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While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.

The research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.

The study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.

Although only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.

This research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.

This study aims to investigate the extent and pattern of the influence of one of the most important decision-making tools in the context of social commerce. This study demonstrates how much customer testimonials (including verified purchases and ordinary users) can influence the sales rank of experience and search goods.

The data were collected by text mining and performing a content analysis on the XML documents of Web pages and processing them. For search goods, 22,311 opinions were recorded regarding 95 mobile phones. Additionally, for experience goods, 67,817 opinions were recorded regarding 162 books in the Amazon online store. The data were analyzed by functional regression method in longitudinal data analysis.

In terms of importance, the opinions and recommendations of verified purchases had a 60% greater impact on the sales rank of experience goods than the opinions and recommendations of ordinary users. In search goods, the opinions of ordinary users had a greater impact than the opinions of verified purchases. The historical effect of the opinions of ordinary users at the end of the review period on sales rank was evident, while the historical effect of the verified purchase viewpoints during the review period had a nonlinear curve. The results showed that it was necessary to increase the volume of comments to increase their reliability in experience goods.

Measuring the effect of customer testimonials helps the managers of retail websites design algorithms and online suggestion systems, thereby improving the sales of their products by providing information desired by customers.

Individuals can be a source of information and influence the buying decision process of others by sharing their experiences. This issue helps reduce the purchase risk and explains the importance of interaction and sharing the customer’s experience.

Analyzing the impact of customer testimonials by separating verified purchases and ordinary users is one of the advantages of this study. The quantitative estimation of the impact of recommendations and the provision of a model of their historical effect is one of the approaches not addressed in similar studies.

The COVID-19 pandemic has disrupted numerous businesses and upended the lives and shopping habits of most consumers. This study aims to examine the price sensitivity and the efficacy of online reviews during a pandemic crisis.

This study borrowed from the regulatory focus theory and heuristic-systematic model and used a unique longitudinal sample of 320,000 product/day observations from the jeans category, collected before and during the pandemic, to investigate how consumers’ online shopping behavior changed during the pandemic.

The results of several hierarchical linear modeling analyses revealed that during the pandemic consumers were less price-sensitive and more willing to pay price premiums for jeans. Furthermore, consumers were more (less) likely to be influenced by online review volume than valence. Finally, the results of a post-hoc study highlighted the potential role of regulatory focus as the underlying psychological mechanism explaining the effect of the pandemic.

This research contributes to the digital marketing and regulatory-focus literatures by showing that the COVID-19 pandemic may have triggered a prevention-focus state of mind and prompted consumers to place a greater value on online review volume than valence when shopping online (for jeans). Furthermore, this paper contributes to the pricing literature by offering further evidence that the pandemic may have inclined consumers to be less price-sensitive.

This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.

The authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.

The relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.

The proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.

This research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

This study aims to investigate the impact of mandatory audit partner rotation (MAPR) on Chinese listed firms’ insider trading, as well as the moderating effects of firm characteristics on this impact. The economic mechanism behind this impact is also explored.

This study conducts a regression analysis on firms associated with mandatory and voluntary audit partner rotation based on 2009–2019 firm data and examines whether corporate insiders of these two types of firms increase their share sales within 12 months before their financial statements are submitted to a new rotated auditor.

Client firms’ corporate insiders increase their share sales within 12 months before their financial statements are submitted to a new mandatory rotated auditor. In addition, such an association is less pronounced for client firms that changed from Big 4 auditors to those with higher financial constraints. This is more pronounced for client firms with higher information asymmetry. The economic mechanism of the finding is that is the MAPR implementation reduces earnings management activities from client firms. Moreover, client firms’ buy-and-hold stock returns decline in the first year after MAPR.

This study should assist investors, corporate shareholders and Chinese policymakers. Investors can be well protected through the adoption of MAPR because upcoming auditors enhance the audit quality of clients by restraining managers’ manipulation of reported earnings and declining firms’ insider trading afterwards. Investors, Chinese policymakers and corporate shareholders should pay more attention to firms’ financial report quality, auditor selection, financial situation, corporate governance and the information environment. Explicitly, firms with less transparent financial report quality, non-big 4 auditors and fewer financial constraints are more likely to be involved in insider trading.

To the best of the authors’ knowledge, none of the extant studies have examined the impact of MAPR on insider sales. This study extends the research on the effect of the audit process on firm market performance by investigating the impact of audit partner rotation policy on insider trading behaviors.

This paper empirically demonstrates the major organizational events of a rolling forecasting process and the roles of controllers therein. In particular, this study aims to investigate how the understanding of a “realistic forecast” is translated and questioned by various mediators in the rolling forecasting process and how it affects the quality of planning as the ultimate accuracy of forecasts is seen as important.

This study follows an actor-network theory (ANT) approach and maps the key points of translation in the rolling forecasting process by inspecting the roles of mediators. This qualitative case study is based on interviews with controllers and managers involved in the forecasting process in a single manufacturing company.

The paper identified two episodes of translation in the forecasting process, in which the forecast partially stabilized to create room for managerial discussion and debate. The abilities of controllers to infiltrate various functional groups and calculative practices appeared to be one way to control the accuracy of forecasting, although this was built on a façade of neutrality.

Prior literature identifies the aims of interactive planning processes as being to improve the quality of planning. The authors apply ANT to better understand the nature of mediators in constructing an entity called a “realistic rolling forecast”.

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

This research investigates the effect of imposing a tax on sugar-sweetened beverages (SSBs) on the likelihood of purchasing SSBs. We design and test an experimental framework that examines this and the effects of providing an explanation about the presence of an SSB tax and information about the negative health effects of consuming SSBs. Consistent with Elbel, Taksler, Mijanovich, Abrams, and Dixon (2013) and Taylor, Kaplan, Villas-Boas, and Jung (2019), we find that imposing a tax, in addition to increasing the conspicuousness of the tax by explaining the presence of a tax (and in some cases, the negative health effects) reduces the likelihood of purchasing an SSB anywhere from 8.39% to 18.15%. We contribute to the public health and tax policy literature by testing consumer choice in a controlled experimental setting and considering the effect of individual differences on the choice to purchase SSBs. Imposing a tax on SSBs may be an effective tool for decreasing SSB consumption that is made more effective when the tax is conspicuous.

Managers engage in marketing efforts to boost sales and in setting marketing budgets based on current or historical sales. Past studies have overlooked the reciprocal relationship between marketing spending and sales. This study aims to examine the nature of the relationship between sales and marketing expenses in the B2B market.

Five hypotheses on the relationship between sales and marketing expenditures were framed. A total of 30 of India’s dyeing firms provided data on revenues, sales (in units) and marketing expenditures over time. The structural vector auto-regressive model and the vector error correction model were fitted to the data.

The results show that marketing expenses and sales are related bidirectionally in a sequential way. Furthermore, sales drive the long-term equilibrium relationship to a greater extent than marketing expenditures.

The findings of this study should assist managers in predicting sales and marketing budgets simultaneously and devising precise marketing strategies and tactics.

Using econometric models in data-driven research is not a frequent practice in marketing. This study adds value to the body of marketing literature by advancing the theory of the relationship between sales and marketing spending using real-world data and econometric models in the B2B sector.

Many studies have shown that the intensity and the number of hurricanes are likely to increase. This paper aims to look at the immediate effects of hurricanes on the time on the market, share of houses sold and percentage of houses with price cuts in the housing market using the metropolitan statistical area-level data in Florida.

Using a difference-in-difference method, the authors estimate the impact that a hurricane has on the housing markets.

The authors find that a hurricane has a positive and significant effect on the time on the market. A hurricane leads to a delay of the sale of a typical house in Florida by five days. The authors test for within-year seasonality and show that these effects change with seasonality of the housing market. Markets with seasonal housing prices tend to be affected more by hurricanes than those where housing prices are not seasonal. The authors also show that effects of a hurricane are transient and fade away in a few months. The results remain significant as the hurricane intensity changes.

This is the first study to look at the short-term effects of the hurricanes and how their effects vary based on seasonality of the markets.