2023년 2월에서 2023년 11월까지 온라인으로 구매한 신 선편의식품 110건과 즉석섭취식품 115건을 대상으로 위생지 표균(일반세균, 대장균군 및 대장균)과 식중독균(Staphylococcus aureus, Salmonella spp., Bacillus cereus, Clostridium perfringens, Listeria monocytogenes, 병원성대장균)의 오염도를 조사하 였으며, 분리된 균주를 대상으로 병원성 유전자를 확인하 였다. 배송 형태는 보냉제를 사용해 배송 시간은 평균 24시간 이 소요되어 일반 택배로 배송되었고 제품 표면온도 평균 은 11.3oC이었다. 일반세균 정량분석 결과, 신선편의식품 의 평균 일반세균수는 4.5 log CFU/g, 즉석섭취식품의 평 균 일반세균수는 10.6 log CFU/g로 나타났다. 대장균군 정 량분석 결과, 신선편의식품, 즉석섭취식품 각각 평균 1.2 log CFU/g이었고, 대장균은 검출되지 않았다. S. aureus, Salmonella spp., C. perfringens, 병원성대장균은 모든 제 품에서 검출되지 않았다. B. cereus의 경우 신선편의식품 및 즉석섭취식품에서 각각 3건(2.7%), 1건(0.9%) 검출되었 고, 오염 수준은 신선편의식품에서 평균 0.05 log CFU/g, 즉석섭취식품에서 0.01 log CFU/g으로 나타났다. B. cereus 검출된 4건의 검체에서 B. cereus가 생성하는 독소 유전자 6종(hblC, bceT, entFM, nheA, CytK, CER)에 대한 유전자 확인시험 결과 4주가 분리되었고, 구토독소를 제외한 1개 이상의 장독소 유전자가 확인되었다. L. monocytogenes의 경우 즉석섭취식품에서는 검출되지 않았고, 신선편의식품 1건(0.9%)이 검출되었다. 분리한 L. monocytogenes에서 iap 등 6종의 병원성유전자가 검출되었고, 1/2a 혈청형이 확인 되어 식중독 발생 위험이 있음을 확인하였다.

This study investigated species identification and labeling compliance of 48 shrimp products sold in the Korean online markets. Species identification was conducted using the standard DNA barcoding method, using the cytochrome c oxidase subunit I gene. The obtained sequences were compared with those deposited in the NCBI GenBank and BOLD Systems databases. Additionally, phylogenetic analysis was performed to further verify the identified shrimp species. Consequently, 16 shrimp species were identified, including Penaeus vannamei, Pandalus borealis, Palaemon gravieri, Leptochela gracilis, Penaeus monodon, Pleoticus muelleri, Metapenaeopsis dalei, Euphausia pacifica, Lebbeus groenlandicus, Trachypenaeus curvirostris, Argis lar, Metanephrops thomsoni, Metapenaeopsis barbata, Alpheus japonicus, Penaeus chinensis, and Mierspenaeopsis hardwickii. The most prevalent species was Penaeus vannamei, found in 45.8% of the analyzed products. A significant mislabeling rate of 72.9% was found; however, upon excluding generic names such as shrimp, the mislabeling rate reduced to 10.4%. The mislabeling rate was higher in highly-processed products (89.3%) compared with that in minimally-processed products (50%). No correlation was found between the country of origin and mislabeling rate. The results of this study provide crucial data for future monitoring of shrimp products and improving the labeling of shrimp species in Korea.

Introduction With the opening of the 4th revolution era, platform business started to come into the spotlight. It was in the early 2000s that academics started full-scale research on platform based on the two-sided market theory but in fact the two-sided market is a business model that has existed around us since before. Examples include credit card industry, real estate brokering, and auctions. These industries are creating value through the interaction of two markets of different needs through a company that provides a specific platform (Rochet and Tirole, 2003, 2006). Recently, with the widespread use of services and products based on high technology, platform business is pouring into our lives at an amazing speed. With a single ID, you can shop, pay for, and receive shipping from a variety of online stores, without having to go through a lot of memberships. You do not have to search every single hotel for the best price, best location. You can even find a room in the house and see the reviews of customers who have stayed there. Compared to traditional pipeline business, one of the key differentiating features of the platform business is a two-sided network effect where consumers and consumers, producers and producers, and consumers and producers interact with each other. This two-sided network effect, with a positive feedback loop, has become a major driver of platform company growth. In the two-sided market, the platform‟s value to any given user largely depends on the size of the users on the other side of the platform due to the indirect network effect (Evans, 2003; Parker & Van Alstyne, 2005). Therefore, increasing the size of one side market, including the issue of „chicken or egg‟, is an indispensable task for the platform managers to maximize platform performance. But more important than increasing the size of one side of the market is transforming the new customers to loyal customers and creating positive feedback loops. This study explores the process of online payment platform users signing up, forming user loyalty, and spreading the loyalty to the sellers and platform providers. More specifically, this study examines (1) what causes the consumers to join the online payment platform at the beginning (2) what are the drivers that lead new members to the active, loyal users (3) whether the loyalty to the payment platform has a positive effect on the attitude toward the sellers (the other side market) and the platform company (platform provider). Theoretical Background and Hypotheses Two- Sided Markets and the Platform The definition of the two-sided market is varied by scholars. Chakravoti and Roson (2006) defines the two-sided market as a market where two different groups of users interact through the platform and the value created at this time is influenced by the indirect externalities of the network. Here, the platform is a physical, virtual, or institutional environment that allows different groups of users to facilitate transactions or interactions. According to Evans (2003), three necessary conditions for two sided platform markets are: (1) there are two distinct groups of customers; (2) there are externalities associated with customer A and B becoming connected or coordinated in some fashion; (3) an intermediary can facilitate that coordination efficiently than bi-lateral relationships between the members of the group. For example, we can think of a credit card company. There is a group of card holders and merchants and the demands of these two groups are very different. There is also a network effect between the two groups. Customers will not use credit cards with fewer merchants. The greater the number of merchants, the greater the benefits the customers have. Likewise, the more cardholders there are, the more profitable the merchants can generate. The intermediary role of the two groups of users to interact is a credit card company. Perceived value and Loyalty to the Online Payment Platform The loyalty to the online payment system can be expressed as the level of participation and commitment the member has over other similar payment systems. O‟Brien and Jones (1995) argue the value perception as an important prerequisite factor in developing brand loyalty; that is, only after the customer has perceived the online payment system as valuable, then would the customer become loyal to the system. The expectations that the perceived value can affect the loyalty to the online payment platform may be explained in terms of “Social Exchange Theory” (Thibaut and Kelly 1959). Within this framework, the customer will remain in the platform only when he or she perceives the value, which is defined as a trade-off between costs and benefits (Woodruff and Gardial 1996) is sufficient. Overall, perceived value of the online payment platform would affect customer loyalty. More specifically, it is expected that non-economic values such as simplicity in account setting, convenience in use will have a more meaningful effect than the economic value including sign-up grant at the stage of attracting new customers. But in the process of new customers becoming active and loyal users, the economic value including mileage points, discount coupons and free trial coupons will play a more important role in addition to this convenience. For example, benefit of being able to use the accumulated mileage or discount coupon on any online store within the platform will make consumers to stay on this payment platform and become loyal customers. Based on such argument, we put forward the following hypothesis. H1: Non economic value has a positive effect on online payment platform loyalty H2: Economic value has a positive effect on online payment platform loyalty. Loyalty Transfer According to Eisenmann et al. (2006), the platforms exhibit two types of network effects: A same-side effect, in which increasing the number of users on one side of the network makes it either more or less valuable to users on the same side; and a cross-side effect, in which increasing number of users on one side makes it either more or less valuable to the users on the other side. In case of online payment platform, it is expected that there will be a positive cross-side network effect. As the number of consumers using a specific online payment platform increases, the number of partner shops participating in the platform will increase, allowing consumers to shop in more diverse online stores. Once customers experienced the value of a specific online payment system, they would insist on paying by this method when shopping online (Parker, Alstein and Choudary, 2016), that is, becoming a loyal customer. Oliver (1999) defines customer loyalty as a deeply held commitment to re-buy or re-patronize a preferred product or service consistently in the future, thereby causing repetitive same brand purchasing. The loyal customers will be among the many online stores selling the same products/services at a same price, shopping at stores that show their favorite online payment system logo, and encouraging friends and family to use the payment system. The frequent transactions will affect consumer‟s attitude towards a certain brand or store and diffuse their loyalty. The customers that are loyal to the payment platform will not only actively try to earn points but also be willing to go and shop at the stores participating in the platform. Some customers may prefer a store among many even though they have not used it before because it belongs to the platform. The loyalty to the platform also can be transferred to the loyalty on the platform provider. Customers will have a favorable attitude when the platform providers do other business (for example, online banking, debit card business, etc.). Given the above, we put forward the following 3 hypotheses. H3: Platform loyalty has a positive effect on the loyalty toward the sellers, the other side market. H4: Positive attitude toward the sellers within the platform has a positive effect on attitude toward the platform company H5: Platform loyalty has a positive effect on the attitude toward the platform company. Conceptual Framework Data Analysis and Results The data used in this study were obtained from a survey of 562 online payment platform users. Data analysis shows that non-economic values such as simplicity, convenience, and platform reliability have a more significant impact on platform loyalty than economic values such as sign-up bonus. However, it was found that economic factors such as mileage points, and discount coupons are more influential factors in the process of converting new members to active users. In addition, loyalty to the payment system has a positive effect on attitudes towards sellers where one can use the means of payment. Also, it can be seen that the customer loyalty to the platform and the favorable attitude toward the sellers make a favorable attitude toward the platform company providing and managing the two-sided market. Conclusion and Discussion This study may contribute to a better understanding of platform business in three particular ways. First, understanding the loyalty diffusion mechanisms within the platform can support platform companies to develop effective strategies to attract new consumers to the platform and to transform them into active users. Second, even though the study uses the data collected from individual consumer level, the findings may provide some inspiration for B2B relationships. For example, as the number of loyal buyers and sellers sharing the platform increases, the value of the platform increases and the platform company can use it as a powerful bargaining power when it comes to third business. Third, the study may help us to understand the role of Platform in two-sided market and how the customer loyalty becomes diffused. Although this study explored the loyalty formation and diffusion using the sample of the major payment systems in Korea, it may be premature to generalize the findings at this stage. It is important to note that there may be negative network effects. They need to be evaluated further through careful research.

To lead a movie to success, managers must understand why consumers buy tickets. Some go to the movies due to trailers or movie posters, others watch movies triggered by their friends’ recommendation. Using Bass (1969)’s terms, we may categorize the former type of consumers into innovators who are influenced by external factors such as advertisements and media reports. We may label the latter type as imitators who are affected by internal factors such as word-of-mouth (WOM). Consumers in the digital era, regardless of their motivation types, easily obtain information related to movies through webpages or social networking services. Therefore, marketers should focus on how online information influence the diffusion of products. Additionally, each country has a unique cultural background, thereby resulting in different consumer behavior. Based on the prior arguments, we expect that the US movie market would show higher innovation effect and lower imitation effect compared to the Korean movie market. Opposite to the hypothesis, there are no significant differences in the innovation effect between the two markets. However, as expected, the imitation effect of US is significantly lower than that of Korea. In both markets, the advertising level and the publicizing level do not show any significant effect on the innovation effect. However, the two variables have significant and positive effect on the market potential. As predicted, the gender and the age heterogeneity of WOM are revealed to affect the imitation effect in the hypothesized direction. Product availability, measured with the number of screens, has a positive relationship with the innovation effect in the US market and with the market potential in both markets. Lastly, seasonality shows a positive association with the imitation effect in the US market and with the market potential in the Korean market. The current research tries to explore key differences in the diffusion patterns of movies between the US and Korean markets by applying Bass diffusion model. Further, this study aims to discover the factors that bring about the innovation and the imitation effects in both markets. By employing data available online, the current study could provide practical implications on how to manage information delivered through online channels.

In this research, we investigate the dynamics of one market cycle of Indian art through its various phases of development from early growth to peak and then through a decline. Using the entire online market data for Modern Indian art, we examine how the internal dynamics of the price formation process during auctions changed during the course of this market’s evolution. Our results show that the structure of the price formation process varies systematically over the course of the cycle and the impact of the brand (artist), the product (painting), market information (pre-auction estimates), demand (competition), and consumer (bidder) characteristics changed across the stages of market evolution. Specifically, the revenue, price per square inch, and the number of lots sold (items) from established artists closely followed the overall trend in the market. On the other hand, the number of lots and revenue from emerging artists had a minimal increase during the growth phase of the market. However, these artists maintained their value even when the market was declining. Furthermore, 80% of the lots by established artists beat the preauction estimates during the growth period, compared to only 40% for emerging artists. In contrast, during the decline stage, more lots from emerging artists (8%) beat the preauction estimates than lots from established artists (2%). We also find that bidders entered the auction much earlier during the growth phase of the market than during the maturity and decline stages. These findings have implications for multiple stakeholders in the online art auction market including, sellers, buyers, artists, and auction houses.