Crackers are one of the food commodities favored by Indonesian people. Most of the cracker business actors are MSMEs. The main problem faced by many MSMEs is the lack of business knowledge, so they ...are often inferior to manufacturing businesses. The same thing was experienced by MSME crackers in Krembung District, Sidoarjo which was managed by Mr. Abdul Ghofur. In this service activity, the University of Surabaya Informatics Engineering has an assistance program through technology, to expand the market share that can be reached and help MSME financial management. The programs carried out are the development of an online market place information system that is integrated with the financial system. The activity begins with program socialization and problem analysis. After that, it is followed by application development, training, mentoring, as well as observation and evaluation. Finally, reflection is carried out for the continuation of further activities. The duration of this community service activity is eight months, including one month of assistance to ensure that MSME personnel are able to use the system properly. During the mentoring process, business processes at MSMEs can run more smoothly, where sales transactions are more centralized in the system, stock records are better, and owners can get financial information more accurately and easily. Therefore, it can be concluded that this community service activity went well and helped Mr. Abdul Ghofur's MSME business processes. Service activities will be developed by providing supplies in the form of digital marketing so that MSMEs can reach new customers to access this application.
This paper utilizes market-level data to explore the relative performance of individual companies amongst defined competitors. We show the potential of using consumer clickstream data, an important ...type of big data, to create a new set of B2B analytical frameworks. In the markets where complex interactions between competitors, search intermediaries and consumers create a network, B2B relationships can be inferred from consumer search patterns, and can then be modeled to gauge the online performance. A commercial dataset from ComScore’s US panel of one million users is used to illustrate a new approach to measure and evaluate the online performance of competitors in the US airline market. The methodology and associated performance framework demonstrate the potential for new forms of market intelligence based on the visualization of market networks, online performance calculated from matrix algorithms, the measurement of the impact of search intermediaries, and the identification of latent relationships. This research makes theoretical and empirical contributions to the debate on the use of big data for B2B market analytics. B2B managers can use this approach to extend their network horizon from an egocentric to a network view of competition and map out their competitive landscape from the perspective of the customer.
•A hierarchical model of big data analytics is presented.•A market-level approach using big data to evaluate online performance is proposed.•Network visualizations of the interaction levels between the key players are derived.•Using this approach, managers can understand the evolving competitive landscape.
In 25 years, research on reputation-based online markets has produced robust evidence on the existence of the so-called reputation effect, that is the positive relation between online traders’ ...reputations and these traders’ market success in terms of sales and prices. However, there is an ongoing debate on what the size of the reputation effect means. We argue that the rate of truthful feedback that traders leave after completed transactions is negatively related to the size of the reputation effect. The higher the rate of truthful feedback, the quicker will untrustworthy traders be screened and disincentivized to enter the market. With mostly trustworthy traders entering the market, buyers will demand smaller price discounts from market entrants without a good reputation. We test this mechanism empirically in two laboratory experiments. In both experiments, we systematically vary the probability with which information about sellers’ behavior in an economic trust game is recorded and shown to future interaction partners of these sellers. In the second experiment, we introduce competition among sellers by allowing buyers to choose one of two sellers in each interaction. We find that sellers give discounts to buyers to build or repair their reputation and that sellers who give discounts or have a good reputation are trusted more. However, we do not find support for our hypothesis that a higher feedback rate significantly decreases sellers’ propensity to give discounts. We argue and show in exploratory analyses that this is likely due to the high level of unconditional trust buyers exhibit towards sellers without a reputation. Yet, seller competition increases the propensity to offer discounts among sellers without a reputation the most.
Most online market exchanges are governed by reputation systems, which allow traders to comment on one another’s behavior and attributes with ratings and text messages. These ratings then constitute ...sellers’ reputations that serve as signals of their trustworthiness and competence. The large body of research investigating the effect of reputation on selling performance has produced mixed results, and there is a lack of consensus on whether the reputation effect exists and what it means. After showing how the reputation effect can be derived from a game-theoretic model, we use meta-analysis to synthesize evidence from 107 studies investigating the reputation effect in peer-to-peer online markets. Our results corroborate the existence of the reputation effect across different operationalizations of seller reputation and selling performance. Our results also show the extent to which the reputation effect varies. We discuss potential explanations for the variation in reputation effects that cannot be attributed to sampling error and thereby point out promising avenues for future research.
The songbird trade has been identified as a major threat to wild populations, and the bird market has now expanded to online platforms. The study explored the use of machine learning models as a ...monitoring framework; developed models for taxa identification; applied the best model to understand the current market situation (taxa composition, asking price, and location); and conducted a survey to understand the profile of sellers. The authors found that the machine learning models produced a high level of accuracy in distinguishing relevant ads and identified the songbirds’ taxa. The Support Vector Machine (SVM) was selected as the best model and was used to predict the ad population. The model identified 284,118 songbirds from 247 taxa that were listed online from April 2020 to September 2021. The authors also found that 6.2% of ads listed threatened taxa based on the IUCN Red List. The survey results suggested that songbird sellers are mostly hobbyists or breeders looking for extra income from selling birds. As current studies of the songbird market are mostly conducted offline in the bird markets, transactions by non-bird traders or among hobbyists in the online market are remain underreported. Therefore, monitoring needs to be extended to the online market and to our knowledge, currently there is no applied system or platform is identified for monitoring online songbird market. The result from this study can help fill this gap. Information from the monitoring of the songbird online market in this study may assist stakeholders in formulating corrective action based on the current market situation.
•The authors demonstrated the songbird online market monitoring using big data and machine learning.•Machine learning models were compared to identify songbirds in online market.•284,118 songbirds were identified from 250 taxa within 1.5 years of observation data.•The authors found 6.3% of online advertisements listed IUCN threatened species.•Most sellers are hobbyists and breeders, only 7% of respondents are bird traders.
In recent decades, the demand, supply, and consumption of plant-based (pb) alternative products have increased worldwide. The objective of this study was to characterize pb meat and cheese products ...and compare them with their respective animal-based products. Data were collected in online market analyses (2019/2021). Nutritional data, Nutri-Score, and analysis of micronutrients are presented in this article. The number of products has grown in all categories, with the largest increase of 110% in pb cheese. The main protein sources in pb meat were soy and wheat, followed by an increasing use of peas. Pb meat generally contained less energy and total and saturated fat, but more carbohydrates and sugars than meat. In pb cheese, the protein content was lower than that of cheese. In 3 of 17 food groups, the salt content of pb alternatives was lower than in animal products. The daily requirement for iron could be covered better by pb alternatives than previously anticipated as well as the need for the vitamins E and K. The calculated Nutri-Score was generally lower for pb meat and higher for pb cheese than for the respective animal products. The trend towards consumption of pb alternative products is increasing, but the high level of processing, wide range of nutrients, and high salt content indicate the need for nutritional guidelines for these products.
This study aimed to investigate the meat quality and safety of chilled pork collected from 45 online stores in China. Total volatile basic nitrogen (TVBN), total viable counts (TVC), and coliform ...counts of 135 meat samples were analyzed, and the shipment conditions including endpoint temperature, transport time, transport distance and package model were also recorded. Foodborne pathogenic bacteria including Salmonella spp, Staphylococcus aureus (S. aureus), methicillin resistant Staphylococcus aureus (MRSA) and Listeria Monocytogenes (L. monocytogenes) were also detected and counted. The positive rate of Salmonella spp (13.32%), S. aureus (14.81%), MRSA (3.70%) and L. monocytogenes (4.44%) was found for 135 online meat samples. Endpoint temperature is the most important indicator of meat safety; when >10 °C, the unqualified rates for coliform counts, TVC and TVBN were 60%, 40% and 30%, respectively, which were significantly higher than those at < 4 °C. The vacuum packing model is found to be better than modified atmosphere (P < 0.05); modified atmosphere with an absorbent pad also has benefits for TVC and TVBN control. For the correlations between shipment condition and meat safety, the endpoint temperature has a positive correlation with meat safety, with a significance level of P < 0.001. However, no significant correlation was found between transport distance and meat quality. Results of this study showed that meat sold online poses potential hazards, and endpoint temperature control is the most important factor to ensure meat safety sold online in China.
•The quality and safety of chilled meat from online was investigated in China.•All of 4 food-borne pathogens were found in the online chilled pork samples.•Endpoint temperature control is the key factor for online chilled meat quality.•Vacuum packing model is better than modified atmosphere for online meat quality.
Finding items with potential to increase sales is of great importance in online market. In this paper, we propose to study this novel and practical problem: rising star prediction. We call these ...potential items Rising Star , which implies their ability to rise from low-turnover items to best-sellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework.
In this paper, product region extraction, which can classify the pixels of the product images as product and background regions, is proposed. The proposed method is based on the handcrafted algorithm ...using both the colour similarity and the saliency detection. Our experiment, which employed 180 product images, clarified that the proposed method increased all the metric for the extraction accuracy compared with conventional methods based on the handcrafted algorithm. The F-measure, which is the comprehensive metric, was significantly increased by 2.20% or more. Our discussion also found that the proposed method also overcame the shortcoming of the conventional method, because the F-measure for the dataset, the accuracy of which was decreased by the conventional method, was significantly improved. In addition, the F-measure was increased by 0.92% or more for each product category. Further comparison and discussion are included in this paper to provide more focused findings.
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