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The primary objective of this study is to consolidate the fragmented body of scholarly literature pertaining to developing entrepreneurial ecosystems, with the intent of determining prospective avenues of inquiry.

The analysis included a longitudinal distribution by category of journals with most recommendations, articles with most citations and the total number of recommendations. In addition, the authors presented a thorough explanation of the recommendations grouped by categories.

This study generated a framework that provides a comprehensive understanding of research on recommendations for the development of entrepreneurial ecosystems. The framework identified 74 recommendations in the fields of policy, support, culture, human capital, market and finance. The results indicated that the domain of recommendations for the entrepreneurial ecosystem is still in its infancy.

This study contributes to research on entrepreneurial ecosystems by focusing on recommendations for their development. The resulting framework can be used by policymakers to develop entrepreneurial ecosystems and by researchers in future studies.

This paper provides evidence that will help stock market participants interpret sell‐side analyst buy/sell recommendations. We examine whether recommendation levels (e.g. buy) correspond with traditional predictors of the underlying stock's performance, and whether recommendation revisions (e.g. an upgrade) are consistent with news analysts receive. Consistent with theory, we find that more optimistic recommendations are associated with higher mean forecast errors, forecast revisions, and forecasted earnings‐to‐price ratios. However, contrary to expectations, they also have higher market‐to‐book ratios, higher market values, and lower ratios of value to price (Lee et al. 1999). These results are probably driven by specific differences between buys and the less optimistic recommendations, as holds and sells are rarely distinguishable from each other. Our recommendation revision findings are consistent with our expectations. Upgrades have significantly larger earnings forecast errors, earnings forecast revisions, and unexpected earnings growth than do reiterations or downgrades.

Briefly reviews previous research on the value of investment advisors’ recommendations and presents a study comparing portfolio returns from analysts’ recommendations in the Wall Street Journal’s “Dartboard” contest 1990‐1996, four randomly selected shares and the Dow Jones Industrial Average. Finds the analysts’ portfolio has the highest average returns and standard deviation; and that although some individual analysts have excellent scores in the contest, this is inversely related to the number of times they participate. Suggests that they do not significantly outperform other portfolios, but that contest winners’ tips have significant effects on the market, especially for non‐listed shares. Assesses the implications of the results for the efficient market hypothesis and the share prices of firms with higher asymmetric information.

The purpose of this study is to investigate the value of analyst coverage on the covered firms.

To isolate the value impact of analyst coverage, the study focuses on a unique set of firms that receive analyst coverage for the first time after having been traded in an exchange for at least one year. Event study and ordinary least square regressions are used to test the hypotheses.

There is a significant and positive price reaction at the time of the announcement of analyst coverage initiations. However, unlike the coverage initiations around the initial public offers (IPOs), the price impact is not related to the reputation of the analyst firm, the exchange listing or whether the analyst firm is also the IPO underwriter. The sample firms do not experience significant reduction in the level of information asymmetry but experience a significant increase in liquidity. The increase in liquidity only occurs after the coverage initiations. The increase in liquidity is not explained by the increase in institutional investors' interest. Finally, the price impact around a coverage initiation is positively related to the change in liquidity.

The findings suggest that firms benefit from analyst coverage through an improvement in liquidity.

This is the first study to focus on the analysts' first‐time coverage initiations. It argues that focusing on the first‐time coverage initiations provides a better analysis of the effects of analyst activities on the firm value.

This study aims to evaluate the short-term impact of brokerage analysts’ recommendations on abnormal returns using a sample selected from the S&P BSE 100 in the Indian context. The efficient market hypothesis, specifically, its semi-strong form, is tested for “Buy” stock recommendations published in the electronic version of Business Standard. The crucial issue is, are there any abnormal returns that can be earned following a recommendation? If so, how quickly do prices incorporate the information value of these recommendations? It tests the impact of analyst recommendations on average abnormal returns (AARs) and standardized abnormal returns (SRs) to determine their statistical significance.

Using a sample of stock recommendations published in the e-version of Business Standard, the event study methodology is used to determine whether AARs and SRs are significantly different from zero for the duration of the event window by using several significance tests.

The findings indicate a marginal opportunity for profit in the short term, restricted to the event day. However, the effect does not persist, i.e. the market is efficient in its semi-strong form implying that investors cannot consistently earn abnormal returns by following analysts’ recommendations. Post the event date, the market reaction to analyst recommendations becomes positive, however, insignificant until the ninth day after the recommendation providing support to the underreaction hypothesis given by Shliefer (2000) and post-recommendation price drift documented by Womack (1996). The study contributes by using different statistical tests to determine the significance of returns.

There are important implications for traders, investors and portfolio managers. The speed with which market prices incorporate publicly available information is useful in formulating trading strategies. However, stock characteristics such as market capitalization, volatility and level of analyst coverage need to be incorporated while making investment decisions.

The study contributes by using different statistical tests to determine the significance of returns.

Replication is a primary self-correction device in science. In this paper, we have two aims: to examine how and when the results of replications are used in management and organization research and to use the results of this examination to offer guidelines for improving the self-correction process.

Study 1 analyzes co-citation patterns for 135 original-replication pairs to assess the direct impact of replications, specifically examining how often and when a replication study is co-cited with its original. In Study 2, a similar design is employed to measure the indirect impact of replications by assessing how often and when a meta-analysis that includes a replication of the original study is co-cited with the original study.

Study 1 reveals, among other things, that a huge majority (92%) of sources that cite the original study fail to co-cite a replication study, thus calling into question the impact of replications in our field. Study 2 shows that the indirect impact of replications through meta-analyses is likewise minimal. However, our analyses also show that replications published in the same journal that carried the original study and authored by teams including the authors of the original study are more likely to be co-cited, and that articles in higher-ranking journals are more likely to co-cite replications.

We use our results to formulate recommendations that would streamline the self-correction process in management research at the author-, reviewer- and journal-level. Our recommendations would create incentives to make replication attempts more common, while also increasing the likelihood that these attempts are targeted at the most relevant original studies.

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.

To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.

A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.

Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.

The purpose of this paper is to develop a journal recommender system, which compares the content similarities between a manuscript and the existing journal articles in two subject corpora (covering the social sciences and medicine). The study examines the appropriateness of three text similarity measures and the impact of numerous aspects of corpus documents on system performance.

Implemented three similarity measures one at a time on a journal recommender system with two separate journal corpora. Two distinct samples of test abstracts were classified and evaluated based on the normalized discounted cumulative gain.

The BM25 similarity measure outperforms both the cosine and unigram language similarity measures overall. The unigram language measure shows the lowest performance. The performance results are significantly different between each pair of similarity measures, while the BM25 and cosine similarity measures are moderately correlated. The cosine similarity achieves better performance for subjects with higher density of technical vocabulary and shorter corpus documents. Moreover, increasing the number of corpus journals in the domain of social sciences achieved better performance for cosine similarity and BM25.

This is the first work related to comparing the suitability of a number of string-based similarity measures with distinct corpora for journal recommender systems.

Describes Standard and Poor’s (S&P: USA) star ranking system for firm performance and presents a study of the impact of ranking changes on share prices. Outlines previous research on the effect of ranking changes and examines the share prices for 70 days before and after S&P ranking change announcements 1993‐1995 to assess abnormal returns. Explains the methodology and presents the results, which show significant changes in abnormal returns around the announcement dates, especially where the change is “in leaps rather than in steps”.

Prior literature has found that as uncertainty in a firms information environment increases, optimism increases in equity analysts’ earnings forecasts. The studies suggest an economic incentive explanation, commonly called the management‐relations hypothesis. However, there is conflicting evidence that managers would prefer pessimistic forecasts and encourage analysts to “walk‐down” their forecasts to prevent negative earnings surprises. To test these contradictory findings, this study uses an experimental setting to remove economic incentives from the analyst’s decision process and isolate the cause of observed bias in analysts’ reports. The results of the experiment show that an increase in the perceived uncertainty of the forecasting task results in significantly lower relative optimism in analysts’ earnings forecasts. This finding is consistent with a negativity hypothesis and the managementrelations hypothesis extolled in the empirical research. The findings also show that relative forecast optimism bias is positively related to the level of analysts’ buy/sell recommendations consistent with more recent findings that suggest that analysts use motivated reasoning (the tendency to process information in a manner that supports one’s goal) in their judgments of forecasted earnings and recommendations. Together, these results suggest that analysts consider and use financial information differently depending on their decision goal.