Measuring Correlation-to-Causation Exaggeration in Press Releases

Press releases have an increasingly strong influence on media coverage of health research; however, they have been found to contain seriously exaggerated claims that can misinform the public and undermine public trust in science. In this study we propose an NLP approach to identify exaggerated causal claims made in health press releases that report on observational studies, which are designed to establish correlational findings, but are often exaggerated as causal. We developed a new corpus and trained models that can identify causal claims in the main statements in a press release. By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22% of press releases made exaggerated causal claims from correlational findings in observational studies. Furthermore, universities exaggerated more often than journal publishers by a ratio of 1.5 to 1. Encouragingly, the exaggeration rate has slightly decreased over the past 10 years, despite the increase of the total number of press releases. More research is needed to understand the cause of the decreasing pattern.

Introduction
Recent studies have found that press releases issued by research institutions are a major source of exaggeration in science communication, which is later spread to mainstream media (Woloshin and Schwartz, 2002;Brechman et al., 2009;Sumner et al., 2014;Sumner et al., 2016). Exaggeration in press releases threatens to undermine public trust in science, which is a foundation for the scientific research enterprise.
The influence of press releases on science communication has increased since the 1980s. With the growing competition for reputation and funding, research institutions are increasingly using press releases as a public relations tool for research promotion (Brechman et al., 2009;Sumner et al., 2014). At the same time, independent journalism faces financial challenges and staff shortage (Galewitz, 2006;Schwitzer, 2008). Newspapers, especially small newspapers, rely heavily on press release material to write science news stories (De Semir et al., 1998;Schwitzer, 2008;Woloshin et al., 2009;Taylor et al., 2015). Prior research also shows that high quality press releases seem to improve the quality of associated news stories (Schwartz et al., 2012). Since press releases are now the dominant link between academia and news media, the information quality of press releases plays a critical role in communicating science research to the public.
The problem of exaggeration in press releases is particularly severe in health research. For example, a manual analysis of a sample of UK press releases in health research found that over a third of them contained exaggerated advice, causal claims from correlational findings, or inference of animal studies to humans (Sumner et al., 2014). The errors and inaccuracies in reporting health research findings may also misinform the public about their conditions, diagnosis, and treatments. On the other hand, medicine and health are the dominant topic in science news articles. research outside the health domain was much less covered in science news (Suleski and Ibaraki, 2010). Therefore, studying exaggeration in the health domain is particularly important for both science communication and public health.
Researchers and media watchdogs such as HealthNewsReview have been conducting manual content analysis to estimate and monitor press release quality (Smith et al., 2005;Cassels and Lexchin, 2008;Schwitzer, 2008;Sumner et al., 2014). Due to the large number of press releases and scarce funding, this labor-intensive approach is difficult to maintain. In fact, HealthNewsReview stopped operation at the end of 2018 after a nearly 13-year run. Manual content analysis is also inadequate for answering important research questions that require large-scale, real-time analyses. For example, has the exaggeration problem worsened or improved over the years? Do some institutions exaggerate more than others? Seeking answers to these research questions requires developing a computational approach to automatically analyze exaggeration in press releases.
In this study we propose an NLP approach for automatically detecting exaggerated causal claims, one of the most common types of exaggeration, by comparing claims in press releases to the corresponding claims in the original research papers. An exaggerated causal claim is defined as a causal statement in a press release with a correlational counterpart in the corresponding research paper.
Our study consists of several subtasks. First, we developed a corpus with paper-press pairs. Press releases were downloaded from EurekAlert! 1 , a nonprofit news-release distribution platform operated by the American Association for the Advancement of Science as a resource for journalists and the public. Doi links were obtained from EurekAlert! and ScienceDaily to associate the press releases with the original research papers in PubMed. The corpus includes 64,177 press releases with references to 62,317 PubMed-indexed journal papers. We then trained a classification model to identify research papers on observational studies; they are designed to establish correlational findings, but are often exaggerated as causal (Woloshin et al., 2009;Sumner et al., 2014;Zweig and DeVoto, 2018). Focusing on research papers with structured abstracts, in which the sentences in the conclusion subsection were used as main statements in research papers, we identified 15,962 observational studies. The headline and first two sentences of a press release are considered as its main statements, according to the "inverted pyramid" structure commonly used by news stories. We then paired the conclusion subsections in the research papers with the main statements in the corresponding press releases for exaggeration analysis. Some research papers were associated with multiple press releases, resulting in 16,431 paper-press pairs dated from 2008 to October 2020 in the final dataset.
Second, we developed BERT-based sentence classifiers to categorize the main statements in press releases and research papers respectively. The sentences were classified by their strength as either "direct causal", "conditional causal", "correlational", or "not claim".
Third, by applying the sentence classifiers to the 16,431 paper-press pairs, we were then able to identify the press releases that used direct causal claims to describe correlational findings in observational studies. Then we used the identified exaggeration cases to answer the following questions: (1) What is the trend of correlation-to-causation exaggeration from 2008 to 2020? (2) Did university press releases make more exaggerated claims than journal press releases?

Related Work
Institutional press releases are an important presentation of science research to the public (Autzen, 2014). A press release serves the dual purpose of science communication and public relations (Carver, 2014). Now that research and innovation have become essential drivers of economic growth and international competitiveness, research institutions are facing growing competition for research funding and talents, and are increasingly emphasizing the commercialization of university research. Such pressure results in tension between the goal of responsible science reporting and the goal of marketing when issuing press releases (Caulfield and Ogbogu, 2015;Samuel et al., 2017). A survey study on UK science press officers found that they were aware of such tension, and were trying to balance and achieve both sensation and accuracy in a delicate way (Samuel et al., 2017).
In addition, prior studies have found an increasing influence of press release on the content of science news since the 1980s (Bauer and Bucchi, 2007;Göpfert, 2007). Actually, press releases have become the dominant link between academia and news media (Sumner et al., 2014;Brechman et al., 2009). A number of studies have found that journalists rely heavily on press release materials to write science news stories, instead of acting as watchdogs by conducting independent investigations (De Semir et al., 1998;Schwitzer, 2008;Lewis et al., 2008;Woloshin et al., 2009;Taylor et al., 2015). For example, over one third of U.S health news stories were largely or solely based on press releases (Woloshin et al., 2009). Under-staffed smaller newspapers even used press releases verbatim (Ashwell, 2016). This trend is likely to continue as science news reporting positions are cut and journalists are rushed for time (Schwitzer, 2008;Galewitz, 2006).
Journalists have long been criticized for pursuing sensational stories that overstate research results (Fahnestock, 1998). However, recent studies have found that press releases may contain more exaggerations than news stories. According to (Sumner et al., 2014), 58%-86% of news stories derived from the exaggerated press releases contained similar exaggerations, compared with the exaggeration rate of 10%-18% in news articles when the press releases were not exaggerated. In the context of the high institutional expectation for generating "hype" and the shrinking of journalism as the watchdog, it has been argued that press releases have become a major source of exaggeration in science communication, e.g. (Woloshin and Schwartz, 2002;Brechman et al., 2009;Sumner et al., 2014;Sumner et al., 2016). Furthermore, university press releases were found to contain more exaggerations than journal press releases, probably because the university press officers face more pressure to generate expectations and hype (Sumner et al., 2016).
Prior studies on exaggeration in press releases have been limited to manual content analysis, which have provided valuable insights for understanding the types of exaggeration and estimating the problem severity at certain times. For example, (Sumner et al., 2014) is the first study on estimating the exaggeration rate in press releases by manually checking a sample of press releases from 20 UK univerities in 2011. However, the manual content analysis method falls short for answering important research questions that require large-scale, real-time analyses, such as longitudinal trend of exaggeration rate over time. This creates a need to develop a computational approach that can consistently measure exaggeration in a large number of press releases, and further study its trend and difference by factors such as the source of press releases (e.g. academic institutions vs. journal publishers).
Although computational modeling of exaggeration in press releases is a new research problem, it is closely related to the field of NLP applications for information credibility assessment, especially in the health domain, given the strong need for curbing misinformation in recent years. The task of credibility assessment has often been modeled as a text categorization problem. A common way to categorize credibility is true/fake binary prediction, e.g. (Abbasi et al., 2012;Dhoju et al., 2019). However, many online documents are not completely true or fake. Instead, multiple claims can be made in one document, and claims can be inaccurate in different ways. Therefore, a more precise way to assess credibility is faceted evaluation guided by established credibility frameworks with multiple quality criteria.
Some frameworks were designed for rating online health information, such as the HON principles and DISCERN. For example, the DISCERN instrument includes 16 questions, such as "are the aims clear?" and "is it balanced and unbiased?" (Charnock et al., 1999). In comparison, HealthNewsReview.org designed 10 criteria specifically for news stories. These criteria include exaggeration, missing information, etc. Expert ratings based on these criteria have been used to train NLP models. For example, an automated system was developed to assess health websites' conformity to the HON principles (Boyer and Dolamic, 2015). AutoDISCERN is an NLP tool designed to automatically assign DISCERN quality ratings to webpages using hierarchical encoder attention-based neural network (Kinkead et al., 2020). NLP models were also developed to automate ratings based on the ten HealthNewsReview criteria (Dai et al., 2020). These methods rely on human expert ratings to train credibility prediction models. In comparison, our method links press releases to the original research papers for fact checking, and we focus on exaggeration, a specific criterion used by HealthNewsReview, due to its significant impact on the quality of press releases (Sumner et al., 2014).
Our work is also built upon prior studies on identifying causal relations, e.g. (Li and Mao, 2019). A causal relation can be broadly defined as any type of cause-effect relation between events, for example Event A is the consequence of Event B (Bethard and Martin, 2008;Mirza and Tonelli, 2014). It is commonly realized by causal indicators such as "because", "as a result of", "as a consequence of" (Prasad et al., 2008). This broad definition is also adopted in the NLP tasks SemEval-2007 (Girju et al., 2007) and SemEval-2010(Hendrickx et al., 2009). However, a causal relation presented in a research finding sentence is in a more specific linguistic format, i.e. a relation between an independent variable and a dependent variable (Cofield et al., 2010), such as "smoking causes cancer". To identify causal claims in research findings, Yu et al. (2019) developed a corpus of claims with different strength levels and trained a BERT-based model to distinguish them. Compared to research papers, press releases are a different genre. Li et al. (2017) found that genre differences may result in more challenge for identifying causal claims in news articles. Based on these prior studies, we decided to construct a new corpus for identifying causal claims in press releases, which are more similar to news stories, and to develop prediction models based on BERT.

Collecting press releases
The press releases used in this study were downloaded from EurekAlert!. Since 1996 EurekAlert! has been the major platform for more than 3,000 scientific journals, research institutes, and government agencies to distribute press releases to journalists and the public. To date, EurekAlert! includes about 137,000 health and medicine research-related press releases 2 . From 2015 EurekAlert! started to add a section named related journal article onto their webpage that has a doi link to the original research paper. Currently, about 34,000 health and medicine-related press releases contain links to the original papers. However, the press releases with doi links account for less than 25% of research-related ones in EurekAlert!.
To link more press releases to the associated journal articles, especially those published before 2015, We used ScienceDaily as an additional data source. ScienceDaily is an independent website that reposts press releases. The editors manually added doi links dated back to 2008. However, we can not adopt the press releases directly from ScienceDaily because its editors sometimes modify the headline and the beginning part of a press release. Given that we need to use the original press releases posted by press officers rather than the edited ones, we borrowed the doi links from ScienceDaily to help fill the missing links between the EurekAlert! press releases and the associated papers. The basic idea is: for each EurekAlert! press release, find a best match from ScienceDaily; if the best match has a doi link, pair the EurekAlert! item with the doi link.
To implement this idea, we created a simple search engine using Elasticsearch 3 , a popular search engine that is based on the open-source Apache Lucene library. Elasticsearch supports a type of decay function that can decay relevance score depending on how far a value is from a given origin. We used this function to match publication dates between EurekAlert! and ScienceDaily press releases, which should be as close as possible. For each press release from EurekAlert! and ScienceDaily, we extracted its metadata such as publication date and headline, and computed 150 TF-IDF weighted keywords from its full text. A query was formed based on the metadata and keywords for each EurekAlert! item. Given the query, the Elasticsearch returned a ScienceDaily item with the highest relevance score. If the score is higher than a threshold, a match between EurekAlert! and ScienceDaily is found. We set the threshold to a value such that Elasticsearch achieved an accuracy of 99.5% and a recall of 90% when evaluated on the 34,000 EurekAlert! items that already have associated doi links. Using this method we found doi links for an additional 30,000 EurekAlert! press releases.
In the end, our final corpus consists of 64,177 press releases, spanning from 2008 to October 2020. They are associated with 62,317 journal publications -some collaborative work were reported by two or more press releases from different research institutions-for which we downloaded their titles and abstracts from PubMed. Find more EurekAlert press releases that have paper references through matching EurekAlert with ScienceDaily on their date, headline, and fulltext content.
About 137,000 health and medicine research-related press releases have been posted on EurekAlert since 1995. About 34,000 press releases have doi links to their original papers since 2015.
20,683 papers (referenced by 21,342 press releases) have structured abstracts that contain a subsection of conclusion. Figure 1: Collecting press releases and associated journal papers that report on observational studies.

Locating claim statements
For a press release, we assume that its main claim statements are presented in the headline or the first two sentences, here referred to as the "three-sentence opening section". According to Sumner et al. (2014), these sentences are usually the main statements, since nearly all press releases and news stories follow the "inverted pyramid" structure of stating their main claims first (Pöttker, 2003).
To verify the above assumption, we randomly sampled 100 press releases, and checked how many press releases violated the assumption. The result shows only two press releases put the main statements outside the three-sentence opening section. Hence, the assumption holds well for our data.
For an academic paper, its main claim statements or findings are usually presented in its conclusion sections. However, the conclusion sections often include not only the main findings but also statements on limitations and future work. In comparison, the abstracts of biomedical and health research papers often have a structure that usually includes subsections of introduction, method, result, and conclusion. For simplicity, we focused on those papers with a structured abstract, and extracted the conclusion subsections as the main statements. This reduces our data to 20,683 research papers and 21,342 associated press releases.

Observational study classification
Our next subtask is to identify the paper-press pairs that report on observational studies. Since PubMed has made a significant effort to identify the study designs in biomedical and health literature, we developed our identification method by expanding on PubMed's existing data.
In 2014, the PubMed staff introduced the tag observational study to their list of publication types, a metadata item for annotating study designs, such as observational studies and clinical trials 4 . To date they have manually annotated more than 60,000 research papers as observational studies. PubMed also established a category of clinical trial in 2008, and has since annotated over 861,000 papers as clinical trials.
We sampled 50,000 PubMed-annotated articles as training data, including 25,000 observational studies and 25,000 clinical trials. Since the fulltext of a article is often not available, our data are in the format of abstracts; specifically, our training data consist only of those articles with structured abstract because in this study, as we explained above, the claim statements of a research paper are identified from the conclusion subsection in the structured abstract.
LightGBM 5 , a decision tree-based gradient boosting algorithm, is chosen to train the observational study classifier based on paper titles and abstracts. Using 80% of them for training, and 20% for testing,

Conditional causal
The statement shows that one variable directly changes the other. However, the relation carries an element of doubt in it, which is normally via hedges or modalities.
increase, decrease, lead to, effect on, contribute to, result in (Cues indicating doubt: may, might, appear to, probably) Being overweight or obese during middle age may increase the risk of certain dementias.

Direct causal
The statement says that the independent variable directly alters the dependent variable.
increase, decrease, lead to, effective in, contribute to, reduce, can Traffic noise increases the risk of having a stroke.

Not claim
The statement is not for current study findings or no correlation/causation relationship is mentioned in the statement.
Many rheumatoid arthritis patients not getting recommended drugs.  we trained a LightGBM model with an F1 score of 0.95. Applying this classifier to the 20,683 research papers, we identified a total of 15,962 observational studies, associated with 16,431 press releases. The corpus construction procedure described above is also illustrated in Figure 1.

Annotating a training corpus
Following the coding schema in (Yu et al., 2019), which itself is a simplified version of the taxonomies defined in previous studies (Kilicoglu et al., 2015;Sumner et al., 2014), we annotated the following four claim types: correlational, conditional causal, direct causal, and not claim. Table 1 lists the category definitions and some common language cues used to identify the relation type for each category. Example sentences of different claim types are also shown in the table.
We randomly sampled 700 press releases from our corpus for manual annotation. They consist of 2,100 sentences in the three-sentence opening sections. Occasionally, a statement could have more than one claim type. These mixed-type statements were excluded, resulting in 2,076 sentences in the final training data set. Table 2 shows the claim type distribution of single-type sentences in the training corpus.
A sample of 200 sentences were randomly selected for conducting the inter-coder reliability test. Two annotators labeled the claim type for each sentence. The overall Cohen's Kappa agreement (Cohen, 1960) was 0.95, indicating a near-perfect inter-coder agreement (McHugh, 2012). The two annotators then annotated the rest of the data. All disagreements during the annotation were later resolved by the team through discussion.

Sentence-level Claim Type Classification
Due to the significant difference in writing styles between academic papers and press releases, we chose to train separate prediction models, one for each genre. For academic papers, we adopted the classification model developed by Yu et al. (2019) that has achieved a performance of 0.88 macro-F1 and 0.90 accuracy. For press releases, we trained a new prediction model using the press release corpus that we constructed as described above.

Prediction model
Three models were trained for main statement sentence classification: bag-of-words based Linear SVM, BERT (Devlin et al., 2018), and BioBERT (Lee et al., 2019). We hypothesized that BioBERT (Lee et al., 2019) would perform the best, since it is a domain specific language representation model that is based on BERT and further trained on large-scale biomedical corpora. BioBERT has shown to outperform BERT on three representative biomedical text mining tasks, including text classification (Lee et al., 2019).
All models were evaluated on our annotated corpus via 5-fold stratified cross-validation. As shown in Table 3(a), BioBERT achieved a macro-averaged F1 score of 0.868, outperforming BERT and Lin-earSVM, whose scores are 0.844 and 0.732 respectively.
To further improve performance, we augmented our press release data with the academic paper data (Yu et al., 2019) 6 . In our implementation, we set the weight of the data in the academic genre to half of the weight in the press release genre. As shown in

Error Analysis
Although the prediction model achieved strong performance, there were challenges in distinguishing the "not claim" sentences from others. An examination of the misclassified sentences shows that many such sentences actually talk about research background instead of research findings. We defined these sentences as belonging to the "not claim" category. However, sometimes these sentences used causal language, which may confuse the prediction model. For example, "choking is a leading cause of injury among children". There was also some confusion between the "direct causal" and the "correlational" categories. This type of error was mainly due to the causal markers in the subordinate clauses instead of the main clauses. For example, the sentence "many children are at increased risk for sleep breathing disorders that can impair their mental and physical development" describes a correlational claim but was misclassified as "direct causal" due to the cue "can impair". In our taxonomy, "can + causal cue" belongs to the category of "direct causal".
The prediction model also had some difficulty in recognizing less commonly used relation markers, especially when nouns were used to describe correlational findings in headlines, such as the phrase "coupled with" in the headline "Kidney disease coupled with heart disease common problem in elderly".

Strategy for identifying exaggeration
Our prediction model operates at sentence level, so each press release will receive three predictions for its three-sentence opening section, and each paper will receive one or more predictions depending on the number of sentences in the conclusion subsection. However, exaggeration is determined at article level, i.e. whether a press release made exaggeration or not. Hence, a strategy is needed to determine article-level exaggeration by aggregating sentence level predictions. Since the study goal is to detect the correlation-to-causation type of exaggeration, we focused on the research papers that made correlational claims only. A research paper's finding is regarded as "correlational" if at least one sentence in the conclusion subsection uses correlational language and no sentence uses direct or conditional causal language. In addition, we only considered the direct causal claims from correlational findings as exaggerations, since sometimes researchers would also speculate a conditional causal claim from a correlational finding, which often serves as the goal of future investigation.
We then designed a strategy for identifying exaggeration at article level by considering the characteristics of press release as a news genre. First, headlines serve the role of attracting readers to the story; therefore exaggeration is more likely to occur in headlines, and can be more impactful than those in content. Hence, predictions of causal claims in headlines should carry more weight than those from first and second sentences. Second, most, but not all headlines contain main claims. In our annotated corpus, 15.6% headlines were annotated as "not claim". An examination of these cases showed that the main claims were significantly shortened or paraphrased. An extremely short headline is "Brain Power". In these cases, the first and second sentences are more likely to contain the main claims. Third, a small number of first and second sentences used causal language to describe research background instead of findings. Error analysis in the previous section has shown that our prediction model has difficulty in distinguishing them from real causal findings. Therefore, when one sentence is predicted as "causal" while the other as "correlational", it may or may not be a case of exaggeration.
Considering the above concerns, we designed a conservative strategy for identifying exaggeration in all paper-press pair with the paper's finding predicted as "correlational", as illustrated in Figure 2.
Input: Three sentence predictions on headline, 1st sentence, and 2nd sentence of a press release Output: Did the press release exaggerate correlational findings as causal? (Yes or No) Process: If headline prediction is direct causal, exaggeration=Yes Else if headline prediction is "not claim", check predictions for 1st and 2nd sentences: If both predictions are "direct causal", exaggeration=Yes Else if one prediction is "direct causal" and the other "not claim", exaggeration=Yes Else exaggeration=No Else exaggeration=No Figure 2: Claim aggregation algorithm for press releases that correspond to papers identified as correlational.

Overall exaggeration in press releases
After applying the exaggeration identification strategy to our corpus of 16,431 paper-press pairs, out of 6,244 cases in which the original research papers drew correlational conclusions, we found 1,391 (22%) cases of exaggeration. This exaggeration rate is in a similar range to previous studies; however, our exaggeration identification strategies differ. Sumner et al. (2014) reported an exaggeration rate of 33% by manual analysis of 462 press releases issued by 20 leading UK universities in 2011. They used seven scales to measure the claim strength: no statement, explicit statement of no relation, correlational, ambiguous (e.g. 'linked to"), conditional causal (e.g. "may" and "might"), "can" causal, and unconditionally causal. An exaggeration was reported when the press release used stronger claims than those present in the associated research paper.
Later, the same research team re-analyzed the data using a five-scale measure by merging the correlational, ambiguous, and conditional causal categories, in order to focus on the types of exaggeration that have a more significant impact on readers' understanding. The new analysis resulted in a lower rate of 19%, since the nuanced exaggeration cases were no longer counted (Adams et al., 2017).
Compared to (Sumner et al., 2014) and (Adams et al., 2017), our schema includes four categories: the "direct causal" category equates to the combined categories of "can" causal and unconditional causal defined in (Sumner et al., 2014); the "conditional causal" category is equivalent to the category with the same name in (Sumner et al., 2014); the "correlational" category corresponds to the combined categories of "correlational" and "ambiguous" in (Sumner et al., 2014); the "not claim" is similar to the "no statement" category in (Sumner et al., 2014). We did not use the "statement of no relation" category, which refers to the statements of negative findings, because our annotations are based on correlational vs. causal language use. A negative finding on correlation would still use correlational language, and thus be annotated as correlation. Similarly, a negative finding on causal relation are annotated as causal.
In comparison, our exaggeration identification strategy is even more conservative than (Adams et al., 2017), in that we only considered the most significant cases of exaggeration, in which the research papers' conclusions used correlational language only, whereas the associated press releases used direct causal claims. We did not count minor exaggeration cases, such as those with conditional causal claims in research papers and direct causal in press releases.  Figure 3(b) shows the total number of observational studies each year, and among them the total number that used correlational language only. These numbers provide more data context for better understanding the trend. The sharp increase of observational studies in 2014 is caused by a big jump in the total number of health-related press releases in EurekAlert!. The dip in 2018 is a result of ScienceDaily starting to block robot access that year. However, ScienceDaily is less likely to be needed in the future since EurekAlert! has been adding more doi links since 2015. For 2020, we collected only 10 months of data to date.

Trend of exaggeration in press releases
More research is needed to understand the cause of the decrease in exaggeration rate. Bratton et al. (2020) hypothesized that warnings within the science community might be impactful, after observing a drastic reduction of exaggeration rate (by half) in the UK's press releases six months after the publication of (Sumner et al., 2014). However, confounding factors cannot be ruled out without further evidence. Our result did not replicate the pattern of sharp decrease in 2014 or 2015. Instead, it shows that the exaggeration rate was mildly decreased globally.

Comparing academic institutions and journal publishers
To investigate whether universities exaggerated more than journal publishers, Sumner et al. (2016) compared press releases from these two sources, and found a higher exaggeration rate in university press releases, exceeding the journals' by 33% to 21%, or a ratio of 1.6 to 1. Adams et al. (2017) re-analyzed the data in (Sumner et al., 2016) based on a simplified, 5-scale claim strength schema, and reported a similar ratio (1.5 to 1, or 19% to 13%).  Figure 4: University press releases have higher exaggeration rate than journal's.
Here we examine whether their findings still hold for our corpus. To identify the source type of a press release, we used two Eu-rekAlert! metadata items: the media contact person's email address and the name of the source institution. Specifically, if the domain name contains .edu or .ac, or the institution name contains the word "University", the source is categorized as a university. If the email address matches with a list of well-known publisher domain names such as bmj.com and plos.org, or the institution name contains the word "Publisher" or "Press", the source is categorized as a journal publisher. With this method, out of 6,244 research papers with correlational findings, we found 3,021 press releases from universities and 1,534 from journal publishers. Figure 4 shows that the exaggeration rate of university press releases is 25.3%, higher than the 16.7% of journal publishers, by a ratio of 1.5 to 1. This result is consistent with the ratios reported in (Sumner et al., 2016) and (Adams et al., 2017).

Conclusion and Future Work
In this study we examined patterns of correlation-to-causation exaggeration in press releases. We developed a new corpus with 16,431 pairs of observational study papers and associated press releases, and a prediction model for identifying cases of correlation-to-causation exaggeration in press releases. Applying the prediction model to the new corpus, we found that among all observational study papers that made correlational claims only, 22% of the associated press releases made exaggerated causal claims. Furthermore, universities made more exaggerated claims than journal publishers by a 1.5 to 1 ratio. The good news is the exaggeration rate has slightly decreased in the past ten years, despite the increase in the total number of press releases. More research is needed to understand the cause of the decreasing pattern.
This study has a few limitations which we plan to improve in future work: First, the current exaggeration detection method relies on the structured abstract of a research paper to identify its main claims from the conclusion subsection. This restriction limits our data set size since only about one third of papers have a structured abstract. In future work we will design an algorithm that can automatically identify the conclusion sentences from unstructured abstracts. Second, the current approach uses the doi links provided by EurekAlert! and ScienceDaily to link research papers and press releases; however, these manually-added links cover less than 50% of EurekAlert! press releases. In future work we would like to develop a content-based matching system to link press releases to scientific literature. Third, the current approach matches the main claims in research papers and press releases at article level, by utilizing the inverted-pyramid structure in press release. This approach works well for press releases, but might not be suitable for identifying exaggeration in other genres. For example, inverted-pyramid structure is not common in online discussion forums (e.g. Reddit (Zhou and Yu, 2020)) and social media posts. Some news articles may also cite findings from multiple scientific studies. In future work we would like to develop a sentence-level approach for pairing claims in research papers and their paraphrased versions in news and social media.

Code and Data
Our code and data are available on github: https://github.com/junwang4/correlation-to-causation-exaggeration.


The Coronavirus Effect: Building Strategic Alliances in Today’s Universities and Colleges

Due to disruptive changes such as COVID-19, universities can no longer afford to tackle these turbulent forces solely. In today’s hyercompetitive environment, companies that want to compete in the future understand that the status quo will not do. According to Accenture, 76% of business leaders surveyed agree that current business models will be “unrecognizable” in the next 5 years. Businesses that attempt to move forward against the backdrop of uncertainty and unpredictability with little or no partnerships will find it difficult to be successful. In this short communication, researchers examine how to build strategic alliances in a disruptive world marked by uncertainty and unpredictability, given the impact of COVID-19.

INTRODUCTION
Nathan did not see how rural America would survive after September 11 th . Before that event, Nathan, as part of his sales job, visited two rural communities in North Louisiana: Minden and Gibsland. Both communities struggled with their lack of a tax base and job opportunities. Many of their bright, young people had left their communities for city life in larger cities in the state and beyond.
Yet, in 2019, Nathan was astounded at these communities now. There was Minden with a slightly larger population in 2011, still struggling with its economy. And then there was Gibsland with its thriving economy.
New companies were opening. There were influxes of new residents, especially from the Northern states, coming to live there. Nathan could not resist talking to Gibsland Mayor Becky Pullen about this turnaround.
Mayor Pullen suggests a simple strategy of a collaborative effort. She explains that their community decided to pull together and pool their resources on a shared vision. We identified the strengths of our community, as well as we got business and non-profits organizations to work together on our vision of being the best catfish spot in the country.
We worked with the local colleges and K-12 systems to build curriculums around tourism and the fishing industry. Citizens in the community were excited about this strategy. We slowly started seeing the results in 2015. We have never looked back. We had successfully created a strategic alliance for our community.
In today's hyercompetitive environment, companies that want to compete in the future understand that the status quo will not do. Accenture Strategy surveyed 1,252 business leaders from diverse industries across the world to better understand the degree to which companies are capturing ecosystem opportunities.
E-ISSN: 2469-4339 merj.scholasticahq.com We discovered that companies are pursuing new business models to navigate, or even lead, disruption. When asked what they would typically do to disrupt their industry, 60% of executives said, "build ecosystems. " Nearly half have already built or are currently building an ecosystem to respond to disruption (Lyman et al., 2018).
According to Accenture, 76% of business leaders surveyed agree that current business models will be "unrecognizable" in the next 5 years (Kalia, 2019). Businesses that attempt to move forward against the backdrop of uncertainty and unpredictability with little or no partnerships will find it difficult to be successful. In this discussion, we will explore how to build strategic alliances in a disruptive world marked by uncertainty and unpredictability, given the impact of COVID-19.

THE URGENCY FOR HIGHER EDUCATION
Due to numerous problems in higher education, universities are struggling for sustainable answers especially after COVID-19.
Today, due to the COVID-19 pandemic, prospective freshman may be unable to even apply for college because of their families' financial situation from rising unemployment rates and a lack of college readiness that may result due to unplanned school closures, or community instability. This exacerbates the financial impact of declining college attendance and along with a reduction in international students, this can mean severe financial shortfalls for colleges resulting in the ability to serve their students. These factors and others make the financial future of higher education in the United States uncertain. Furthermore, Donoghue (2009) asserted that it is only possible to truly understand higher education in the United States until you appreciate the significant change in the concentration of wealth in the country during the last 30 years. During that period, a sharp socioeconomic divide has resulted in the richest 1% of Americans coming to own more private wealth than the bottom 90 percent. The top 10% of Americans now own 71% of all private wealth and is now mirrored in higher education. The top and bottom segments of U.S. higher education have grown so differently that they are no longer a single coherent social institution.

THE NEED FOR STRATEGIC ALLIANCES
Strategic alliances can help an organization through disruptive changes. What is a strategic alliance? According to the business dictionary, a strategic alliance is an "agreement for cooperation among two or more independent firms to work together toward common objectives. " Unlike in a joint venture, firms in a strategic alliance do not form a new entity to further their aims, but collaborate while remaining apart and distinct. The advantages of a strategic alliance include: (a) sharing resources and expertise, (b) new-market penetration, (c) expanded production, and (d) innovation.
Organizations are able to leverage limited resources and maximize their core competencies to spin-off innovative ideas into tangible actions. The disadvantages include loss of control and increased liabilities. Effective strategic alliances must be built on trust and transparency. In most cases, organizations must surrender their autonomous thinking to collaborative efforts. With strategic alliances, there is a large risk that the outcomes will not be desirable ones.
Having taken risks in strategic alliances can pay off. For example, one company that has continued to infuse itself with effective strategic alliances is Starbuck, the Seatlle-based business. In 1971, Starbucks started as a single coffee shop in the historic Pike Place Market. The company's mission is "to inspire and nurture the human spirit-one person, one cup, and one neighborhood at a time. " (Khor, 2013).
Today, Starbuck is the world largest coffee retailer, with over 19,000 locations in more than 60 countries, selling over 2 billion cups of coffee every year (Krikorian, 2014). In 1993, Starbucks partnered with Barnes and Noble bookstores to provide in-house coffee shops; Starbuck built an alliance with United Airlines to serve Starbucks coffee on all its flights; Starbuck made a strategic partnership with Kraft foods, resulting in Starbucks coffee being marketed in grocery stores; and finally, in 2006, Starbucks partnered with Apple to collaborate on selling music as part of its "coffee house" experience.

THE STEPS FOR STRATEGIC ALLIANCES
Building long-lasting relationships take time and effort. Therefore, good strategic alliances that last over time is not done by chance. Rosabeth Kanter, former Chief Editor of Harvard Business Review, and author of the new book Think Outside the Building: How Advanced Leaders Can Change the World One Smart Innovation at a Time, argues, "In rapidly changing environments, compatibility in values, philosophy, and goals are more important than specific features of an immediate business deal. The basis for collaboration must be more enduring, and there must be a foundation for mutual trust to help inevitable weather changes or problems" (Kanter, 2010). Additionally, Wakeam (2003) suggested five critical elements for strategic alliances, which are (a) critical to the success of a core business goal or objective; (b) critical to the development or maintenance of a core competency or other source of competitive advantage; (c) blocks a competitive threat; (d) creates or maintains strategic choices for the firm; and (e) mitigates a significant risk to the business. Regardless of the situations, all parties must understand, agree, and recognize what is expected of them.
The following key strategic alliance steps are key for success of any business:

Each organization should have a shared vision.
DeDiemar (2020) found that a clear and open line of communication among the university president, chief of staff, and chief communications officer is critical during the COVID-19 situation. These positions are involved in every strategic and operational area within the university. They establish and maintain the choreography that is so vital between risk management experts and the strategy for communicating with university audiences. According to Pepperdine University (2020), the University continues to update its pandemic emergency response plan to reflect current COVID-19 circumstances. It has a comprehensive plan in place to protect the well being of the University community and the University's Emergency Operations Committee (EOC) is working closely with University stakeholders, as well as an outside infectious disease expert; consulting with International SOS regarding international programs and travel risk assessment; and following advice from the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and the Los Angeles County Department of Public Health. Their plan addresses many possible scenarios at all University campuses with the overarching goal to best protect the community.

Each organization should bring its strengths (core competencies), but be aware of its weaknesses.
Organizations are using alliances to fill needs in their own competencies and to grow in new markets and regions in the world. In dealing with the challenge of global competition, textile companies are using strategic partnerships to maintain competitiveness. Strategic alliances are enduring interfirm co-operative arrangements, involving flows and linkages that utilize resources, or governance structures from independent organizations, for the purpose of joint accomplishment of individual organizational goals connected to the corporate mission of each sponsoring firm (Dadashian et al., 2007). Strategic alliances are an important source of resources and learning and they help grow core competencies. Therefore, managers must make decisions to develop the most important competencies necessary for success. Firms look for strategic alliances to leverage their partner organization's competencies and understand their weaknesses, which can be turned into strengths and opportunities (Dadashianet al., 2007).

Develop short-term and long-term goals.
Setting goals for both the short-and long-term alliance is a vital part of an organizational success. Computer maker Hewlwett-Packard's alliance with The Walt Disney Co. has produced very good results. HP provides major IT solutions and innovation to Disney's divisions with co-branding as a critical part of the relationship. This alliance was negotiated and structured, with a clear understanding of what each partner had to contribute and could expect to derive from the relationship with an eye on the future and how that might change. These alliance partners are both complex organizations and integrating their alliance goals only happened due to solid planning and manageable expectations regarding implementation. The success factor in any alliance and especially one with a complex organization is being able to coordinate brand exposure, joint marketing, and customer experience ("5 keys, " 2002).

Seek compatibility in values.
Partners must spend time up front to jointly define the relationship they want, the alliance generates significantly greater value than when they focus exclusively on business goals, contract terms, and formal governance structures. Schering-Plough sought ways E-ISSN: 2469-4339 merj.scholasticahq.com to establish a stronger foundation for collaboration with partners from the start of alliances and alliance generates significantly greater results (Hughes and Weiss, 2007). 5. Develop measurable outcomes and track results. Developing measureable alliance outcomes is important in a partnerhsip, but instead of focusing exclusively on the bottom line measurements of financial value, companies should work to build measurements of factors that will impact the alliance's ultimate performance, the leading indicators. Posiitve results on these interim metrics can help earn sustainable corporate commitment (Hughes and Weiss, 2007). 6. Put it in writing as a collaborative work, but don't leave it all to the attorneys. Just as good fences make good neighbors, so do contract and documents that define an alliance. According to Wharton@Work (2011), Hewlett-Packard developed 60 different tools and templates, included in its 300-page manual for guiding decision-making in alliances. The manual includes template and tools for building the foundations for an alliance, forms for evaluation of the partnership, a template outlining negotiation and the roles and responsibilities of departments and roles and for measuring alliance performance, and even an alliance-termination checklist. 7. Build on mutual respect. The U.S. Department of defense builds upon the idea of strength in numbers and working with alliance for success. One foundation of alliance expectations is that they uphold a foundation of mutual respect, responsibility, and accountability. They state they will uphold their commitments and that they expect allies and partners to contribute their fair share, including financial responsibility (Lange, 2018).

Keep communication flowing.
Communicate fully and consistently in partnerships. This will help you build bonds that will strengthen your relationship and keep it growing. "Friends, " the famous sitcom, was one of the most successful shows on television and lasted 10 seasons due to the ability of its six actors and actresses to make things work. They achieved this by by sticking together and dedicating themselves to a shared vision. One clear sign that they communicated effectively was the fact that throughout the show's entire airing, all six cast members earned the same pay per episode throughout the show's run (Ta and Dhanuwardoyo, 2016). 9. Have a champion in each organization to serve as liaisons. Someone needs to be accountable for the actions associated with this alliance. According to ASQ (2020), champions are those in organizations who translate the company's vision, mission, goals, and metrics to create an organizational deployment plan and identify individual projects in alliances. Their role is to also identify resources and remove roadblocks and liase between the organizations.

THE PATH FORWARD
As we move forward after the aftermath of COVID-19, there will be an onslaught of crises across the globe. Most large firms with the financial power will opt to solo through tragedies alone. Some will succeed in the short-term, while many will fail as history shows.
In this article, we demonstrated how to build strategic alliances in a disruptive world marked by uncertainty and unpredictability. While the coronavirus poses a challenge for the world, dealing with crises alone is not wise. Organizations that can be collaborative on a shared vision create a culture that can spin off innovation and can ensure sustainable successes. Find the right organizations for effective strategic alliances. Pray that it is not too late.


THE EFFECTS OF VISUAL MERCHANDISING AND PRICE SENSITIVITY ON IMPULSE PURCHASE BEHAVIOUR AMONG YOUNG APPAREL SHOPPERS IN BANGLADESH

Article History Received: 17 February 2020 Revised: 19 March 2020 Accepted: 23 April 2020 Published: 11 May 2020

terms of quality (Park, Kim, Funches, & Foxx, 2012). Consumers now-a-days would like to compare price with other retailers and purchase decisions are made based on the quality and quantity provided by retailers.
Visual merchandising can also influence impulse purchases. Impulse purchasing is a rapid, persuasive, hedonically complex action in which the pace of an impulsive decision-making process excludes consideration of alternative knowledge and selection (Beatty & Ferrell, 1998). Impulse purchase occurs when no prior purchase intentions are associated and it could either be defined as no specific products to be bought or no specific purchase task to be filled. On the other hand, price-sensitive consumers are prone to purchase impulse.
The young consumer group has gained tremendous attention from marketers as they have increased purchasing power; their attitude towards money has also changed with relatively easy access to credit cards.
However, impulse purchase behavior among youngsters is particularly relevant to examine because they now comprise a sizeable segment of a target market that is rising in numbers as well as financial strength (Mangleburg, Doney, & Bristol, 2004). Therefore, young consumers are considered as an important cohort in consumer behavior which is worth to be studied.
The apparel stores in Bangladesh are on the rise. Significant numbers of local boutique houses and fashion houses launched their goods in Bangladesh by targeting young college and university customers and professionals.
However, there is limited literature available which clearly identifies the purchasing behavior of this specific group. Therefore, the main objective of this study is to investigate the factors that affect impulse purchase behavior among young apparel shoppers in Bangladesh. To meet the special objective of this study three variables such as visual merchandising (VM), price sensitivity (PS) and shopping enjoyment (SE) are employed to see the effects on impulse purchase behavior. The remaining paper exhibits five sections include literature review, research methodology, results and findings, limitation and future research directions.

Theoretical Framework
The Stimulus organism and response model (SOR) was first introduced by Mehrabian and Russell (1974) in environmental psychology. The SOR model has been widely exercised in marketing studies and the literature of consumer behavior (Buckley, 1991;Donovan, Rossiter, Marcoolyn, & Nesdale, 1994;Wakefield & Blodgett, 1996).
In the SOR model, stimuli are believed to have an effect on an individual's internal state (Eroglu, Machleit, & Davis, 2001). Organism is characterized as an internal process or state mediated between the stimulus and the individual's final response. Response is final outcome which determine consumers approach or avoidance behavior. Chang, Eckman, and Yan (2011) applied SOR model to investigate ambient design and social characteristics of retail stores on impulse purchase behavior through consumers' positive emotional responses. Sultan, Jan, Basit, and Rafiq (2018) studied impulse purchase behavior by applying SOR model where window display, promotional activities and store environment were the key stimuli to determine impulsive nature of a consumer through an individual's positive emotion. However, many researchers (Ahmad, Ali, Malik, Humayun, & Ahmad, 2019;Bharathi & Sudha, 2017;Graa & Dani, 2012;Hashmi, Attiq, & Rasheed, 2019) have studied consumers' stimuli, emotional responses and impulse purchase by adapting SOR model.

Impulse Purchase Behavior
Impulse purchase behavior described as sudden, unplanned and thoughtless purchase behavior which takes place due to the characteristics of products and services. It is a purchase behavior where consumers think emotionally rather than rationally (Rook, 1987;Rook & Fisher, 1995). However, Mowen and Minor (1998) signifies impulse purchase as an uncontrollable reaction, which increases the desire to obtain and possess, and encourages consumers to have impulse purchase behavior. The basic impulse buying paradigm started with Stern (1962) research in which purchasing activity was categorized into planned, unplanned, and impulse. Planned buying takes longer time because it involves with information searching and rational thinking. In contrast, unplanned buying does not require advance planning. Impulse buying differs from unplanned buying in terms of rapid decisionmaking. The impulse buying behavior involves a sudden, intense, and irresistible purchasing urge (Stern, 1962).
According to Zeb (2016) impulse purchase decision are sudden and spontaneous where no prior purchase decisions made to purchase specific products. Consumers' impulsive purchase occurred without prior information search and evaluation of alternative. However, Muruganantham and Bhakat (2013) developed a framework for impulse purchase behavior where external stimuli described as the most challenging implication on impulse purchase.
According to Kang (2013) impulse purchasers are categorized into two types. The first type of impulse purchasers makes the decision with no information search. Their buying behavior desired to fulfill certain affective needs. The second type of impulse purchasers makes the decision right after information search. Their buying behavior are still impulse but through all the decision-making steps to enhance the purchase benefits. It is vital for retailers to understand consumers mind in order to sustain the competitive market as impulse purchase behavior has become an important issue (Badgaiyan & Verma, 2014).

Shopping Enjoyment
Shopping enjoyment is described as a buyer's characteristic in which they tend to find that shopping is enjoyable and allow them to experience greater shopping pleasure (Goyal & Mittal, 2007). Shopping enjoyment describes as the pleasure ones perceived in the shopping process (Beatty & Ferrell, 1998). Higher level of shopping enjoyment leads to greater impact on impulse purchase (Saad & Metawie, 2015). Shopping enjoyment is also considered to be an important intrinsic factor as enjoyment comes from the shopping process within shoppers themselves because of their shopping activity (Bong, 2016).
Extensive research was carried out to examine the relationship between mood and emotion towards impulse purchasing behavior. For example, Pradhan (2016) found mood to have significant relationship with consumers' impulse behavior. However, Maulana and Novalia (2019) describes emotion as a crucial factor in making decision by consumers. Emotion can be divided into two dimensions namely: positive and negative. Consumers' positive emotions can be expressed by positive feelings for instance pleasure, enjoyment and satisfaction and these have positively related to their impulse purchase behavior (Suhud & Herstanti, 2017). Because higher level of shopping enjoyment tendency give rise to higher levels of positive affect (Mohan, Sivakumaran, & Sharma, 2013). Consumers who enjoy shopping are more opted to purchase impulsively. With the above-mentioned literature, the following hypothesis is formed: H1: Shopping enjoyment has positive effect on impulse purchase behavior.

Visual Merchandising
Extensive research has been carried out to portray visual merchandising on impulse purchase behavior. Visual merchandising consists of few factors such as store layout, color, lighting, store design and cleanliness (Kouchekian & Gharibpoor, 2012). Bhatti and Latif (2014) found from the study that signifies strong influence of visual merchandising on impulse purchase behavior. Kaur (2013) found window display as one of the most important elements that increase the stimulus so that consumers can enter into the store. She also found that the 85% consumers try apparel that is found on display. Mehta and Chugan (2013) found to have significant relationship between visual merchandising (i.e. window display, floor merchandising and promotional signage) and impulse purchase behavior. Setyawati, Sumarsono, and Praditya (2018) studied visual merchandising on impulse purchase behavior and found that visual merchandising had positive and significant relationship with positive emotion. Maulana and Novalia (2019) define consumers' positive emotions as pleasure, enjoyment and satisfaction. However, in visual salient factor for pleasant feelings (Crowley, 1993). Saad and Metawie (2015) examined impulse buying behavior where shop enjoyment mediate the relationship between environmental factors and impulse purchase behavior. Therefore, following hypotheses are formed: H2a: Visual merchandising has positive effect on impulse purchase behavior.
H2b: Visual merchandising has positive effect on shopping enjoyment.

Price Sensitivity
Price sensitivity can be defined as the degree to which consumers react to price changes and price levels (Goldsmith, Kim, Flynn, & Kim, 2005). Consumers purchase behavior are significantly and positively influenced by price and consequently sales and profit of the firm are affected by price too (Han, Gupta, & Lehmann, 2001). Mihić and Kursan (2017) examined consumers' personal factors on shopping enjoyment and result found price sensitivity to have significant and positive influence on enjoyment. Although some authors denoted that higher level of shopping enjoyment tendency among consumers are less price sensitive (Saad & Metawie, 2015) and typically less cost-conscious (Kang. & Park-Poaps, 2010). Previously, price-sensitive consumers were regarded to be rational and logical as they seek utilitarian values through purchasing activities (Schindler, 1989;Tauber, 1972) but researchers further identified that price sensitive consumers seek hedonic shopping value too (Arnold & Reynolds, 2003;Jin & Kim, 2003). Consumer's enjoyable shopping experiences are based on hedonic benefits acquired from bargaining because one of the possible shopping enjoyment factors is bargain hunting (Cox, Cox, & Anderson, 2005).
H3b: Price sensitivity has positive effect on shopping enjoyment.

Proposed Model
Considering all above-mentioned discussion, the following hypothesised model was proposed for the study.

METHODOLOGY
Data was collected via online and face-to-face distribution. Survey questionnaire was developed in English language and adopted from several past studies (Dash & Akshaya, 2016;Irani & Hanzaee, 2011;Mihić & Kursan, 2017) then modified to ensure the validity of those constructs. The questionnaire had two sections: First section was requested for demographic information. Second, survey questionnaire which was measured using five-point Likert scale from (1) strongly disagree to (5) strongly agree. A pre-test test was conducted by distributing 20 sets of questionnaires to the university students with the aim of getting comments and feedback to improve the clarity. These data were collected by utilizing throughout the month of March, 2020. The target respondents for this study were young adults. Thus, study was conducted using convenience sampling method where students from the university with minimum shopping experience were a part of this survey. A total of 305 replies were received from the respondents living at Dhaka city in Bangladesh. After the data screening and cleaning processes, 258 responses were found as completed for further analysis.

Descriptive Analysis
The profile of the respondents is shown in Table 1. Out of 258 responses, 56.2% and 43.8% were males and females, respectively. Most respondents were between 22 and 30 years old (68.6%), followed by the 31 to 40 age group (22.88%) and below 21age group (8.52%). Majority are pursuing undergraduate degree (62.02%) followed by postgraduate degree (28.68%) and diploma (9.3%). Respondents then were asked about shopping experience and found that major respondents with three years of shopping experience (33.72%). Therefore, respondents were asked which apparel stores they visit mostly. Among top ten apparel stores in Bangladesh, Cats Eye was the most visited stores among respondents (16.28%) followed by Richman (12.79%), Yellow (12.02%) and others (10.85%).

Data Analysis
While descriptive data was analyzed by using SPSS version 23.0, Smart PLS software version 3.0 in order to validate the measurement model and structural model. To evaluate the research model, two-step approach was followed (Chin, 2010). First, the assessment of the measurement model associated with the loading of the indicator.
Second, it applies to the structural model which is related to the measurement of path coefficient.

Measurement Model Evaluation
Convergent and discriminant validity were conducted to assess the measurement model. To evaluate convergent validity, the value of both composite reliability and Cronbach's alpha are suggested to be higher than 0.7 (Chin., 1998). Additionally, average variance extracted (AVE) values should be greater than 0.5 as recommended by Hair, Black, Babin, Anderson, and Tatham (2006). To illustrate discriminant validity, it refers the degree to which factors are uncorrelated and distinct (Hair, Ringle, & Sarstedt, 2013). Measures of different constructs should not correlate highly with each other (Fornell & Larcker, 1981). Table 2 shows the good discriminant validity as it illustrates the square AVE of each factor are larger than any of its' correlations with the other factors.

Structural Model Evaluation
The structural model reflects the relationship between the constructs or the latent variables that were hypothesized in this study. Both path coefficients and R 2 score were determined by the structural model analysis.
According to Hair., Sarstedt, Ringle, and Gudergan (2017) R 2 values of 0.75, 0.50 and 0.25 which describes substantial, moderate and weak levels of predictive accuracy. The t-value>1.96 is significant at p<0.05 and t-value>2.58 is significant at p<0.01 (Hair et al., 2017). So, Figure 2 shows the R 2 value in this model is 0.432 (43.2%) for shopping enjoyment (SE) and 0.455 (45.5%) for impulse purchase behavior (IPB). Figure 2 and Table 3 illustrates the path coefficient (β), t-statistics and p-value of each hypothesis. Based on the analysis all the hypotheses are found to be significant thus supported. H1 (β= 0.309, t= 3.300) indicates the path between shopping enjoyment and impulse purchase behavior; describing the positive and significant relationship among SE and IPB of apparel goods. H2a (β= 0.272, t= 2.897) shows the path between visual merchandising and impulse purchase behavior; representing positive relationship between VM and IPB among young apparel shoppers.

Hypotheses Testing
H2b (β= 0.517, t= 6.782) demonstrates the positive relationship between visual merchandising and shopping enjoyment of apparel. H3a (β= 0.235, t= 2.928) explains that price sensitivity has positive and significant influence on impulse purchase behavior among young apparel shoppers in Bangladesh. Lastly, H3b (β= 0.236, t= 2.919) demonstrates that, positive and significant relationship is associated between price sensitivity and shopping enjoyment.

DISCUSSION
The objective of this study was to understand the effects of visual merchandising and price sensitivity on impulse purchase behavior among young apparel shoppers in Bangladesh. The study also examined the mediating role of shopping enjoyment. The findings of this study support SOR theory. In this study, shopping enjoyment were found to be significantly and positively affecting impulse purchase behavior. Thus, finding is consistent with the prior study of Saad and Metawie (2015). Visual merchandising found to have significant and positive influence on impulse purchase behavior. Young consumers perceive that store environment, mannequin display and wide variety of apparel selection influence them to attempt unplanned purchase. Thus, study is in line with the previous findings (i.e. (Bhatti & Latif, 2014;Dash & Akshaya, 2016)). Results found price sensitivity to have significant and positive relationship with impulse purchase behavior. Price sensitive consumers are bargain hunter and looking for discounts but they don't hesitate to pay more when it comes to new apparel. Thus, the study is consistent with previous studies (i.e. (Mihić & Kursan, 2017;Nagadeepa et al., 2015)). Rather than being a rational thinker, consumers seek hedonic gratification such as consumer feel enjoyed and pleased by shopping environment and price offered by apparel stores. This study also examined the mediating effects and found that shopping enjoyment mediate the relationship between visual merchandising, price sensitivity and impulse purchase behavior.

Mediation Effect Analysis
Two mediating effects in this study were examined. Young apparel shoppers' shopping enjoyment has a mediating effect on impulse purchase behavior and the visual merchandising. Second, shopping enjoyment has a mediating effect on price sensitivity and impulse purchase behavior. In this analysis, the results show that the values of this Sobel test are all higher than 1.96 (Sobel, 1982) and with 95% confidence intervals, 5000 simulation were bootstrapped and found no 0 straddle in between (Efron & Tibshirani, 1993). The study indicates that shopping enjoyment has significant impacts on visual merchandising, price sensitivity and impulse purchase behavior.

Contribution
Current study discusses the theoretical and managerial implications which arise from the present research.
Firstly, the findings of this study imply the reliability and validity of the stimulus organism and response (SOR) model in measuring the young shoppers' impulse purchase behavior of appeals through perceived enjoyment.
Additionally, this study contributes to the external stimuli of SOR model by supporting it in the Bangladeshi context. The study also supports the conceptual SOR framework and provides the evidence from several literatures for the relationships between visual merchandising, price sensitivity, perceived enjoyment and impulse purchase behavior among young appeal shoppers.
The findings of this study provide a few managerial implications for the Bangladeshi appeal outlets. Firstly, the result of this study would help the management team to better understand about the best policy to be undertaken in building strong relationships with consumer especially young adult groups. Furthermore, appeal outlets could provide suitable products with flexible price where consumers are allowed to have discount price upon request. In addition, visual merchandising is also an important factor that allows apparel outlets for gaining competitive advantages and customer retention. Visual merchandising and price sensitivity are two external stimuli found from this study that affects consumers' emotional states and impulse purchase behavior of apparel products.

Limitation and Recommendation for Further Research
This study holds few limitations. First, convenience sampling method was employed to collect the data which may not best representation of young apparel shoppers in Bangladesh context. Because of the time constraint, researcher particularly focused on students obtaining degrees at various institutions in Dhaka city. Second, there is a high possibility of receiving false or even undesirable answers from participants. Third, current study employed visual merchandising and price sensitivity to examine impulse purchase behavior through shopping enjoyment.
Though visual merchandising had widely exercised in the impulsive purchase studies, price sensitivity comparatively less concentrated and few more factors are to be applied in order to study impulsive purchase of apparel products.
However, it is further recommended that, the study should be carried out whole Bangladesh including rural and urban area. Therefore, study should also focus on jobholders or salaried worker because the impulse purchase behavior among young students and employees may vary based on the situational factors such as time, money availability. However, further study could apply different factors such as service quality, reliability and trust to investigate impulsive behavior especially on apparel purchase.
Funding: This study received no specific financial support. Competing Interests: The authors declare that they have no competing interests.


Dynamics of Corporate Dividend Policy under Hyperinflation and Dollarization: A Quantile Regression Approach

Purpose: Zimbabwe experienced hyperinflation (2000-2008) followed by dollarization from 2009 onwards which had implications on dividend policy. In this context, this study isolates the main determinants and examines their behaviour across the distribution of dividend policy. Design/methodology/approach: The study employs quantile regression analysis and a sample of 30 firms listed on the Zimbabwe Stock Exchange (ZSE), covering the period 2000 to 2016. The fixed effects (FE) analysis is applied as a base model. Finding(s): The most robust determinants are ownership structure, earnings per share (EPS) and taxation. In our context, results are more informative, than those based on FE analysis by showing the change in the impact of each explanatory variable across the distribution. EPS has a positive and significant impact on dividend policy throughout the distribution in both sample periods. Its effect increases in magnitude as firms move from low to high quantiles. The other variables are useful in explaining dividend policy at selected points of the distribution. Thus, there is clear heterogeneity in the determinants of dividend policy. Research limitations/implications: The study shows the importance of developing dividend policy by focusing on the position of the firm on the distribution. Dividend policy should be developed in view of the earnings potential of the firm, ownership concentration and perceived changes in fiscal policy. A well-designed policy should have a differentiated approach to influencing corporate dividends. Originality/value: This study enhances our understanding of dividend policy in unique markets. It confirms the applicability of dividend relevance theories. Furthermore, It shows that quantile analysis provides more reliable estimates than those obtained using standard panel data models.

Introduction
Zimbabwe's economy experiences structural changes between 1997 and 2019. This is triggered by both political and economic factors. In 1998, the government embarks on a reform to compulsorily acquire land from the white minority and give it to the landless black majority (Mandizha, 2014). Unbudgeted gratuities are paid to war veterans and the government supports the war in the Democratic Republic of Congo. The international community does not support such decisions and multilateral institutions like the International Monetary Fund (IMF), Africa Development Bank and the World Bank (WB) withdraw financial support. In response, the government prints money to finance its activities resulting in hyperinflation from 2000 to 2008. The effects include: the worsening of the exchange rate, loss of import cover, fall in export revenue, negative gross domestic product (GDP) growth, deterioration in the balance of payments position and fall in production levels and a rise in unemployment. By the end of 2008 the official inflation rate reaches 231 million percent (Makochekanwa, 2007, Mandizha, 2014, Kararach Kadenge and Guvheya, 2010. During this period of high inflation, some firms pay dividends to retain investors. More so, the stock market provides a hedge against inflation and trading activities remain high (Jagongo and Mutswenje, 2014). The money market instruments perform badly due to high inflation and investors shift their portfolios to stock market related securities (Njanike, Katsuro, and Mudzura, 2009). Electronic copy available at: https://ssrn.com/abstract=3881056 However, inflation falls to single digits in February 2009 following the adoption of a multicurrency regime (Kanyenze, Chitambara andTyson, 2017, Zhou andZvoushe, 2012). The economy is partially dollarized, and the United States Dollar, South African Rand and Botswana Pula become legal tender. The speculative activities cease, and firms shift their focus to generating production profits (Sikwila, 2013, Njanike et al, 2009). The Reserve Bank of Zimbabwe (RBZ) loses its lender of last resort function which limits the availability of liquidity in the market. The market is still unstable which affects the level of dividends distributed by firms to shareholders as well. Hence formulating corporate dividend policy is still important for firm managers under dollarization period.
Previous studies (Edwards andMagendzo, 2001, Nor, 2012) on hyperinflation and dollarization fail to discuss these structural changes in view of corporate dividend policy. Understanding the determinants and dynamics of dividend policy in these periods adds to the current debate in corporate finance. Literature lacks studies that directly examine dividend dynamics in this context. This is despite the possibility that the predictive power of main dividend theories may be lost under these circumstances. Previous discussions are mainly based on standard panel data models which provide conclusions based on mean values of explanatory variables. They fail to fully exploit the critical information at different points of the distribution of dividend policy. In view of this, this study extends the current literature by employing quantile regression techniques to obtain useful information due to the presence of heterogeneity in the firms' dividend policy. The effect of each explanatory variable is sensitive to the position of the firm on the distribution of dividend policy. This is tested and confirmed using data for Zimbabwe firms.
The study offers some new insights by showing that there is clear heterogeneity in the determinants of dividend policy. The most robust determinants are ownership structure, earnings per share and taxation. In our context, results are more informative, than those based on fixed effects analysis by showing the change in the impact of each explanatory variable across the distribution. Earnings per share has a positive and significant impact on dividend policy throughout the distribution in both sample periods. Its effect increases in magnitude as firms move from low to high quantiles. The other variables are useful in explaining dividend policy at selected points of the distribution. Thus, quantile analysis provides more reliable estimates than those provided by standard panel data models. This study enhances our understanding of dividend policy in unique markets. It confirms the applicability of dividend relevance theories.
The rest of the study is organized as follows: section two summarizes the key theories underpinning this study and the main determinants of dividend policy, section three discusses the methodology applied in this study, section four discusses main results and section five concludes and provides policy implications.

Literature Review
This study is done under imperfect market conditions as such dividend policy affect firm value (Gordon, 1963, Lintner, 1962. It is underpinned by dividend relevance theories which are summarized as follows: the bird in hand theory, by Lintner and Gordon, shows that investors prefer current dividends which have low risk. They tend to discount future cashflows with a lower rate which increases the value of the firm. A firm that does not pay dividends experiences low firm value (Gordon, 1963, Lintner, 1962; the agency costs theory shows that the payment of dividends reduce the agency problem by removing excess cash which might be misused by managers (Easterbrook, 1984) and the clientele theory (Allen at el, 2000, Seida, 2002 shows that the payment of dividend attracts institutional investors due to low taxation. Investors' preference of dividend payments is determined by the level of taxation. Those in higher tax brackets prefer shares with low or no dividends while those in low tax brackets prefer cash dividends.

Determinants of Dividend Policy Past Dividends
Thus far, literature is clear on the impact of previous dividend payouts on current dividend policy. Studies (Edmund, 2018, Mirbagherijam, 2014, Tran and Nguyen, 2014 have shown that past dividend payments are a good predictor of future dividend policies. Investors can use the payment of dividend as a signal for the future prospects of the firm. Firm managers are reluctant to reduce dividends payments even during inflationary periods. They continue to make disbursements to mimic good prospects for current and potential investors.

Financial Leverage
Highly leveraged firms (LEV) pay less dividends due to high debt service costs (Hosain, 2016, Edmund, 2018, Fliers, 2017. Such firms are exposed to the risk of bankruptcy. More dividends can be paid where a firm relies on other sources of cash flows (Nguyen et al, 2013, Ahmad andJavid, 2009). Payment of dividends may differ according to debt composition. Firms may be willing to acquire more debt to finance dividend payouts which acts as a signaling device to shareholders. However, some studies (Rizqia andSumiati, 2013, Alzomaia andAl-Khadhiri, 2013) argue that financial leverage has no effect on dividend policy.

Investment Outlays
Previous studies (Al-Najjar andBelghitar, 2011, Ahmed andJavid, 2009) show that high investment expenditure (INV) reduces the likelihood of paying dividends. Firms with more investment opportunities may source external funding where access to financial markets is easy and they can still maintain high dividend payouts. Bildik, Fatemi, and Fooladi (2015) argue that large firms can still pay dividends in the absence of credible growth opportunities.
Their study confirms the positive relationship between investment decisions and dividend policy. This is consistent with theoretical propositions (Adediran andAlade, 2013, Lahiri andChakraborty, 2014) that firms can make investment and dividend decisions concurrently.

Earnings per Share
Literature confirms the linkages between earnings per share and dividend policy. For previous studies (Basse and Reddeman, 2011, Adediran and Alade, 2013, Bassey, Asinya, and Elizabeth, 2014, King'wara, 2015 show that high earnings per share (EPS) guarantee the payment of more dividends. Again, firms may not necessarily make huge dividend disbursements as they seek to retain funds for future use. Ahmed and Javid (2009) argue that though dividend policy is dependent on earnings per share (EPS) and past year's dividends, it is more sensitive to the former than the latter.

Managerial Ownership
The agency theory shows that managerial ownership is related to dividend policy. Studies (Björn andLantz, 2016, Ahmed andJavid, 2009) show that more dividends are paid where managers seek to reward themselves using free cash flows. On the other hand, high managerial ownership (OWN1) may cause managers to postpone the payment of dividends and invest so as to increase the firm's future income generating capacity (Kania and Bacon, 2005, Mirza and Azfa, 2010, Bushra and Mirza, 2015. Low dividends in firms with high inside ownership are explained by the desire by management to increase the expected value of their stock options which they receive as compensation. However, studies like Arshad et al (2013) and Hosain (2016) show that in the case of Pakistan firms inside ownership and dividend policy have no relationship

Institutional Ownership
The presence of institutional shareholders brings discipline among managers who are deterred from overinvesting a firm's excess funds. Past studies (Bozec et al, 2010, Björn andLantz, 2016) show that institutional ownership (OWN5) promotes the payment of dividends where a firm is at the high quantile of its growth opportunities. In this case the firm may be having more excess cashflows which are useful for rewarding owners. On the other hand, institutional shareholders may restrict payment of dividends and advocate for more money to be spent on growth opportunities (Kania andBacon, 2005, Bushra andMirza, 2015). However, Mossadak, Fontaine, and Khemakhem (2016) argue that institutional ownership has no effect on dividend policy.

Taxation
The taxation policy of the firm affects investor choices. Institutional investors and the elderly prefer dividend paying stocks since tax on dividends is low. Taxation (TP) reduces funds available for payment of dividends (Berzins, Bohren and Stacescu, 2017, Morck and Yeung, 2005). On the other hand, previous studies (Atia, 2017, Amidu andAbor, 2010) show that taxation has a positive relationship with dividend payout where firm managers have chosen a certain dividend policy, desire to use dividends as an investor retention strategy or have access to other financing alternatives. Chetty and Saez (2010) argue that corporate taxation does not distort the ability of a firm to pay more dividends in contrast to the agency cost theory. It has an insignificant effect on dividends (Gul et al, 2012, Khan, Jehan andShah, 2017).

Firm Size
Large sized firms (SIZE2) pay more dividends as they are likely to be financially stable (Arshad et al, 2013, Michaely andRoberts, 2012). Similarly, Bildik et al (2015) show that large firms can still pay dividends in the absence of credible growth opportunities, but they have to be profitable (Kowalewski, Stetsyuk and Talavera, 2007). On the other hand, King'wara (2015) argues that large firms could have taken more debt to finance their current levels of growth. This reduces the ability to make dividend payments in the short term.

Inflation and Money Supply
Inflation (INFN) and money supply (MSP) are useful in controlling for hyperinflation and dollarization respectively as firms design their dividend policy. Basse and Reddeman (2011) argue that firms pay more dividends even when they are faced with high inflation. They can still rely on high nominal earnings. Firms are expected to have reduced dividends payout under hyperinflation (Elly andHellen, 2013, Edmund, 2018) and more payouts during dollarization period. On the contrary, Mirbagherijam (2014) argues that dividends signal bad future prospects. Chronic high inflation results in a fall in the earnings as well as dividends. Pandey and Bhat (2004) show that money supply has a positive effect on dividend policy. On the contrary, Akyildirim et al (2013) supports the proposition that high money supply reduces the payment of dividends. Furthermore, Mambo (2012) argues that monetary policy activities have no effect on dividend policy.

Model Specification
Dividend policy (PR) is measured using dividend per share. The choice is determined by its usage in literature, diagnostic tests and giving of better results consistent with Zimbabwean context. The dependent variable, PR, is specified as function of the firm and macroeconomic variables and their expected signs are guided by literature. The generalized model is stated as: x as represented by the firm and macro variables.
Quantile regression model (Koenker and Basset, 1978) is used to analyse the effects of each explanatory variable on corporate dividend policy in different quantiles. It helps in exploring, accurately, the determinants of dividend policy. This approach helps in understanding the effects of each variable by looking at the sign of the coefficient, size and level of significance across the distribution. It gives better results than those given by OLS models (Fattouh, Harris and Scaramozzino, 2008). The design matrix bootstrap method is used to estimate standard errors for coefficients (Buchinsky, 1998). Confidence intervals are constructed using the percentile method (Koenker and Hallock, 2001). Estimations are done using nine quantiles: 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80 and 0.90. The model estimated is specified as follows: The study also employs the fixed effects model to provide base results for comparison with quantile regression. Robust standard errors are employed. Potential endogeneity is tested by checking the robustness of estimates. This is done by removing or adding explanatory variables to see if results remain unchanged (See results in Tables 5 and 6 in the appendix). A sample of 30 non-financial firms, is used, that are listed on the ZSE, giving a total of 510 firm years. Annual data on firm characteristics and macroeconomic variables is extracted from financial statements of firms and WB (2017)  Electronic copy available at: https://ssrn.com/abstract=3881056 *The Table contains definitions of main variables as they are applied in this study.

Diagnostic Tests and Descriptive Statistics
The study employs Levin, Lin and Chu (LLC) and Im, Pesaran and Shin (IPS) to test for unit root at 5% level of significance and results show that all variables are stationary at levels. The study considers multicollinearity using Pearson correlation matrix. Correlation coefficients are mostly less than 0.5 which implies that there is limited multicollinearity between any pair of variables. Thus, all the variables could be used in the same model without giving spurious results. All the variables (Table 2) are positively and highly skewed except for the measure of investment decisions. Variables in the analysis are leptokurtic with a measure of kurtosis higher than 3. The study shows that the average dividend per share is US$0.025. The average firm growth rate is 11.9% and firms are not highly geared since the average level is slightly above 50%. The average inflation rate is 136190.1% during the period of analysis. Management hold about 8.6% of shares while institutional shareholders hold 74.4% of shares on average. The average of money supply is 55% of GDP for Zimbabwe. Earnings per share are US$0.041 while the tax paid is around 12% on average. The level of investment fell, on average, by about 16%, during the period of analysis.  Table 3 and Figure 1 show results during hyperinflation period while Table 4 and Figure 2 show results under dollarization. The most robust determinants of corporate dividend policy, in both periods, are ownership structure variables, earnings per share and taxation. Findings show that investment decisions, firm growth (Edmund, 2018), leverage (Rizqia and Sumiati, 2013  -5.03E-10 -9.97e-11 -6.25e-10 -4.09e-10 -7.37e-10 -6.69e-10 -7.73e-10 -1.94e-09 -2.05e-09 -1.73e-09 OWN1 0.0505*** 0.0248*** 0.0249*** 0.0216*** 0.0219*** 0.0223*** 0.0152*** 0.0252*** 0.0393*** 0.0491*** SIZE2 -0.0012 -3.71e-05 -8.32e-05 -6.91e-05 -0.  Electronic copy available at: https://ssrn.com/abstract=3881056  The study shows that insider ownership has a positive effect on dividend policy which is consistent with the results based on the FE model. However, quantile regression shows clear variations in the magnitude of coefficients as firms move from low to high quantiles. Under hyperinflation, there is evidence of managerial entrenchment throughout the distribution. Under dollarization, insider ownership is important from 30 th quantile onwards. In both periods, the effect of inside ownership increases as firms move to higher levels of the distribution of dividend policy. Findings are consistent with past studies (Mossadak, Fontaine andKhemakhem, 2016, Gowri andSaravanan, 2016) which support strong managerial entrenchment as firms move towards higher levels of dividend policy.

Quantile Regression Analysis
Institutional ownership has a negative effect in both structural periods. Firms with high institutional ownership can successfully reduce the payment of dividends. This is consistent with previous studies (Reyna, 2017, Yusof andIsmail, 2016). This study shows that their monitoring role is effective for firms with high dividend policy or payout ratios. The significance of institutional ownership is in the 90 th quantile and starts from 60 th quantile under hyperinflation and dollarization respectively.
Under hyperinflation, expansionary monetary policy reduces the payment of dividend at the higher level of the distribution of dividend policy. This is consistent with Akyildirim et al, (2013) who suggest that high money supply is inflationary which further erodes cashflows meant for dividend payouts. Under dollarization, the effect of money supply is insignificant. Consistent with Mohsin and Ashraf (2011), the results show that expansionary monetary policy has no effect on dividend policy. Firms have alternative sources of finance to improve dividend payouts.
The positive impact of earnings per share differs across the conditional distribution of firms' dividend policy in both periods. The magnitude of the coefficient increases as firms move from lower to higher quantiles. This shows the persistent impact of earnings per share on dividend payout as predicted by theory. Thus, at higher levels of dividend policy, firms with higher earnings pay more dividends as supported by previous studies (King'wara, 2015, Bassey et al, 2014. Taxation has a positive impact on dividend payout at lower quantiles, under hyperinflation upto the 60 th quantile. The positive effect is consistent with past studies (Atia, 2017, Amidu andAbor, 2010). This may indicate their desire to maintain a certain level of dividend policy and more so their ability to source funding elsewhere. At high levels of dividend policy, profitable firms are not considering the effects of taxation when deciding to pay or not to pay dividends. However, under dollarization, taxation has a negative effect within the 90 th quantile. In our context, this shows that firms face the real burden of taxation at higher quantiles considering that the economy is using a stable currency. Previous studies (Arif andAkbarshah, 2013, Chuang et al, 2018) show that higher taxation adversely affects remuneration for shareholders.
The study shows that firm size explains dividend policy under hyperinflation and it is significant in the 90 th quantile. Thus, at higher levels of dividend policy, larger firms are likely to increase their dividend payout ratio and move towards the upper levels. This confirms the argument by Arif and Akbarshah (2013) that large firms have more access to debts market and hence experience fewer external constraints. Dividend policy is not sensitive to firm size under dollarization.
Investment decisions are important under dollarization and have a positive effect between the 40 th and 70 th quantiles only. The positive effect is consistent with Bildik et al (2015). Thus, policy makers' focus should be on firms that are within this range since they are likely to withstand any financial constraints within the economy and continue to pay dividends. Such firms can access alternative sources of investment funds like debt and retained profits. Our results suggest that a study like Lestari (2018), showing that investment has no effect on dividend policy may have analysed behavior of firms at the lowest parts of the distribution.
Consistent with past studies (Bushra and Mirza, 2015, Cristea andCristea, 2017), this study shows that firm growth has a negative effect on dividend policy under dollarization. Thus, firms at the high level of the distribution reduce payment of dividend to free up funds for taking up new opportunities. More so, the study shows that leverage has a positive effect on dividend policy from the 70 th quantile. This is consistent with past studies (Thirumagal andVasantha, 2017, Gowri andSaravanan, 2016) which show that firms are willing to acquire more debt to finance dividend payouts which acts as a signaling device to shareholders. This behavior is expected where firms are not afraid of the risk of bankruptcy as they are exposed to more debt.

Conclusion
This study contributes to corporate finance literature by examining dynamics of dividend policy under unique market conditions. It identifies the major determinants and examines their contribution at different positions of the firm's dividend distribution. By using quantile regression analysis, the study brings useful information which is critical for policy making. The study confirms the importance of dividend relevance theories by showing role played by institutional shareholders, managerial share ownership and taxation. The results are more informative than those from previous studies which are based on OLS methodology. They indicate the points, on the distribution, at which key variables affect dividend policy. For example, under hyperinflation firm size, money supply and institutional ownership have a positive, negative and negative effect, respectively, on dividend policy at 90 th quantile. Earnings per share and inside ownership are positive and significant throughout the distribution while taxation has a positive effect at lower quantiles. Under dollarization, the behavior of institutional ownership and firm growth suggests a non-linear relationship with dividend policy since the sign of the parameter changes from positive to negative. Insider ownership is significant from the 30 th quantile onwards. Other variables like leverage, taxation and investment decisions are important at specific points on the distribution. These results provide a firm foundation for understanding dividend policy in markets under unique conditions. They show the importance of developing policies by focusing on the position of the firm on the distribution of dividend policy. A dividend policy that focus on reducing informational inefficiencies would be desirable for the Zimbabwean market.  239 *** significant at 1%; ** significant at 5%; *significant at 10% The models are estimated using FE and with robust standard errors. The first model contains all explanatory variables which are removed one at a time to check for robustness. The results remain fairly stable for all models. 0.0425** 0.0367** 0.0413** 0.0458*** 0.0352** 0.0217*** 0.0351** 0.0453** FG -0.0021*** -0.0023*** -0.0019*** -0.0022*** -0.0021*** -0.0024*** -0.0008** FLEV6 -0.0013** -0.0016** -0.0014** -0.0014* -0.0010** -0.0013* -0.0017** INFLN -5.03E-10 -5.18E-10 -6.59E-10 -5.24E-10 -5.02E-10 -4.33E-10 -3.65E-10 OWN1 0.0505*** 0.0526*** 0.0504*** 0.0506*** 0.0487*** 0.0509*** 0.0529*** SIZE2 -0.