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The purpose of this study is to investigate whether, in the context of making a go/no-go decision regarding a failing new product, the use of a stopping rule and/or a new decision-maker would reduce the escalation of commitment (EOC).

This study uses a classroom experiment design and uses logistic regression and a chi-square test to analyze its data.

The findings show that both responsible and non-responsible participants are more likely to perceive the negative performance of a new product as less negative and believe that the goal for the product can be reached when there is a stopping rule and proximal negative feedback indicates a level of performance below but very close to it than when there is no stopping rule. Therefore, they are more likely to continue the failing new product, whether they are responsible for the product or not. However, non-responsible decision-makers are more likely than their responsible counterparts to discontinue the failing new product in the absence of a stopping rule.

This paper extends the theory of EOC by showing that the use of a stopping rule and/or a new decision-maker may not reduce EOC.

This paper provides useful guidelines for managers on how to reduce EOC.

The originality and value of this paper are found in the investigation of a situation in which the use of a stopping rule and/or a new decision-maker may not reduce the EOC.

The current study implies self-quantification to consumer behavior and investigates how self-quantification influences consumers' persistence intentions, then indicates the underlying mechanism and examines the role of sharing in social media context.

The hypotheses are tested by three experimental studies. In study 1, the authors test the main effect of self-quantification on persistence intentions and demonstrate goal specificity as the mediator. In study 2 and 3, the authors explore sharing and sharing audience as the moderators.

The current research demonstrates that quantifying personal performance increases consumers' persistence intentions because self-quantification makes the focal goal more specific. However, sharing self-quantification performance with others has a negative effect on the relationship between self-quantification and persistence intentions. Building on goal conflict theory, sharing diverts consumers' focus away from the goal itself and toward others' evaluation and judgment, which makes the focal goal more ambiguous. Moreover, the negative effect depends on who is the sharing audience. When consumers share with close others who hold a similar goal with them, the negative effect of sharing is dramatically reversed.

The present research offers guidelines to managers about how to design self-tracking system to increase user's engagement and how to establish social community on social media platform to motivate users' goal pursuit.

This study contributes to the research of self-quantification from consumer behavior perspective. It also enriches interactive marketing literature by broadening self-quantification relevant research from social interaction dimension.

This study aims to examine consumers’ responses to two types of loyalty programs: coalition and single-firm programs. This study explains the mechanism underlying the link between this program structure and consumers’ program evaluation by incorporating the type of firm offering the program (i.e. a more hedonic or a more utilitarian disposition), the type of rewards (i.e. presence/absence of experiential rewards) and consumers’ reactance.

Two online experiments were employed to test the proposed framework.

Consumers commonly preferred a coalition program to a single-firm program. This preference for the coalition program was strengthened when a utilitarian-dominant firm offered the program. Additionally, consumers evaluated the program lower when a utilitarian-dominant firm provided experiential rewards. Furthermore, situational reactance toward the program mediated the effect of the program structure on the program evaluation.

This study’s findings suggest that firms should consider whether the value consumers predominantly perceive from the firms is utilitarian or hedonic when launching coalition programs. Consumers may not be pleased by the coalition programs offered by hedonic-dominant firms as much as those provided by utilitarian-dominant firms. Moreover, this study’s results help design reward options. Consumers may not well evaluate the inclusion of experiential rewards when offered by utilitarian-dominant firms. For utilitarian-dominant firms, rewards requiring less time and effort may be more suitable.

This research significantly contributes to the literature on loyalty programs. This study showed that consumers viewed single-firm and coalition programs differently and elucidated the mechanism behind the response.

This study aims to empirically investigate how susceptibility to social influence in new product adoption varies with one’s structural location in a social network.

The social network data were collected based on a sociometric network survey with 589 undergraduate students. Social network analysis and ordinary least squares regression analyses were used to test the hypotheses.

This study finds that consumers with high degree centrality (i.e. hubs) who have a large number of connections to others and consumers with high betweenness centrality (i.e. bridges) who connect otherwise distant groups in social networks are both less sensitive to informational influence from others. More importantly, the authors find evidence that consumers with moderate levels of degree/betweenness centrality are more susceptible to normative influence and status competition than those with low or high degree/betweenness centrality. The inverse-U patterns in the above relations are consistent with middle-status conformity and anxiety.

This research complements social influence and new product diffusion research by documenting important contingencies (i.e. network locations) in consumer susceptibility to different types of social influence from a social network perspective.

The findings will assist marketers to leverage social influence by activating relevant social ties with effective messages in their network marketing strategies.

This research provides a better understanding of the mechanisms driving susceptibility to social influence in new product diffusion.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

The aim of this paper is to investigate the onset of bio‐thermal convection in a shallow fluid layer; the convection is thus driven by the combined effect of swimming of oxytactic microorganisms and inclined temperature gradient.

Linear stability analysis of the basic state is performed; the numerical problem is solved using the collocation method.

The most interesting outcome of this analysis is the correlation between three Rayleigh numbers, two traditional, “thermal” Rayleigh numbers, which are associated with the vertical and horizontal temperature gradients in the fluid layer, and the bioconvection Rayleigh number, which is associated with the density variation induced by the upswimming of microorganisms.

Further research should address the application of weakly nonlinear analysis to this problem.

The increase of the horizontal thermal Rayleigh number stabilizes the basic flow. The effect of increasing the horizontal thermal Rayleigh number is to distort the basic temperature profile away from the linear one. The increase of the Schmidt number stabilizes the basic flow. The increase of the Prandtl number first causes the bioconvection Rayleigh number to decrease and then to increase.

To the best of the authors’ knowledge, this is the first research dealing with the effect of inclined temperature gradient on the stability of bioconvection.

The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.

The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression, random forests, gradient boosting and neural networks) and two updating methods (moving and extending windows). They use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017.

The gradient boosting method yields the greatest accuracy, while the robust method provides the least volatile predictions. There is a clear trade-off across methods depending on whether the goal is to improve accuracy or avoid volatility. The choice between moving and extending windows has only a modest effect on the results.

This paper compares a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but which have not been considered in prior research. The techniques include robust regression, which has not previously been used in this context. The data updating allows for analysis of the volatility in addition to the accuracy of predictions. The results should prove useful in improving hedonic models used by property tax assessors, mortgage underwriters, valuation firms and regulatory authorities.

This study aims to examine the impact of social media (SM) on the interactivity among teachers, among students and between students and teachers for sustainable academic performance and for achieving sustainable development (SD) in higher educational institutes. This study also investigates the moderating impact of knowledge creators (KNC) and knowledge seekers (KNS) on the collaborative learning environment using SM.

With the help of Vroom’s expectancy motivation theory (1964), collaborative learning theory and other theories, a theoretical model has been developed. This theoretical model has been tested using the structural equation modeling technique with 375 participants taken from different educational institutes. The respondent-–participants were both teachers and students.

The study found that SM plays a significant role in achieving SD al goals and enhances collaborative learning activities among teachers and students to improve academic performance to achieve SD in higher educational institutes. Also, the study highlighted that both “knowledge creators” and “knowledge seekers” have effective moderating impact on the linkage between “intention to use SM for knowledge sharing” and “collaborative learning using social media” to achieve SD al goals.

With the inputs from expectancy-instrumentality-valance theory and collaborative learning theory and existing literature, a theoretical model has been developed conceptually. Later, the model was successfully validated with an overall high explanatory power (72%) of this model. As the sample of the study do not represent a global representation of the population, thus the findings cannot be generalizable.

This study has provided valuable inputs to the SD practitioners and educational policymakers to formulate appropriate policies that enable SD al activities in higher educational institutes. This study also provides food for thought to the policymakers about the role of KNC and KNS toward the collaborative learning environment in achieving SD al goals in higher educational institutes.

The theoretical model developed in this study is unique. This study shows how both “knowledge creator” and “knowledge seeker” play a significant role toward collaborative learning and helps to achieve SD in higher learning institutes and improves their performance. The overall predictive power of the model is 72%, which also shows the effectiveness and uniqueness of the proposed model.

This paper is concerned with reducing the barriers imposed on the flexibility and responsiveness of automated manufacturing systems by current control software technology. The general question that is addressed by this research is, how can insights be gained from the manufacturing system that can assist the control system in meeting this goal of responsive behavior? The approach that is taken is to investigate appropriate means of integrating available manufacturing system information into the control system. A framework for integrating status information to control an automated assembly line is introduced that combines both transient information (e.g. station queue length) and steady‐state information (e.g. station gradient estimates) obtained by observing the operation of the assembly line. It is shown that, through the use of an appropriately designed fuzzy‐logic controller (FLC), the combined information results in flow time performance superior to that achieved using the transient or steady‐state measures individually.