two or more specific mechanisms may operate simultaneously or in concert to produce assumed similarity and false-consensus effects. Discussion focuses on identifying the process or sets of processes operating in specific situations. We identify several gaps in the knowledge of mediating relationships and suggest directions for future research. We also discuss issues related to definition and measurement. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
You think your industry is tough? Imagine customer preferences that can shift literally overnight, product lifecycles measured in weeks, and the value of your product plummeting if you miss the latest trend. Welcome to the world of fast fashion.
Donald Sull and Stefano Turconi examine how Zara, a leader in the industry, has pioneered an approach to navigate the volatility of fast fashion, offering lessons for any company facing rapidly changing markets.
This research investigates preference uncertainty generated as a function of specific alternative characteristics during multiattribute evaluative judgments. We propose that preference uncertainty has at least two behavioral manifestations: longer judgment times and greater response error in expressed preferences. We investigate two hypotheses regarding stimulus-based causes of preference uncertainty. As predicted by our attribute conflict hypothesis, greater within-alternative conflict (discrepancy among the attributes of an evaluative alternative) led to longer judgment times and greater response error. As predicted by our attribute extremity hypothesis, greater attribute extremity (very high or low attribute values) resulted in shorter judgment times and less response error. We also found that judgment times and response errors were strongly positively correlated at the item level, consistent with our assumption that preference uncertainty generated by stimulus characteristics is manifested in judgment time and error. Finally, we found that the item-level preference uncertainty effects proposed here operate in parallel with strategy-level, effort-accuracy tradeoffs observable across participants. These findings are consistent with the RandMAU random multiattribute utility model developed in a companion article by Fischer et al. (2000).
The use of students as surrogates for non-students has been a controversial issue in behavioural accounting research. However, the empirical evidence on student surrogates suggests that students can be adequate surrogates for practitioners in decision-making tasks. This present paper seeks to examine the adequacy of accounting students as surrogates for their practising counterparts in an investment decision task. For this purpose, an experimental decision task used in Milne and Patten [Acc., Audit. Acc. J. 15 (3) (2002) 372] is replicated using student subjects. The results indicate that, in general, students’ short-term and long-term investment decisions compare well with those of the practitioners. These findings encourage more empirical investigations into the adequacy of students as surrogates for accounting practitioners.
Many medical decisions are made on a probability estimate. Models of risk benefit calculations in health behaviour rarely articulate how people perceive and appraise such probability. Cognitive processes may mediate the processing of probability expressions and may be important to understand the meaning or the range of meanings that probability statements portray to patients. Past studies have indicated that verbal expressions of probability are vague and subject to individual interpretation. On the theoretical level when subjects are asked to translate a set of verbal probability expressions, ranging from high to low, into their equivalent numerical expressions subjects usually produce a continuum of numerical equivalents also ranging from high to low. In practice clinicians frequently communicate information about uncertainty to the patients by verbal probability estimates. This study explored the effect of the order of presentation of the verbal expressions on the numerical probabilities produced by a group of medical students (n = 87) in relation to medical probabilities. The results showed that the order of presentation (descending vs. random) of the verbal probability expressions was found to have a significant effect on three of the seven numerical probabilities produced by the subjects. The order effect is discussed to together with implications for clinical practice.
A bipolar adjective scale, the Personal Involvement Inventory (PII), was developed to capture the concept of involvement for products. The scale successfully met standards for internal reliability, reliability over time, content validity, criterion-related validity, and construct validity. Tests of construct validity demonstrated that the scores were positively related to perceived differences among brands, brand preferences, interest in gathering information about the product category, and comparison of product attributes among brands.
What is the structure of emotion? Emotion is too broad a class of events to be a single scientific category, and no one structure suffices. As an illustration, core affect is distinguished from prototypical emotional episode. Core affect refers to consciously accessible elemental processes of pleasure and activation, has many causes, and is always present. Its structure involves two bipolar dimensions. Prototypical emotional episode refers to a complex process that unfolds over time, involves causally connected subevents (antecedent; appraisal; physiological, affective, and cognitive changes; behavioral response; self-categorization), has one perceived cause, and is rare. Its structure involves categories (anger, fear, shame, jealousy, etc.) vertically organized as a fuzzy hierarchy and horizontally organized as part of a circumplex.
Many judgmental biases are thought to be the product of insufficient adjustment from an initial anchor value. Nearly all existing evidence of insufficient adjustment, however, comes from an experimental paradigm that evidence indicates does not involve adjustment at all. In this article, the authors first provide further evidence that some kinds of anchors (those that are self-generated and known to be incorrect but close to the correct answer) activate processes of adjustment, whereas others (uncertain anchors provided by an external source) do not. It is then shown that adjustment from self-generated anchors does indeed tend to be insufficient, both by comparing the estimates of participants starting from different anchor values and by comparing estimates with actual answers. Thus, evidence is provided of adjustment-based anchoring effects similar to the accessibility-based anchoring effects observed in the traditional anchoring paradigm, supporting theories of social judgment that rely on mechanisms of insufficient adjustment.
In recent years, many U.S. and Japanese firms have adopted Quality Function Deployment (QFD). QFD is a total-quality-management process in which the “voice of the customer” is deployed throughout the R&D, engineering, and manufacturing stages of product development. For example, in the first “house” of QFD, customer needs are linked to design attributes thus encouraging the joint consideration of marketing issues and engineering issues. This paper focuses on the “Voice-of-the-Customer” component of QFD, that is, the tasks of identifying customer needs, structuring customer needs, and providing priorities for customer needs.
In the identification stage, we address the questions of (1) how many customers need be interviewed, (2) how many analysts need to read the transcripts, (3) how many customer needs do we miss, and (4) are focus groups or one-on-one interviews superior? In the structuring stage the customer needs are arrayed into a hierarchy of primary, secondary, and tertiary needs. We compare group consensus (affinity) charts, a technique which accounts for most industry applications, with a technique based on customer-sort data. In the stage which provides priorities we present new data in which product concepts were created by product-development experts such that each concept stressed the fulfillment of one primary customer need. Customer interest in and preference for these concepts are compared to measured and estimated importances. We also address the question of whether frequency of mention can be used as a surrogate for importance. Finally, we examine the stated goal of QFD, customer satisfaction. Our data demonstrate a self-selection bias in satisfaction measures that are used commonly for QFD and for corporate incentive programs.
We close with a brief application to illustrate how a product-development team used the voice of the customer to create a successful new product.
The fashion industry is a very fascinating sector for the sales forecasting. Indeed, the long time-to-market which contrasts with the short life cycle of products, makes the forecasting process very challenging. A suitable forecasting system should also deal with the specificities of the demand: fashion trends, seasonality, influence of many exogenous factors, …. We propose here a review of the different constraints related to the sales forecasting in the fashion industry, the methodologies and techniques existing in the literature to cope with these constraints and finally, the new topics which could be explored in the field of the sales forecasting for fashion products.
We study the conditions that influence judgmental forecasting effectiveness when predicting demand in the context of fashion products. Human judgment is of practical importance in this setting. Our goal is to investigate what type of decision support, in particular historical and/or contextual predictors, should be provided to human forecasters to improve their ability to detect and exploit linear and nonlinear cue-criterion relationships in the task environment. Using a field experiment on new product forecasts in the music industry, our analysis reveals that when forecasters are concerned with predictive accuracy and only managerial judgments are employed, providing both types of decision support data is beneficial. However, if judgmental forecasts are combined with a statistical forecast, restricting the decision support provided to human judges to contextual anchors is beneficial. We identify two novel interactions demonstrating that the exploitation of nonlinearities is easiest for human judgment if contextual data are present but historical data are absent. Thus, if the role of human judgment is to detect these nonlinearities (and the linearities are taken care of by some statistical model with which judgments are combined), then a restriction of the decision support provided would make sense. Implications for the theory and practice of building decision support models are discussed.
We compare how experienced procurement managers and students solve the newsvendor problem. We find that managers broadly exhibit the same kind of pull-to-center bias as students do. Also, managers use information and task training no better than students. The performance of managers is positively affected by the level of their education and their level in the organizational hierarchy. We discuss implications for theory and for how ordering might be improved in practice.
This paper was accepted by Teck Ho, decision analysis.
Fast fashion is a commonly adopted strategy in fashion retailing. Under fast fashion, operational decisions have to be made with a tight schedule and the corresponding forecasting method has to be completed with very limited data within a limited time duration. Motivated by fast fashion business practices, in this paper, an intelligent forecasting algorithm, which combines tools such as the extreme learning machine and the grey model, is developed. Our real data analysis demonstrates that this newly derived algorithm can generate reasonably good forecasting under the given time and data constraints. Further analysis with an artificial dataset shows that the proposed algorithm performs especially well when either (i) the demand trend slope is large, or (ii) the seasonal cycle's variance is large. These two features fit the fast fashion demand pattern very well because the trend factor is significant and the seasonal cycle is usually highly variable in fast fashion. The results from this paper lay the foundation which can help to achieve real time sales forecasting for fast fashion operations in the future. Some managerial implications are also discussed.
Companies undertaking operations improvement in supply chains face many alternatives. This work seeks to assist practitioners to prioritize improvement actions by developing analytical expressions for the marginal values of three parameters – (i) lead time mean, (ii) lead time variance, and (iii) demand variance – which measure the marginal cost of an incremental change in a parameter. The relative effectiveness of reducing lead time mean versus lead time variance is captured by the ratio of the marginal value of lead time mean to that of lead time variance. We find that this ratio strongly depends on whether the lead time mean and variance are independent or correlated. We illustrate the application of the results with a numerical example from an industrial setting. The insights can help managers determine the optimal investment decision to modify demand and supply characteristics in their supply chain, e.g., by switching suppliers, factory layout, or investing in information systems.
Previous work has shown that people use anchor-and-adjust heuristics to forecast future data points from previous ones in the same series. We report three experiments that show that they use different versions of this heuristic for different types of series. To forecast an untrended series, our subjects always took a weighted average of the long-term mean of the series and the last data point. In contrast, the way that they forecast a trended series depended on the serial dependences in it. When these were low, people forecast by adding a proportion of the last difference in the series to the last data point. When stronger serial dependences made this difference less similar to the next one, they used a version of the averaging heuristic that they employed for untrended series. This could take serial dependences into account and included a separate component for trend. These results suggest that people use a form of the heuristic that is well adapted to the nature of the series that they are forecasting. However, we also found that the size of their adjustments tended to be suboptimal. They overestimated the degree of serial dependence in the data but underestimated trends. This biased their forecasts.
This paper offers a framework in order to examine the temporal sequences of the activities and decisions of the textile apparel chain and to present the prospects for improvement initiatives throughout the various production and distribution chain phases, whether in terms of technology or of organisation and management. This paper focuses on the methods, techniques and approaches that could be adopted throughout the entire chain in order to obtain a set of coherent improvements to achieve quick response (QR). The fundamental improvement stages are considered to be: reduction of lead times in supplying fabric; reduction of lead times in producing a garment; and innovative relationships with the distributors. The structural and infrastructural changes required for a QR approach are also analysed, and the need for a systemic view of the chain is highlighted.
Evidence from 4 studies with 584 undergraduates demonstrates that social observers tend to perceive a "false consensus" with respect to the relative commonness of their own responses. A related bias was shown to exist in the observers' social inferences. Thus, raters estimated particular responses to be relatively common and relatively unrevealing concerning the actors' distinguishing personal dispositions when the responses in question were similar to the raters' own responses; responses differing from those of the rater, by contrast, were perceived to be relatively uncommon and revealing of the actor. These results were obtained both in questionnaire studies presenting Ss with hypothetical situations and choices and in authentic conflict situations. The implications of these findings for the understanding of social perception phenomena and for the analysis of the divergent perceptions of actors and observers are discussed. Cognitive and perceptual mechanisms are proposed which might account for distortions in perceived consensus and for corresponding biases in social inference and attributional processes. (33 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This article reviews evidence indicating that, in most circumstances, positive affect enhances problem solving and decision making, leading to cognitive processing that is not only flexible, innovative, and creative, but also thorough and efficient. These results have implications regarding consumers’ potential reactions to affect generated by ads, products, consumption situations, and service encounters.
These cognitive effects of positive affect are considered in the context of effects on social interaction that show that positive affect leads to helping, generosity, and interpersonal understanding. Together, these findings suggest implications especially for customer satisfaction, and particularly for a role for employee positive affect, or employee satisfaction, in generating customer satisfaction.
Moreover, studies specifically in the domain of medical decision making and problem solving indicate that these implications would apply to the specific areas of doctor–patient interaction, medical decision making, and medical consumer satisfaction. Finally, it is suggested that the benefits of positive affect be considered when formulating healthcare policy and be included in economic models and policy decisions more generally as well.
Executives use analogies to improve strategic decisions. However, existing research provides little guidance on the types and number of analogies that produce the best decisions. We examine models of analogy and present findings from two empirical tests. The first test, a study of private-equity investment decisions, finds that an ‘outside view’—forming a reference class of analogies—performs better than a few analogies familiar to the decision maker. The second test, a study of film revenue forecasts, compares a new model we call similarity-based forecasting (SBF) with existing methods. The study finds that SBF, which combines elements of reference class forecasting and case-based decision making, produces better forecasts than regression models. We discuss the consequences of our findings for research and practice. Copyright © 2012 John Wiley & Sons, Ltd.
This research analyzes how individual differences affect performance in judgmental time-series forecasting. Decision makers with the ability to balance intuitive judgment with cognitive deliberation, as measured by the Cognitive Reflection Test, tend to have lower forecast errors. This relationship holds when controlling for intelligence. Further, forecast errors increase for very fast or very slow decisions. We provide evidence that forecast performance can be improved by manipulating decision speed.
Before and during the 2010 Soccer World Cup, participants made probabilistic forecasts for the outcomes of the tournament. We examine the relationship between their depression levels and their performance in this forecasting task. Across two different waves of predictions and with multiple measures and components of prediction accuracy, we find that depressed forecasters were less accurate. The poorer accuracy amongst the more depressed forecasters was primarily driven by neglect for base rate probabilities: The depressed participants assigned probabilities that departed more substantially from the base rates, particularly for low base rate events. Given the high incidence of depression in the workforce, the importance of judgmental probabilistic forecasting in many settings, and that we may be the first to look at depression- accuracy relationship using a real-world prediction task involving exogenous uncertainty, these findings may have important implications for both theory and practice.
The contribution of product and industry knowledge to the accuracy of sales forecasting was investigated by examining the company forecasts of a leading manufacturer and marketer of consumable products. The company forecasts of 18 products produced by a meeting of marketing, sales, and production personnel were compared with those generated by the same company personnel when denied specific product knowledge and with the forecasts of selected judgemental and statistical time series methods. Results indicated that product knowledge contributed significantly to forecast accuracy and that the forecast accuracy of company personnel who possessed industry forecasting knowledge (but not product knowledge) was not significantly different from the time series based methods. Furthermore, the company forecasts were more accurate than averages of the judgemental and statistical time series forecasts. These results point to the importance of specific product information to forecast accuracy and accordingly call into question the continuing strong emphasis on improving extrapolation techniques without consideration of the inclusion of non-time series knowledge.
Sales forecasting is a challenging problem owing to the volatility of demand which depends on many factors. This is especially prominent in fashion retailing where a versatile sales forecasting system is crucial. This study applies a novel neural network technique called extreme learning machine (ELM) to investigate the relationship between sales amount and some significant factors which affect demand (such as design factors). Performances of our models are evaluated by using real data from a fashion retailer in Hong Kong. The experimental results demonstrate that our proposed methods outperform several sales forecasting methods which are based on backpropagation neural networks.
Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company’s demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.
The past 25 years has seen phenomenal growth of interest in judgemental approaches to forecasting and a significant change of attitude on the part of researchers to the role of judgement. While previously judgement was thought to be the enemy of accuracy, today judgement is recognised as an indispensable component of forecasting and much research attention has been directed at understanding and improving its use. Human judgement can be demonstrated to provide a significant benefit to forecasting accuracy but it can also be subject to many biases. Much of the research has been directed at understanding and managing these strengths and weaknesses. An indication of the explosion of research interest in this area can be gauged by the fact that over 200 studies are referenced in this review.
Most laboratory research in business has used graduate or undergraduate business students as subjects. These subjects have been assumed to be suitable surrogates for business managers and, therefore, the experimental results should be applicable to real businesses. This experiment compared the decision making of line managers no lower than second level with that of students with no managerial experience. The level of educational achievement was held constant between the two groups. There were no significant differences between these two groups in making production scheduling decisions.
Eighteen time series differing in their trend (three categories), randomness (three categories), and presentation on a graph (two categories) were given to 350 MBA students in a laboratory experiment. Each student was asked to estimate judgmentally a forecast and confidence interval. The results showed that when compared to the commonly used forecasting approach of simple regression, the judgmental forecasts differed significantly in their response to trend and presentation but not to randomness. The judgmental confidence intervals were very influenced by trend but insufficiently influenced by randomness when compared to the regression estimates.
Despite all efforts, many product development projects fail and lead to the introduction of products that do not meet customers' expectations. A high level of customer satisfaction cannot be obtained. On the other hand, in many product development projects the process of product development is conducted very unsystematically and resources are wasted because of a lack of communication between the different functions involved in product development. Time especially is a critical factor within product development as time to market is becoming increasingly more important.Managers need a set of practical step-by-step tools and methods which ensure a better understanding of customers' needs and requirements, as well as procedures and processes to enhance communication by focusing on the voice of the customer within a product development project.The authors propose a methodology, based on Kano's model of customer satisfaction, to explore customers' stated needs and unstated desires and to resolve them into different categories which have different impacts on customer satisfaction. It is shown how this categorization can be used as a basis for product development, especially for quality function deployment. The paper begins with a brief discussion of the strategic importance of customer satisfaction, then Kano's model and its combination with quality function deployment is demonstrated, using a case study from the ski industry. The paper closes with a brief discussion of the managerial implications and the consequences of the application of these tools.
This paper reports a study which examines the ability of people and statistical models to forecast time series which contain major discontinuities. It has often been suggested that human judgement will be superior when circumstances change dramatically and statistical models are no longer relevant. Using ten time series that contained five different discontinuities and two levels of randomness, the results indicated that people performed significantly worse than (parsimonious) statistical models. This occurred for the segments of the time series where the discontinuity was to be found and for the subsequent segment where the series was stable. People seemed to change their forecasts in response to random fluctuations in the time series, identifying a signal where it did not exist. This was especially true for the series with high variability. The implications of these results for forecasting practices are discussed.
The paper investigates how the decision variables of the production planning process for a network of firms in the textile-apparel industry, i.e. planning period length, material availability, the link between production orders and customer orders as regards colour mix, can affect the system's time performance, whose measurement has involved the creation of two new indicators. To adhere to reality, we studied and collected actual data from one of the most important Italian companies, the Benetton Group SpA and using these observations as a basis, a simulation model was built. Only the production planning period compression has been recognised as yielding a significant improvement in the external time performance. A relation between the external time performance and the internal time performance of the network is recognised. The cash flows associated with different lengths of the production planning period are analysed.
Although contextual or causal information has been emphasised in forecasting, few empirical studies have been conducted on this issue in controlled conditions. This study investigates the way people adjust statistical forecasts in the light of contextual/causal information. Results indicate that people appeared to reasonably incorporate extra-model causal information to make up for what the statistical time-series model lacks. As expected, the effectiveness of causal adjustment was contingent upon the reliability of the causal information. While adjustment of forecasts using causal information of low reliability did not lead to significant improvement, adjustment using highly reliable causal information produced forecasts more accurate than the best statistical models. However, people relied too heavily on their initial forecasts compared with the optimal model. Moreover, people did not seem to learn over time to modify this conservative behaviour. People also seemed to prefer statistical forecasts in favour of causal information.
Turbulent and volatile markets are becoming the norm as life cycles shorten and global economic and competitive forces create additional uncertainty. The risk attached to lengthy and slow-moving logistics “pipelines” has become unsustainable, forcing organizations to look again at how their supply chains are structured and managed. This paper suggests that the key to survival in these changed conditions is through “agility,” in particular by the creation of responsive supply chains. A distinction is drawn between the philosophies of “leanness” and “agility,” and the appropriate application of these ideas is discussed.
We report on a qualitative investigation of the influence of emotions on the decision-making of traders in four City of London investment banks, a setting where work has been predominantly theorized as dominated by rational analysis. We conclude that emotions and their regulation play a central role in traders’ decision-making. We find differences between high and low performing traders in how they engage with their intuitions, and that different strategies for emotion regulation have material consequences for trader behavior and performance. Traders deploying antecedent-focused emotional regulation strategies achieve a performance advantage over those employing primarily response-focused strategies. We argue that, in particular, response-focused approaches incur a performance penalty, in part because of the reduced opportunity to combine analysis with the use of affective cues in making intuitive judgments. We discuss the implications for our understanding of emotion and decision making, and for traders’ practice.
Consumers often browse through many products (a product context) before evaluating a particular target product. We examine the influence of four product context characteristics on happiness with a target product: pleasantness, sequence, domain match with target (i.e., whether products in the context set belong to the same category as the target), and context set size. When context and target match, pleasant and improving (compared to less pleasant and worsening) contexts induce less happiness with the target product. When there is domain mismatch, however, the results are reversed. Furthermore, the assimilation effects are significantly influenced by set size, but the contrast effects are not. While the contrast effects appear to occur by default and appear to be driven by a process of comparison, the assimilation effects appear to be driven by mood. These effects hold even when perception of domain match is manipulated via instructional framing. Copyright 2001 by the University of Chicago.
We present the results of a study designed to test several hypotheses concerning the effects of intrinsic and situational sources of personal relevance on felt involvement and on the amount of attention and comprehension effort, the focus of attention and comprehension processes, and the extent of cognitive elaboration during comprehension. Felt involvement is a motivational state that affects the extent and focus of consumers' attention and comprehension processes, and thus the specific meanings that are produced. The results of the study provide strong evidence that felt involvement plays a motivational role in consumers' attention and comprehension processes.
Recent research has identified a positivity effect in consumers' evaluations of agents, such as friends and professional critics, who provide word-of-mouth evaluations and recommendations. Specifically, agreement with an agent on previously loved alternatives is perceived as more diagnostic of the agent's suitability than agreement on previously hated alternatives. This article argues that the positivity effect arises from greater ambiguity about attribute ratings of hated versus loved alternatives. Three studies support this by showing that the effect is moderated by the number of attributes, the number of alternatives, and the revelation of an agent's attribute ratings, and is mediated by attribute ambiguity. (c) 2007 by JOURNAL OF CONSUMER RESEARCH, Inc..
The new product planning process generates an upward bias in the forecast of a product's performance. Three sources of such bias are discussed: (1) the post-decision audit bias reflects a regression-to-the-mean phenomenon since only those products that are forecasted to do well, including those with the most upward biased forecasts, are brought to market; (2) the advocacy bias reflects the tendency of product planners to champion their project by overpromising on forecasts; (3) the optimism bias results from the act of participating in planning activities. Two role-playing experiments found that persons who were more deeply involved in a planning exercise were more optimistic about the outcome of the plan than those who were less involved. A third role-playing experiment demonstrated that one reason for the optimism bias is that during the planning process the illusion of control over the environment leads the planner to change assumptions about uncontrollable events which are likely to affect the outcome.
Recent literature on nonlinear models has shown that neural networks are versatile tools for forecasting. However, the search for an ideal network structure is a complex task. Evolutionary computation is a promising global search approach for feature and model selection. In this paper, an evolutionary computation approach is proposed in searching for the ideal network structure for a forecasting system. Two years’ apparel sales data are used in the analysis. The optimized neural networks structure for the forecasting of apparel sales is developed. The performances of the models are compared with the basic fully connected neural networks and the traditional forecasting models. We find that the proposed algorithms are useful for fashion retail forecasting, and the performance of it is better than the traditional SARIMA model for products with features of low demand uncertainty and weak seasonal trends. It is applicable for fashion retailers to produce short-term retail forecasting for apparels, which share these features.