Exploring negativity bias in brand beliefs and stated brand switching propensity

Negativity bias has been well studied by psychologists but limited research has been conducted on it in a marketing context. Given previous research, this exploratory study aims to examine whether there are any negativity bias effects in brand beliefs and whether there is any influence on stated brand switching propensity amongst current users of a brand. The results suggest that there is a negativity bias evident in brand image data.

Is optimistic bias influenced by control or delay?

Optimistic bias is a commonly observed but poorly explained phenomenon. Our aim was to determine whether optimistic bias varied according to the nature of the event. Two event characteristics were explored: control and delay. A sample of 100 participants aged 18–30 years was randomly selected from the local residential telephone directory. Respondents were interviewed over the telephone. The highly structured interview schedule assessed respondents' perceptions of their own risk, and the risk of an average person of their age and sex for experiencing four negative life events: developing skin cancer, being involved in a serious car accident as the driver, being involved in a serious car accident as a passenger and having to wear a hearing aid. It also assessed respondents' perceptions of control and delay for each event. Data analysis using a repeated-measures MANOVA showed that optimistic bias occurred for all four events. Optimistic bias was significantly greater for the two events high in control (skin cancer and accident as the driver) than for those low in control (accident as a passenger and hearing aid). Delay was not related to the magnitude of optimistic bias. These findings have implications for health promotion campaigns and self-protective behaviors.

Optimistic bias for negative and positive events.

Presents results of an investigation into the occurrence of optimistic bias in relation to both positive and negative events, using absolute judgements to assess perceived risk for each of six events. Participants were asked why they offered different ratings for themselves and for others. The results showed that optimistic bias is a pervasive phenomenon that occurs for both positive and negative events. It occurred for all six events when comparisons were made with an unspecified person of the same age and sex, and occurred for three of six events when comparisons were made with the same-sex best friend. Participants could provide information about their own behaviour that they felt justified their positive outlook; however, they implicitly assumed that the comparison target did not engage in the same behaviour. Concludes that risk-reduction education needs to be made personally relevant to the target audience.

Maintaining life satisfaction: the role of positive cognitive bias

Recent research into population standards of life satisfaction has revealed a remarkable level of uniformity, with the mean values for Western populations clustering at around three-quarters of the measurement scale maximum. While this seems to suggest the presence of a homeostatic mechanism for life satisfaction, the character of such a hypothetical device is uncertain. This paper proposes that well-being homeostasis is controlled by positive cognitive biases pertaining to the self. Most particular in this regard are the positive biases in relation to self-esteem, control and optimism. Past controversies in relation to this proposition are reviewed and resolved in favour of the proposed mechanism. The empirical data to support this hypothesis are discussed in the context of perceived well-being as an adaptive human attribute.

Publication bias in the economic freedom and economic growth literature

The impact of institutions on economic performance has attracted significant attention from researchers, as well as from policy reformers. A rapidly growing area in this literature is the impact of economic freedom on economic growth. The aim of this paper was to explore publication bias in this literature by means of traditional funnel plots, meta‐significance testing, as well as by bootstrapping these meta‐significance tests. When all the available estimates are combined and averaged, there seems to be evidence of a genuine and positive economic freedom – economic growth effect. However, it is also shown that the economic freedom – economic growth literature is tainted strongly with publication bias. The existence of publication bias makes it difficult to identify the magnitude of the genuine effect of economic freedom on economic growth. The paper explores the differences between aggregate and disaggregate measures of economic freedom and shows that selection effects are stronger when aggregate measures are used.

Publication bias in union-productivity research?

This paper develops and applies several meta-analytic techniques to investigate the presence of publication bias in industrial relations research, specifically in the union-productivity effects literature. Publication bias arises when statistically insignificant results are suppressed or when results satisfying prior expectations are given preference. Like most fields, research in industrial relations is vulnerable to publication bias. Unlike other fields such as economics, there is no evidence of publication bias in the union-productivity literature, as a whole. However, there are pockets of publication selection, as well as negative autoregression, confirming the controversial nature of this area of research. Meta-regression analysis reveals evidence of publication bias (or selection) among US studies.

The need for low bias algorithms in classification learning from large data sets

This paper reviews the appropriateness for application to large data sets of standard machine learning algorithms, which were mainly developed in the context of small data sets. Sampling and parallelisation have proved useful means for reducing computation time when learning from large data sets. However, such methods assume that algorithms that were designed for use with what are now considered small data sets are also fundamentally suitable for large data sets. It is plausible that optimal learning from large data sets requires a different type of algorithm to optimal learning from small data sets. This paper investigates one respect in which data set size may affect the requirements of a learning algorithm — the bias plus variance decomposition of classification error. Experiments show that learning from large data sets may be more effective when using an algorithm that places greater emphasis on bias management, rather than variance management.

Publication bias in industrial relations research

Publication bias arises when statistically non-significant results are suppressed or when only results satisfying prior expectations are published. Like most fields, research in industrial relations is vulnerable to publication bias. In this paper qualitative and quantitative techniques are used in order to detect publication bias in the union-productivity effects literature. We find no evidence of publication bias in this literature, although there does appear to be autoregression in the published results.

Convex hulls as an hypothesis language bias

Classification learning is dominated by systems which induce large numbers of small axis-orthogonal decision surfaces which biases such systems towards particular hypothesis types. However, there is reason to believe that many domains have underlying concepts which do not involve axis orthogonal surfaces. Further, the multiplicity of small decision regions mitigates against any holistic appreciation of the theories produced by these systems, notwithstanding the fact that many of the small regions are individually comprehensible. We propose the use of less strongly biased hypothesis languages which might be expected to model' concepts using a number of structures close to the number of actual structures in the domain. An instantiation of such a language, a convex hull based classifier, CHI, has been implemented to investigate modeling concepts as a small number of large geometric structures in n-dimensional space. A comparison of the number of regions induced is made against other well-known systems on a representative selection of largely or wholly continuous valued machine learning tasks. The convex hull system is shown to produce a number of induced regions about an order of magnitude less than well-known systems and very close to the number of actual concepts. This representation, as convex hulls, allows the possibility of extraction of higher level mathematical descriptions of the induced concepts, using the techniques of computational geometry.

Reducing performance bias for unbalanced text mining

In text categorization applications, class imbalance, which refers to an uneven data distribution where one class is represented by far more less instances than the others, is a commonly encountered problem. In such a situation, conventional classifiers tend to have a strong performance bias, which results in high accuracy rate on the majority class but very low rate on the minorities. An extreme strategy for unbalanced, learning is to discard the majority instances and apply one-class classification to the minority class. However, this could easily cause another type of bias, which increases the accuracy rate on minorities by sacrificing the majorities. This paper aims to investigate approaches that reduce these two types of performance bias and improve the reliability of discovered classification rules. Experimental results show that the inexact field learning method and parameter optimized one-class classifiers achieve more balanced performance than the standard approaches.

Primary sex ratio bias in an endangered cooperatively breeding bird, the Black-eared Miner, and its implications for conservation

The aim of our study was to investigate primary and adult sex ratios in the cooperatively breeding black-eared miner, Manorina melanotis. We used genetic methods to determine the sex of all birds. Observations were made to quantify differences in helping behaviour between the sexes. As in other miners, Manorina spp., non-breeding males provided most of the help in raising young. Male and female nestlings did not differ significantly in weight, suggesting that both sexes are equally costly to produce. Like other miners, the adult sex ratio in black-eared miners is male-biased (64.4%). However, unlike its congeners, the black-eared miner’s primary sex ratio was strongly biased toward females (62.5%). This suggests that females suffer higher juvenile mortality than males. Our study illustrates how understanding sex ratios is both of theoretical interest and relevant to biological conservation.

Purchase commitments : Big business bias or solution to the 'neglected diseases' dilemma?

Miniscule research resources are allocated to researching the diseases of developing countries such as malaria, tuberculosis (TB), dengue fever, river blindness, Chagas disease and leishmaniasis, and the strains of HIV prevalent in Africa. Plainly, the patent system and the commercial model of drug research fail to respond to the needs of the poor for the simple reason that the poor exercise little purchasing power. But pressures are mounting on governments and corporations to tackle the ‘neglected diseases’ calamity. An important argument in an intense global debate is that corporations would respond to the needs of developing countries if the diseases of the poor could be made profitable. This is the idea developed by Kremer and Glennerster in a crisply written book, Strong Medicine: Creating Incentives for Pharmaceutical Research on Neglected Diseases.

Investigating sample bias in student evaluations of teaching

Student evaluations of teaching have increased in importance to universities in Australia over recent years due to changes in government policy. There has been significant debate in the literature as to the validity and usefulness of such evaluations and as to whether students who respond to the evaluations are indeed representative of the student population. A potential invalidating issue is self selection in the evaluation process. In this paper, we consider student evaluations of a large first year business statistics subject that had 1073 eligible students enrolled across four campuses at the time of the evaluation. The study is based on the 373 students (34.8%) who responded to the survey, and their final results. The evaluations were open for a period of six weeks leading up to and just after the final exam. The study looks in detail at the student population identifying such attributes as gender; home campus; course of study; domestic/international; Commonwealth Supported Place/full fee paying, etc. and then mapping these results to those of the students who responded to the survey.

Publication selection bias in minimum-wage research? A meta-regression analysis

Card and Krueger's meta-analysis of the employment effects of minimum wages challenged existing theory. Unfortunately, their meta-analysis confused publication selection with the absence of a genuine empirical effect. We apply recently developed meta-analysis methods to 64 US minimum-wage studies and corroborate that Card and Krueger's findings were nevertheless correct. The minimum-wage effects literature is contaminated by publication selection bias, which we estimate to be slightly larger than the average reported minimum-wage effect. Once this publication selection is corrected, little or no evidence of a negative association between minimum wages and employment remains.