Retail Products Research Articles

The Correlation of China's Social Fixed Asset Investment with GDP, Fiscal Expenditure and Total Retail Consumer Goods

Since the reform and opening up, China's economy has experienced unprecedented rapid growth, in which social fixed asset investment, as a key driver, has contributed to economic development that cannot be ignored. This study focuses on the period from 2003 to 2022 and employs key economic indicators, including gross domestic product (GDP), fiscal expenditure, and total retail sales of consumer goods, to investigate the correlation between these variables and their impact on fixed asset investment. To this end, econometric analysis is utilized to elucidate the underlying mechanisms through which these variables influence fixed asset investment. The results indicate a positive correlation between fiscal expenditure, retail sales of social consumer goods and overall fixed asset investment. Furthermore, a negative correlation exists between GDP and fixed investment. This is due to the fact that an increase in GDP leads to an increase in total social consumer goods, which in turn reduces the funds available for investment. Consequently, a negative correlation emerges between total social consumer goods and fixed asset investment. This illustrates that fiscal expenditure exerts the greatest influence on social fixed asset investment, followed by GDP and total social consumer goods. The objective of this empirical analysis is to identify the key factors influencing China's fixed asset investment. The findings are intended to inform the formulation of more precise and effective economic policies by the government, facilitate the optimization of the investment structure, and support the advancement of high-quality economic development.

Bacterial microbiota associated with raw plant-based meat analogue products and their influences on selective enrichment for Escherichia coli O157:H7

Towards fostering a more sustainable food production system in face of the climate change challenge, alternative protein meat-substitute products that are plant-based and free of animal by-products have been gaining attractions from both food manufacturers and consumers. With these so-called plant-based meat analogues (PBMAs) becoming increasingly available at supermarkets, there is very little known about their microbial properties. In this short report, we characterized the bacterial composition of raw plant-based ground meat imitation retail products using 16S rRNA gene amplicon sequencing. Despite the observed bacterial community dissimilarity between sample brands, a total of 18 shared genera (dominated by Bacilli and Gammaproteobacteria classes) were identified as the core constituents of the bacterial microbiota of these PBMA products. Within the scope of food safety testing, to gain insights on the dynamics of the enrichment process for E. coli O157:H7 in accordance with the Health Canada reference method MFHPB-10, bacterial taxonomic analyses were conducted at different stages of the prescribed cultural procedures. Using both control and E. coli O157:H7-inoculated PBMA samples it was revealed that, independent of the presence of E. coli O157:H7, off-target bacteria of the Clostridium sensu stricto 1 genus were significantly enriched from the uncultured samples. Additionally, the abundance of Hafnia-Obesumbacterium bacteria in the PBMA samples was also increased in the enrichment products, but only when E. coli O157:H7 was absent. Consistent with the spread-plating results indicating that the inoculated E. coli O157:H7 cells were capable of reaching a high density (>108 CFU/ml) in the resultant enrichment cultures, the significant enrichment of bacterial 16S rRNA gene sequences belonging to the targeted genus of Escherichia, but not Hafnia-Obesumbacterium. This further highlights the competitive nature of the selective enrichment for E. coli O157:H7 against specific background bacteria associated with the PBMA products.

Bacterial composition and cultural dynamics of microgreens-associated microbiota during selective enrichment for Listeria monocytogenes.

Widely-regarded as a so-called "superfood", microgreens have become an increasingly significant food crop from both nutritional and agricultural standpoints. However, similar to other produce commodities that are also cultivated using modernized indoor-farming methods, there have been mounting concerns over the potential risks of consuming microgreens contaminated by Listeria monocytogenes. To gain insights into the microbial properties of microgreens, this study characterized the bacterial composition of fresh microgreen retail products using amplicon sequencing of 16S rRNA genes. Dominated by Gammaproteobacteria, a total of 36 shared genera were identified as putative constituents of the microgreen core microbiome. By monitoring the dynamics of microgreen-borne bacteria undergoing a Listeria-selective cultural enrichment procedure, it was revealed that, regardless of the presence or absence of L. monocytogenes, off-target bacteria of the Klebsiella and Enterococcus genera were significantly enriched from microgreens by the primary enrichment step, with the secondary enrichment step continuing to promote the expansion of Enterococcus population. While Listeria was generally neither the most-enriched nor the dominant taxon in cultures sampled at different enrichment stages, significant enrichment of Lysinibacillus and Bacillus bacteria were detected in microgreens contaminated with L. monocytogenes, suggesting they could be co-enriched in competition with Listeria.

Fine‐tuning of artificial intelligence managers' logic in a supply chain with competing retailers

AbstractToday, with the advance of artificial intelligence, companies in the real world are using AI as managers to make operational decisions, who can respond quickly to market shocks and whose logic can be fine‐tuned to programmed pessimism/optimism, that is, underestimating/overestimating the market. The introduction of AI managers poses new challenges to supply chain management, and how to manage AI managers warrants further exploration. We investigate the optimal AI manager fine‐tuning strategies in a supply chain consisting of one manufacturer and two competing retailers, each operated by an AI manager in the face of an uncertain market shock. We establish the manufacturer–retailer AI manager fine‐tuning game, where the manufacturer and two retailers endogenously decide whether to fine‐tune their AI managers' logic. The market may suffer an uncertain shock, and once the shock occurs, the AI managers' logic settings and price decisions can be quickly adjusted. We find that the manufacturer would never fine‐tune the AI manager, while the retailers may fine‐tune their AI managers to programmed optimism. Notably, AI manager's fine‐tunability only benefits the retailers and harms the manufacturer, entire supply chain, consumers, and social welfare. To make AI manager's fine‐tunability beneficial to all participants, that is, to reach a win–win–win situation, we design two incentive mechanisms, retailer pessimism incentive mechanism and mutual pessimism incentive mechanism (MPIM), where MPIM can lead to the win–win–win situation. Further, we endogenize the compensation, endogenous retailer pessimism compensation and endogenous mutual pessimism compensation, both achieving the win–win–win outcome. We also make several extensions and provide suggestions for supply chain firms to fine‐tune their AI managers' logic.

Harnessing Disruptive Technologies: Strategic Approach to Retail Product Innovation

Innovations in the retail industry, like Amazon's Just Walk Out (JWO) technology, are changing the way people shop by providing a cashierless experience. This article examines how to strategically utilize disruptive technologies for retail product innovation, with JWO as a case study. The examination summarizes JWO technology, its uptake, and the obstacles encountered during implementation. The research also explores the business dynamics of technological advancement, underlining the crucial role of disruptive technologies in improving customer satisfaction, operational effectiveness, and product creativity. The document points out the main drawbacks of JWO technology, such as restricted scalability, expensive implementation and operational costs, and the requirement for frequent technology updates. Suggestions for enhancing JWO technology and its utilization include implementing a licensing or subscription-based business model, incorporating modular design, and enhancing flexibility. The research also covers tactical factors for merchants when adopting innovative technologies, highlighting the significance of prioritizing customer needs, evaluating costs and benefits, considering scalability, building partnerships, and incorporating other technologies. The paper ends by emphasizing the capability of disruptive technologies to change the retail sector and the requirement for companies to find a balance between innovation and practical factors to thrive in a competitive environment. Keywords—Disruptive Technologies, Retail Product Innovation, Just Walk Out (JWO), Customer Satisfaction, Operational Efficiency, Machine Learning, Artificial Intelligence(AI), Technology Strategy, Machine learning (ML), Data.

Framework for adoption of generative AI for information search of retail products and services

PurposeGenerative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.Design/methodology/approachWe conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.FindingsResults show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.Originality/valueConsumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

Online Retailing Operations: Is It Wise to Offer Free Sample Trial and Money-Back Guarantee Together?

In retail operations, free sample trial (FST) and product return with money-back guarantee (MBG) both serve as important strategies to reduce consumers’ worries and valuation uncertainty. This paper is the first study which analytically examines when and whether an online retailer should provide both strategies simultaneously, and when one strategy outperforms the other. We consider an online retailer selling to consumers who are uncertain about the product values when making purchasing decisions. The retailer decides whether to offer MBG or FST. We develop analytical models to examine four strategies: strategy NN (both FST and MBG are not provided), strategy NS (FST is provided and MBG is not offered), strategy MN (FST is not provided and MBG is provided), and strategy MS (both FST and MBG are offered). Comparing among these four strategies, we find that when only FST is offered, the retailer surprisingly should decrease the price if the product cost is low. If the retailer only provides MBG, he will charge a higher price compared with the situation when MBG is not provided. When the size of free sample is very small, strategy NS (MS) is the optimal strategy if the product's cost is low (high). When the size of free sample is large, strategy MN is the optimal strategy. Then, we extend our model to consider three situations, namely: (a) the retailer offers coupons to consumers to experience the free sample, (b) consumer returns incur a cost, and (c) the size of free sample is endogenous. We find the main conclusion remains robust. Our findings may help explain some real-world practices, such as why some retailers (e.g., Amazon and Walmart) provide consumers with low volume toners to decrease the free sample cost; skincare product retailers such as Lancôme, Estee Lauder, and Guerlain implement both MBG and FST. We also interestingly show that a bigger size of free sample may not necessarily benefit consumers.

Revolutionising Skincare Through Airtificial Intelligence

Abstract: Background: Over the last 10years there has been a drastic shift in the development of computer-based systems, artificial intelligence, algorithms and technology in the world of dermatology be it cosmetic or even retail production of products sold to customers with high-tech technology and innovative means to build their client base through customer delight. Artificial Intelligence is particularly helpful in recognizing the type of skin customers have and suggesting solutions which help customers pick from the range of options given to them. Most e-commerce platforms have begun using such AI driven soft wear to help customers while they are in the comfort of their homes to choose from a range of cosmetic options right from lipsticks, concealers, foundation, eyeshadows, shades of tins, serums etc. Given that every person has a different skin type, these applications ensure 100 percent personalized suggestions. This new trend is sending shock waves across the retail sector around the world attracting a massive customer base to adapting to AI Derma logy and retail. The intelligent software used by sever companies not only benefits the customer, but also helps companies with their operational efficiency as they can use AI algorithms to predict and forecast the inventory to help reduce over production and excessive stock production minimizing product wastage and improving sustainably.AI is booming in both clinical diagnosis as well as giant companies such as Loreal and Olay who are dedicated to providing customers with advanced tools like skin analyzers or apps based on predefined rules which help AI-powered tools analyze skin conditions, age, and personal skincare history to provide highly personalized product recommendations. In clinical diagnosis AI offers valuable insight to the patient’s skin and condition be it cancer or other skin diseases making judgments to assist the doctors and dermatologist in making informed decisions regarding the line of treatment for the patient in question. This includes screening, diagnosis, treatments and predictions of the treatment outcomes. These new innovative and robust AI systems leaving customers feeling confident and have not begun relying on AI driven treatments and procedures

Green beauty: examining factors shaping millennials’ attitudes toward organic personal care products in South Africa

PurposeThis study aims to explore the factors that influence millennials’ attitudes toward organic personal care products, focusing on the role of health consciousness, environmental concerns and quality perceptions.Design/methodology/approachAn electronic, self-administered survey was used to collect 377 responses. The data analysis utilized partial least squares structural equation modeling (PLS-SEM).FindingsThe findings reveal that environmental concerns, health consciousness and perceived behavioral control play a significant role in shaping millennials’ attitudes toward organic personal care products, while the perceived quality of organic personal care products significantly influences their purchase intentions.Practical implicationsThe results of this study provide valuable insights for personal care product manufacturers, retailers and marketers looking to target millennial consumers. By understanding the key factors that influence millennials’ attitudes and purchase intentions, marketers in the personal care product industry can tailor their strategies effectively.Social implicationsThe study’s findings inform strategies that promote healthier and more environmentally conscious consumer behavior. This aligns with broader societal goals of promoting sustainability and health consciousness, contributing to a more environmentally and socially responsible consumer culture.Originality/valueThe study’s contribution lies in its focused exploration of the interplay between health consciousness, environmental concerns and quality perceptions on millennials’ attitudes toward organic personal care products.

Live streaming channel and product assortment with both national and store brand products

To counteract the fierce competition in the online retailing market, retailers are increasingly adopting live streaming selling model and introducing store brand product to enhance their competitiveness. In this context, we attempt to examine whether and when a retailer should launch a live streaming channel. If launched, we further investigate how the retailer should assort both national and store brand products across available channels. To delve into these questions, we consider a supply chain consisting of a manufacturer and an online retailer, where the retailer resells the manufacturer's national brand product and also sells a store brand product. We examine the retailer's optimal channel strategy and product assortment strategy by considering six scenarios, differentiated by whether introducing the live streaming channel and various product assortment tactics. Our findings indicate that as long as the fixed setup cost of the live channel is relatively low, the retailer will always introduce a live streaming channel. The retailer's optimal assortment of both products across channels highly depends on the consumers’ additional value derived from the live streaming channel and the perceived quality of the store brand product. We also find that, the retailer's optimal channel strategy and product assortment may not always benefit the manufacturer, but there exist conditions that can create win-win situations for both players. Our study further shows that if the retailer launches the live streaming channel at the start, it may not always be necessary to introduce the store brand product under certain conditions.

Supply Strategies and Business Model Options for Online Retailers of Agricultural Products

Supply Strategies and Business Model Options for Online Retailers of Agricultural Products

Stochastic Differential Games of Carbon Emission Reduction in the Four-Tier Supply Chain System Based on Reference Low-Carbon Level

This paper takes corporate social responsibility goodwill and consumers’ reference low-carbon level as endogenous variables of joint carbon emission reduction in the “supplier–manufacturer–retailer–consumer” supply chain system. The joint carbon emission reduction strategies of this four-tier system are analyzed from a dynamic perspective by considering random factors that affect the endogenous variables. Three stochastic differential games are proposed to examine the mechanism between each player, namely the cooperative model, Nash non-cooperative model, and Stackelberg master–slave model. Compared to the Nash non-cooperative game, the manufacturer/supplier-led Stackelberg master–slave game leads to Pareto improvement in the profits of the entire supply chain system and each player. The cooperative game demonstrates the highest expected emission reduction and corporate social responsibility goodwill, but also the highest variance. More importantly, the reference low-carbon level embraces consumers’ subjective initiative in the dynamic of carbon emission reduction. This level is an internal benchmark used to compare against the observed low-carbon level. This paper provides a theoretical foundation for strategic decision-making in emission reduction, contributing to sustainable development. By addressing environmental, economic, and social sustainability, it promotes climate action through carbon reduction strategies and offers policy recommendations aligned with the Sustainable Development Goals.

스포츠 브랜드 매장의 체험마케팅이 브랜드사랑에 미치는 영향 - 관여도의 조절효과를 중심으로

The aim of this study is to investigate the relationship between experiential elements in sports brand stores and brand love, with involvement serving as a moderating effect. This research seeks to enable sports product manufacturers and retailers to better understand consumers and establish a foundation for experiential marketing strategies. The survey was conducted over a 15-day period from January 10, 2024, to January 25, 2024. A total of 720 questionnaires were distributed online, and after excluding insincere responses, 697 questionnaires were used as the final data for analysis. The collected survey data were analyzed using SPSS 26.0 and AMOS 28.0, employing frequency analysis, factor analysis, reliability analysis, correlation analysis, and structural equation modeling. The results of the analysis are as follows: First, in the study of sports brand store experiences, targeting consumers who have experienced sports brand experiential marketing in Shanghai, China, it was found that sensory, emotional, cognitive, behavioral, and relational elements have a significant positive impact on brand love. Second, involvement was found to have a significant moderating effect on the relationship between experiential elements (sensory, emotional, cognitive, behavioral, and relational) and brand love. Therefore, it is important to develop experiential marketing strategies that enhance consumer experiences, leveraging the fact that the experiential elements (sensory, emotional, cognitive, behavioral, and relational) at sports brand stores positively impact brand love. In particular, since the relationship between experiential elements and brand love is strengthened when consumer involvement is high, it is necessary to plan customized experiential marketing activities to increase involvement. Such an approach will contribute to strengthening the relationship with consumers and enhancing brand loyalty.

Consumer perspectives on interventions to combat fraudulent product returns in omnichannel fashion retail

PurposeThe expansion of online shopping aligned with challenging economic conditions has contributed to increasing fraudulent retail product returns. Retailers employ numerous interventions typically determined by embedded perspectives within the company (supply side) rather than consumer-based assessments of their effectiveness (demand side). This study aims to understand how customers evaluate counter-fraud measures on opportunistic returns fraud in the UK. Based on the fraud triangle and the theory of planned behaviour, we develop an empirically informed framework to assist retail practice.Design/methodology/approachWe collected 485 valid survey responses about consumer attitudes regarding which interventions are effective against different types of returns fraud. First, a principal component section evaluates the policies' effectiveness to identify any policy grouping that could help prioritise specific sets of policies. Second, cluster analysis follows a two-stage approach, where cluster size is determined, and then survey respondents are partitioned into subgroups based on how similar their beliefs are regarding the effectiveness of anti-fraud policies.FindingsWe identify policies relating to perceived effectiveness of interventions and create customer profiles to assist retailers in conceptualising potential opportunistic fraudsters. Our product returns fraud framework adopts a consumer perspective to capture the perceived behavioural control of potential fraudsters. Results suggest effectiveness of different types of interventions vary between different types of consumers, which leads to the development of propositions to combat the fraud.Originality/valueThis study is unique in assessing the perceived effectiveness of a range of interventions based on data collection and advanced analytics to combat fraudulent product returns in omnichannel retail.

Microbial profiling of oysters from a processing plant and retail products: Analysis based on culture-dependent methods and 16S rRNA gene sequencing

Oysters (Crassostrea gigas) are one of the most consumed shellfish globally. However, there is a lack of comprehensive microbiome studies that include the processing and distribution stages of oysters. The present study used both culture-based methods and 16S rRNA sequencing to produce comprehensive microbial profiles of oysters in two parts: (1) an oyster processing plant that processes raw and frozen oysters (n = 57) and (2) retail oyster products across two seasons (winter and spring) (n = 112). In the processing plant, shucking increased the aerobic plate count (APC) from 1.86 log CFU/g in freshly harvested oysters to 3.95 log CFU/g in shucked oysters. Controlling the washing process is important, as the APCs decreased after washing and remained level until the final products, raw and frozen oysters (2.54 and 2.34 log CFU/g, respectively). After desalting in the frozen oyster plant, the bacterial community shifted to be dominated by the family Spirochaetaceae, Mycoplasma, and Shigella taxa, indicating a need to control problematic bacteria in the desalting process. SourceTracker analysis revealed that raw materials contributed more to the microbiota of final products than environmental samples. In retail oyster products, APCs were marginally higher in spring (3.58 log CFU/g) than in winter (3.05 log CFU/g) samples. While bacterial count differences were not dramatic, the proportions of taxa in the microbial community differed by season. In winter retail products, Photobacterium (27.91 %) and Aliivibrio (20.42 %) dominated, while spring samples showed a diverse distribution of the family Vibrionaceae (19.90 %), Photobacterium (14.20 %), Psychromonas (11.84 %), and Aliivibrio (7.20 %). These findings contributed to our understanding of oyster microorganisms and identified food safety control points and cross-contamination sources. This in-depth understanding is expected to inform the development of fishery and seafood safety management measures.

A Literature Review to Identify Drivers and Barriers Affecting the Adoption of Green Practices for Indian Manufacturing Companies.

Environmental concerns are an increasingly important issue for many businesses for sustainable development. A supply chain with eco-friendly practices is called a 'Green supply chain' and is commonly observed in most manufacturing companies today. The present study provides an overview of the factors influencing the adoption of green practices by Indian manufacturing companies, including both drivers and barriers. Approximately 260 papers from various journals in the field of green supply chain management were collected to conduct this review to identify the major drivers responsible for implementing green practices and the associated challenge. This review has contributed to identifying the research gap concerning the necessity to explore the key factors influencing the Indian retail manufacturing sector. The study assists organizations in recognizing both the drivers and obstacles that affect the effective and enduring implementation of green manufacturing practices. Embracing these processes is increasingly vital for organizations aiming for sustainability, as well as for comprehending the factors that are propelling the swift transition towards green practices.

Product Demand Forecasting For Inventory Management with Freight Transportation Services Index Using Advanced Neural Networks Algorithm

Purpose: Accurate demand forecasting is critical for optimizing inventory management, improving customer satisfaction, and maximizing profitability in the retail sector. Traditional forecasting models predominantly utilize micro-level variables, such as historical sales data and promotional activities, often neglecting the influence of macroeconomic conditions. Materials and Methods: This study addresses this gap by integrating Freight Transportation Services Index (TSI) which is an indicator for overall economic health with time series data of retail product sales. Utilizing an advanced neural networks model, we demonstrate that incorporating macroeconomic variables significantly enhances the model's predictive accuracy and explanatory power. Findings: The results reveal that the model with the TSI index outperforms conventional models, highlighting its potential for practical application in the industry. Implications to Theory, Practice and Policy: This approach offers a more comprehensive understanding of demand dynamics, enabling businesses to make more informed decisions, adapt to market fluctuations, and maintain a competitive edge.

A thorough evaluation of flavor characteristics among various retail peanut oil products using multivariate statistical methods

AbstractFlavor is a crucial characteristic of peanut oils, exerting a significant impact on their quality and acceptability. However, given the uncertainty in the correlations between flavor attributes and volatile compounds, assessing the flavor quality of commercial peanut oil retail products (PORPs) based on updated analysis techniques remains challenging. In this study, 21 commercial samples were collected for investigating this correlation. From the sensory evaluations, the 16 fragrant PORPs show differences esp. for the roasted (4.25–10), the sweet (0–4.25), and the steamed (0–4.25) attributes while the roasted attribute is dominant for all the products. For volatile profile analysis with updated analytical technology, the fragrant PORPs also show significant difference in terms of volatile profiles and intensities. The total intensity can differ from 16.37 to 66.59 mg/kg. Multivariate statistical methods (MSMs) revealed the significant correlations between volatile compounds and specific sensory attributes. Notably, 1‐hexanol and phenylethyl alcohol demonstrate a strong correlation with the raw peanut flavor attribute. Benzeneacetaldehyde (BA) exhibited a positive correlation with sweet flavor. dl‐Pantolactone has a positive effect on roasted flavor. Hexanal and 3‐octen‐2‐one are highly related to off‐flavor notes. In addition, based on machine learning (ML), three groups of volatile compounds were found to be highly correlated with specific flavor attributes. Notably, a cluster comprising seven compounds, namely 2‐ethenyl‐6‐methylpyrazine, 2‐methyl‐6‐[(E)‐1‐propenyl]pyrazine, benzaldehyde, BA, nonanal, dl‐pantolactone, and 2‐acetopyridine exhibiting a positive correlation with roasted, steamed, and sweet flavor attributes. Furthermore, recombination experiments were conducted to verify the synergistic effect of these seven compounds on flavor performance and agreements were reached.

Enhancing organic milk sales through cumulative power of product – extrinsic cues: empirical evidence from China

PurposeTo examine the impact of manufacturer reputation, retailer reputation, and product price on consumers’ perceived quality and purchasing behavior regarding organic milk.Design/methodology/approachEmploying a 2 × 2 experiment, data were collected from 1,259 consumers in 32 provinces in China.FindingsWhen a low-reputation manufacturer sells products through a high-reputation retailer, it improves consumers’ perception of quality and positively influences their purchasing behavior. Interestingly, setting higher prices for products manufactured by low-reputation companies and selling them through high-reputation retailers did not significantly enhance consumers’ perceived quality and deter their purchasing behavior.Originality/valueThe analysis expands the framework for cue diagnosis. While the existing framework primarily focuses on the influence of cue-type combinations on perceived quality, it does not integrate purchasing behavior into the conceptual framework. This limitation hinders people understanding of the theoretical mechanisms underlying the use of cues in purchasing decisions. This paper address this by gradually introducing variables, such as retailer reputation and product price, into the baseline model, thereby extending this theory. In addition, this paper advances the marketing research literature within the business-to-business-to-consumer context by examining the additive effects of manufacturer reputation, retailer reputation, and product price on consumers’ perception of quality and purchasing behavior.

Prevalence and Characterization of Salmonella during Pork Sausage Manufacturing.

Pork carcasses and meat may harbor Salmonella and may contaminate other products during harvest and fabrication. Sources of contamination include manure on hides, environmental contamination, ingredients from external sources, and lymph nodes. Swine lymph nodes are often incorporated into ground meat, as their anatomical location makes removal labor prohibitive. A sausage processing plant in the midwestern United States was sampled monthly (except for December) from May 2021 to April 2022 to enumerate Salmonella and Enterobacteriaceae (EB) throughout the sausage manufacturing process to determine high-risk stages and efficiency of existing in-plant interventions. Salmonella serotypes and antimicrobial susceptibility were evaluated on a subset of isolates recovered at the end phases of sausage production. In each collection, samples were taken from the carcasses of eight sows through 11 stages of sausage manufacturing. A total of 830 samples were cultured. Thirty-four Salmonella were isolated from the final three production stages; of these, there were eleven serotypes. Three isolates displayed resistance to ampicillin, whereas the remainder of the isolates were pan-susceptible to the antimicrobials tested. Salmonella and EB were significantly reduced (p < 0.001) by acid washes at different stages of production, and the results point to the beneficial effects of interventions to lessen Salmonella concentrations in retail products.