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Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools.

This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies.

This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles.

This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

This study aims to examine how financial analysts evaluate other comprehensive income (OCI) information with a focus on the information content and economic substance of OCI gain and loss.

This study conducted a 2 × 2 between-subject experiment by manipulating profitability (net profit or net loss) and OCI (OCI gain or loss). A total of 103 equity research analysts participated in the experiment.

The results show that when the company suffers a net loss, the presence of unrealized gain in OCI appears to cause concern for analysts, in that they assigned a lower valuation to the OCI gain company than the OCI loss company. However, in the cases where the company is profitable, analysts appeared to respond to the direction of OCI (i.e. gain or loss) and incorporated the directional information in their valuation judgment.

The experimental results complement prior archival research on OCI valuation. This study extends prior work on OCI’s decision usefulness, improves understanding of the impact of OCI on firm valuation and contributes to the ongoing debate about whether OCI is viewed as a performance measure. The findings indicate that the effect of OCI gains or losses is most pronounced when the company experiences a loss. During such instances, analysts may interpret a combination of net loss and OCI gain as a potential indicator of earnings management opportunities. Consequently, they may perceive it as a signal of deteriorating future financial performance.

Recent evidence suggests that financial analysts have substantial conflicts of interest when publishing their research reports. We argue that not only investors but also listed companies benefit from analyst coverage and suggest that the financial burden of such coverage be shifted entirely to those companies. This article presents a detailed evaluation of a not‐widely‐known proposal that stock exchanges ensure analyst coverage for the companies they list through a levy on their listing fees. We discuss key aspects of the regulatory framework required to ensure the independence of these financial analysts as well as some of its shortcomings. We conclude that this proposal has the potential to ensure the independence of financial analysts more efficiently than the current regulatory approach does.

A principal participant in developing computer‐based information systems is the systems analyst. A wide range of skills and knowledge requirements seems to be necessary to be a systems analyst. To date, few studies have attempted to identify the specific skills that analysts indicate are critical to their success during the systems development life‐cycle process. Identifies and investigates the characteristics of a successful systems analyst’s domain and knowledge base. Specifically, groups domain knowledge into four major categories: technical skills, systems skills, managerial skills, and business skills. From within each category, analysts indicated which skills they perceived to be of importance to perform their job functions as systems analysts successfully. The results help identify the educational and training requirements for future systems analysts that need to be in place for both academic and corporate instructional programmes.

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.

This paper aims to conduct a comparative analysis of the impact of market uncertainty on the degree of accuracy and bias of analysts' earnings forecasts versus four model-based earnings forecasts.

The study employs panel regression analysis on a sample of Egyptian listed companies from 2005 to 2022 to examine the impact of market uncertainty on the accuracy and bias of each type of earnings forecast.

The empirical analysis reveals that market uncertainty significantly affects analysts’ earnings forecast accuracy and bias, while model-based earnings forecasts are less affected. Furthermore, the Earnings Persistence and Residual Income model-based earnings were found to be superior in terms of exhibiting the least susceptibility to the impact of market uncertainty on their forecast accuracy and biasness levels, respectively.

The findings have important implications for stakeholders within the financial realm, including investors, financial analysts, corporate executives and portfolio managers. They emphasize the importance of considering market uncertainty when formulating earnings forecasts, while concurrently highlighting the potential benefits of using alternative forecasting methods.

To our knowledge, the influence of market uncertainty on analysts' earnings forecast accuracy and bias in the MENA region, particularly in the Egyptian market, remains unexplored in existing research. Additionally, this paper contributes to the existing literature by pinpointing the forecasting method, specifically distinguishing between analysts-based and model-based approaches, whose predictive quality is less adversely impacted by market uncertainty in an emerging market.

In this study, we examine the influence of a firm’s environmental, social, and governance (ESG) performance on analysts’ stock recommendations and earnings forecast accuracy in the Chinese context.

We take a textual analysis approach to analyst research reports issued between 2010 and 2019, and differentiate between two distinct analyst categories: “sustainability analysts,” which refer to those more inclined to incorporate ESG information into their analyses, and “other analysts.”

Our evidence indicates that sustainability analysts tend to be significantly more likely than others to provide positive stock recommendations and demonstrate enhanced accuracy in forecasting earnings for companies with superior ESG performance. Our additional analyses reveal that this finding is particularly prominent for analysts who graduated from institutions emphasizing the protection of the environment, those recognized as star analysts, those affiliated with ESG-oriented brokerages, and forecasts made by analysts in the later part of the sample period. Our findings further indicate that sustainability analysts exhibit a more pronounced negative response when confronted with a negative ESG event.

In general, the evidence from this study reveals the interplay between ESG factors and analyst behavior, offering valuable implications for both financial analysts and sustainable investment strategies.

This study aims to examine the association between chief executive officers’ (CEOs) characteristics and sell-side analysts’ recommendations.

This study uses a sample of firms listed on the London Stock Exchange and uses two databases, Capital IQ and BoardEx to study the above relationship. A variety of regression analyses are used in the empirical models, including ordinary least squares, fixed effect, random effect, Tobit, Logit and generalized method of moments.

The authors find that firms with CEOs who had a wider network size and firms with foreign CEOs receive favorable investment recommendations. Further, firms with CEOs who have more time to retire are more likely to receive favorable investment recommendations. However, the authors find that firms with CEOs with more qualifications receive unfavorable recommendations and female CEOs are not affecting investment recommendations.

Ultimately, this study demonstrates the importance of CEO characteristics for sell-side analysts who play an important role in the stock markets.

Analyst team forecasts are the most frequent form of earnings expectations available to investors, with teams issuing more than 70% of research reports in 2016. Prior research provides differing evidence on whether analyst teams issue higher or lower quality forecasts than individual analysts.

I use a sample of more than 17,000 hand-collected analyst reports representing 7,586 forecasts from 89 companies in three industries from 1994–2005.

I document that analyst teams benefit from an assembly bonus, and issue more accurate forecasts than individual analysts only in time periods when teams would be expected to benefit from an assembly bonus.

I outline multiple factors within the control of brokerage houses that impact teams’ relative forecast quality, such as the number of members in the team, how long the team has worked as a unit and the costliness of integrating information when forming a forecast.

Given the preponderance of analyst teams and the strength of market reaction to their forecasts, it is valuable to document factors both in the past and present likely to affect analyst teams’ relative forecast accuracy.

This study aims to investigate the relationship between corporate sustainable development goals (SDGs) disclosure and analyst forecast quality.

The study focuses on a sample of 95 Italian-listed companies preparing the mandatory non-financial declaration (NFD) according to the Global Reporting Initiative (GRI) standards over a five-year period (2017–2021), corresponding to an unbalanced sample of 438 observations. Analyst forecast quality was proxied by earnings forecast accuracy (FA) and earnings forecast dispersion (FD), built on data retrieved from the Refinitiv database. A manual content analysis was performed on NFDs to derive an SDG disclosure score (SDGD) for each sampled company.

This study provides empirical evidence suggesting that voluntary SDG disclosure matters to the capital market in that it helps enhance the information environment of companies, evidenced by improved analyst forecast quality. In particular, this study highlighted that SDG disclosure positively influences analyst FA while negatively affecting analyst FD.

This study focuses on the Italian context, which has idiosyncratic characteristics regarding the structure of the financial market, the composition of corporate ownership and experience in non-financial reporting practices.

This study indicates to corporate managers that following GRI standards may represent the right way to better integrate SDG disclosure in corporate non-financial reports and increase the relevance of such information for investors and other capital market participants.

To the best of the authors’ knowledge, this is the first study that empirically examines the association between SDG disclosure and analyst forecast quality.