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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 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.

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.

This study aims to identify the changes in different selling situations/styles during and post-COVID scenarios.

To attain the above-mentioned objective, a qualitative study drawn upon the principles of the theories-in-use approach is conducted. The data were collected through 23 in-depth semistructured interviews, conducted with professional salespeople working at various levels in different industries. The data thus generated was analyzed through open, axial and selective coding, which resulted in three broad categories of changes in professional selling.

The findings of the study suggest that though sales jobs are perceived to be similar in nature, but there are differences in how various selling jobs are being performed. The same is the case with the effect of the pandemic on sales jobs. The authors found that every selling style faced a different challenge due to the pandemic and so is the case for the salespeople engaged in the respective selling practice.

To the best of the authors’ knowledge, this is the first research of its kind that has focused on the differences in various selling styles. Though the recent academic literature on personal selling does manifest the effect of the pandemic. But, in doing so, these studies have presented “personal selling” as an overarching concept encompassing all types of selling and have failed to differentiate between the various nuances of personal selling which include trade selling, technical selling, new-business selling and missionary selling.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

The purpose of the paper is twofold: first, to observe the relationship between sale to list ratio (SLR)/ for-sale inventory (FSI)/ sale count nowcast (SCN) and real/nominal housing value, and second, to produce a handbook of empirical evidence that can serve as a foundation for future research on this topic.

This paper broadly compiles empirical evidence, using three of the most common causality tests in the field of housing economics. The analysis methods include lagged Pearson correlation test, Granger causality test and cointegration test.

Causal relationships were observed between SLR/FSI/SCN and both nominal and real housing values. SLR and SCN showed positive long-term correlations with housing value, whereas FSI had a negative correlation. Adjusting the housing value with the Consumer Price Index (CPI) to derive real housing values could potentially alter the direction of the causal relationships. It is crucial to distinguish the long-term relationship from temporal dynamics, as FSI displayed a positive immediate impulse–response relationship with nominal housing price despite the negative long-term correlation.

SLR/FSI/SCN are housing market parameters that have only recently begun to be documented and have seen little use in research. So far, housing market research has revolved around traditional macroeconomic indicators such as unemployment rate. To the best of the authors’ knowledge, this study is one of the first studies that introduce these three parameters into housing market research.

This paper aims to investigate associations between firm resources and reliance on entrepreneurial marketing (EM) channels among agrofood ventures. It accounts for agropreneur gender and racial/ethnic status in the context of marketing channel portfolio composition. The authors examine the established assumption that resource limitations drive EM and whether socially disadvantaged status of agropreneurs is associated with marketing strategy beyond standard resourcing measures.

Using 2015 Local Foods Marketing Practices Survey data, the authors apply linear regression to investigate differences in the use of EM channels, accounting for resources, social status and other factors.

Limited-resource ventures rely more on consumer-oriented channels that require EM practices. Socially disadvantaged entrepreneurs favor these channels, even when accounting for resources. Notably, ventures headed by men of color rely more on the most customer-centric local foods marketing channel.

Future research should investigate how social and human capital influences the use of EM.

Entrepreneurial support policy and practice for agropreneurs should be cautious about the “double-burden” folk theorem of intersectional disadvantage and review how to best direct resources on EM to groups most likely to benefit.

This paper uses a unique, restricted, nation-wide, federal data set to examine relationships between resource endowments, social status and the composition of agrofood enterprises’ marketing channel portfolios. To the best of the authors’ knowledge, it is the first to include racial- and ethnic-minority status of agropreneurs and to account for intersectionality with gender.

The purpose of this study is to analyze the influence of the firms' external environment on their export intensity. More specifically, it assesses whether domestic market characteristics such as domestic demand and general export environment related to tradability across borders affect firms' export intensity.

The authors use a sample of 29,266 firms from nine European countries, for the period of 2010–2016, and test several estimation methods (random effects models, Tobit models, and Heckman's selection models).

Results show that external factors such as domestic demand and ease of trade across borders are important determinants of firms' export intensity. Moreover, results reveal that firm's internal characteristics such as age, size and productivity also play an import role.

Studies about the influence of the firms' external environment on firms' export intensity are scarce because most of them are confined to a single country context. In this way, the present study contributes to the body of knowledge on the influence that external factors can have on firms' export performance by analyzing firms from nine European countries, which has important policy implications.

This paper attempts to investigate the approaches used for implementing total quality management (TQM) practices and study its impact on ABC India Limited (Name changed), a tyre manufacturing industry for realizing leadership in the Indian market. The other objectives are to explore the key issues faced by the company before TQM and study the benefits achieved after TQM implementation.

A conceptual case study research methodology has been utilized in this paper. The industry was facing some major business challenges such as low productivity, financial issues and low market presence since 1996. To overcome these issues, the top management of the industry adopted TQM as a business improvement tool and started its implementation in 2008.

A good number of improvement initiatives under four TQM approaches such as management approach, development approach, improvement approach and involvement approach were performed systematically and strategically. As a result of the successful implementation of TQM initiatives, the industry received the most prestigious quality award namely Deming Prize in 2017.

With the implementation of the TQM philosophy in the industry, it gained the top market position, the customer satisfaction score increased from 75 to 81, market presence increased from 7.2% to 26.3% and the percentage participation of employees increased by 73.5% within a period from 2009 to 2016.

The novelty of this research is to consider a case-based approach that will be helpful in promoting the utilization of the TQM philosophy in other industries for gaining competitive advantages.

The purpose of this paper is to describe and analyze the impacts that the recent proliferation of private and overlapping standards is having in the trade of agricultural products from developing countries.

In a first stage industry experts in the Chilean fresh fruit trading industry were interviewed to understand the perceived impact that private standards are imposing in the industry. These interviews allowed to identify the market case study, table grapes, the landscape of private standards and their prevalence in different countries. In a second stage, a gravity trade model for trade in table grapes was estimated, with a focus on the more stringent countries identified by experts in the first stage.

We show evidence that the proliferation of private standards required by large European retailers has diverted trade away from more stringent countries that require more certifications (and into less stringent European markets). We also show that the costs of these additional certifications have been shared by trading partners, via an increase in direct sales, as opposed to consignment (the traditional marketing mode), which is associated with higher prices.

The impacts of the recent proliferation of private and overlapping standards in international trade needs to be better understood both by the legal and economic literature. While the use of private standards has been growing since the 1990s, there is a recent trend of large European retailers imposing their own and overlapping standards that needs to be better understood to inform policy.

While there is a thin literature on the impact of private standards on trade, most of this has studied the effects of the now de facto mandatory GlobalGAP certification. However, there is a recent trend by large European retailers of demanding their own private certifications, together with other already existing overlapping private standards. This study describes and analyzes the impacts of this rather new trend.