Hasil untuk "Marketing. Distribution of products"

Amel Touer , Omeyr Cheloufi

تهدف هذه الدراسة إلى البحث في العلاقة السببية بين سعر الصرف ومعدل التضخم في الجزائر، خلال الفترة الممتدة من 1990-2023 باستخدام نموذج var واختبار السببية لغرانجر الأجل القصير واختبار تودا ياما موتو في الأجل الطويل حيث توصلت نتائج الدراسة إلى وجود علاقة سببية وحيدة من سعر الصرف الى معدل التضخم في الأجل القصير واستدامتها في الأجل الطويل، وتتوافق هذه النتيجة مع ما أتت به النظرية الاقتصادية التي تشير إلى أن معدلات التضخم المرتفعة كان سببها الرئيسي هو انخفاض قيمة العملة.

Devi Yulia Rahmi, Fikri Alwi, Ratni Prima Lita et al.

Objective: The study aims to examine the effect of halal awareness, halal logo, religiosity, and price on consumers' intention to buy food products with a halal logo. This study also examines the effect of purchase intention on consumer purchase behaviour of food products with the halal logo Research Design & Methods: This study uses a quantitative method with 200 respondents who consume food products with the halal logo in West Sumatra. Data were analyzed using the PLS-SEM (Partial Least Square-Structural Equational Modeling) method. Findings: The results of the study show that the halal logo, religiosity, and price are significant for purchase intention. No significant effect was found on halal awareness and attitude towards purchase intention. Moreover, the study's results show that purchase intention significantly affects purchase behaviour. Implications and Recommendations: This research implies that consumers of food products labelled halal consider the halal logo on a product before consuming it, so food producers must try to sell products that have a halal logo Contribution & Value Added: This study addresses the existing literature by modifying the research model regarding the purchase behaviour of halal products. In practical, companies in the halal food industry should focus on maintaining and enhancing product quality, obtaining halal certification, and establishing a trusted halal label to foster consumer purchase intention and behavior.

Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick et al.

Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.

W. Madushan Fernando, Amila Thibbotuwawa, H. Niles Perera et al.

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.

Zlatanović Dejana, Dukanac Milica, Erić Nielsen Jelena

The new product serves as the foundation for ensuring the long-term survival and growth of the company, as well as being a driving force for the economy and a critical factor in the competitiveness of national economies. The launch of a new product on the market, or its commercialization, is a crucial phase in the development process, requiring a strategic approach to decision-making and activities related to sales. The research subject in the paper are various factors related to the launch of a new product, with a particular emphasis on consumers' perceptions of the product's characteristics and its market acceptance, as well as elements of the marketing mix and marketing activities of the company, and characteristics of the market and its segments. The research objective is to identify the key factors that can influence the level of acceptance of a new product in the market, as well as differences in how users perceive the importance of these factors. An empirical study was conducted from June to August 2023, involving 101 respondents from the Republic of Serbia that use of Apple Iphone. The results of the study indicate that compatibility, complexity, quality of marketing efforts, and market characteristics are the key factors in the successful launch of a new product. Furthermore, there are differences in respondents' perspectives on these factors based on age, type of phone used, and categories of new product adopters.

Hajar Ebrahimi, Fahimeh Babalhavaeji, Dariush Matlabi et al.

Objective: This study aims to investigate the factors influencing professional publishing and propose a model for professional book publishing in Iran. Method: The research was conducted using a survey method with a researcher-developed questionnaire. The statistical population consisted of professional publishing managers in the country, totaling 581 publishers, from which 231 were selected as a sample based on Cochran's formula. Ultimately, 211 questionnaires were completed and analyzed. Data processing was carried out using SPSS software, employing exploratory the factor analysis and multiple regression test. Findings: Based on exploratory factor analysis, nine factors have been identified as influential in professional publishing: the economics of publishing, the supply and display of publishing products, government support and backing, adherence to copyright, publishing evaluation and auditing, advertising, marketing and branding, publishing management, and the creation of publishing content. Additionally, five factors have been recognized as dimensions of professional publishing, which include technical elements, cultural and literary circles and centers, authors and audiences, electronic systems, and distribution and marketing elements. Ultimately, in the regression model, five independent variables were included in the equation due to their significance level being below .05. Conclusion: The findings of this research contribute to enhancing the awareness and understanding of audiences regarding publishing processes. They also assist publishers and industry managers in recognizing successful trends and existing challenges within the field, as well as in formulating supportive policies and strategies for publishing by relevant authorities.

Jaydip Sen, Hetvi Waghela, Sneha Rakshit

This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible, recent research has shown the potential of advanced algorithms and predictive models. The study builds upon existing literature on stock price prediction methods, emphasizing the shift toward machine learning and deep learning approaches. Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices. These predictions guide buy/sell decisions for each stock and analyze sector profitability. The study's main contributions are threefold: introducing an optimized LSTM model for robust portfolio design, utilizing LSTM predictions for buy/sell transactions, and insights into sector profitability and volatility. Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions. By comparing sector profitability and prediction accuracy, the work provides valuable insights into the dynamics of the current financial markets in India.

Burcu Akdeniz, N. Derya Ergün Özler

For the past 100 years, Turkishwomen have had the opportunity to obtain an education and enter the labor force due to the democratic regime established in 1923. Despite some economic and social barriers, they have taken advantage of these opportunities and advanced in their careers to some extent. However, as with all women around the world, they face barriers in their career paths. This study aims to analyze the scientific research studies on the glass ceiling syndrome conducted in Türkiye to gain detailed insights into people’s perception of the glass ceiling and capture their perspectives on the factors that contribute to it. This is a meta-synthesis study aimed at conducting a systematic review of selected qualitative studies and integrating their findings. A systematic search was conducted across local academic databases, namely, Dergipark and Tubitak Ulakbim-Equal. MAXQDA2022 software was used to code and analyze the articles. The factors forming glass ceiling defined by the studies were renamed as 18 subthemes in total and classified under three themes: (1) personal factors, (2) sociocultural factors and (3) organizational factors. Although the role of motherhood and work life balance was found to be the most frequently referred factor, some current research revealed that not only women but also men experience this syndrome in Türkiye, despite the patriarchal culture of the country. The study’s limitations are noted, and the implications and future research directions are discussed.

Norzaidi Mohd Daud, Raja Nur Hannah Fatimah Raja Mohd Hashimb, Anis Irdina Yang Asri et al.

Purpose – The purpose of this paper is to examine the relationship between information commerce usage and purchase decisions made by university students during the COVID-19 pandemic. Design/Methodology/Approach – Survey questionnaires were distributed to students at public and private universities through a variety of online platforms. Data was collected from 151 respondents within a two-week period and analyzed using IBM Statistical Package for the Social Sciences (SPSS) Version 26 for descriptive (i.e., mean, standard deviation, reliability test, model fit test) and inferential (i.e., correlation and regression for hypotheses testing) purposes. Findings and implications – The findings indicate that, while students engage in information commerce for its convenience, their usage of it is unrelated to the degree of trust. Nonetheless, price comparison has a sizable influence on how information commerce is used. Additionally, information commerce usage has an effect on purchase intention, while purchase intention is found to be a significant predictor of purchase decisions. Limitation – This study examined factors that influence the usage of information commerce among students in Malaysian universities, as well as their purchase intention, on a rather small sample size. Originality – This may be the first study to define information commerce usage, with its results confirming students’ purchase behavior and demonstrating that online shopping simplifies their lives. Surprisingly, the findings suggest that the degree of trust does not influence the usage of information commerce. This contradicts previous studies conducted by Anas et al. (2021) and Viktoria et al. (2021).

Long Kim, Thanapa Chouykaew, Siwarit Pongsakornrungsilp et al.

Promoting repurchase intention to existing consumers is a crucial advantage that helps businesses manage sufficient supply for their customers and ensure their business sustainability. Business managers must understand factors that can significantly promote repurchase intention. Therefore, this study aims to examine the influence of brand love, brand preference, and brand loyalty on the intention to repurchase Covid-19 antigen test kits among a sample of Thai consumers. To achieve this aim, 670 Thai people, who had used the antigen test kit for their Covid-19 testing, were invited to answer questionnaires using an online Google Forms survey. After clearing outliers, only 523 responses were deemed valid and reliable and kept for further path analysis. The research findings showed that brand love and brand preference demonstrated positive relationships with brand loyalty. In addition, brand love and brand preference displayed positive relationships with repurchase intention. In conclusion, the results emphasized brand loyalty as the primary driver of repurchase intention because of its significant impact on Thai customers.

Romeo Bandinelli, Gabriele Scozzafava, Bianca Bindi et al.

The importance of traceability in food products in regard to consumer preferences, the difficulty of certifications in communicating credence attributes, and concerns about food safety, have led consumers to ask for more information about the credibility of information reported on the label with the product itself, due to the importance of traceability in food products. An objective of this study is to analyze the benefits of implementing blockchain technology in the supply chain for ancient wheat, as a technology capable of sharing reliable information about the products easily along the entire value chain. In particular, the aim is to evaluate how the consumer reacts when confronted with a package of ancient wheat pasta for which all the information on its origin and processing methods is available. In order to achieve these results, a survey based on the Technology Acceptance Model has been developed and conducted in the Italian country. Result indicates the importance of identifying an independent variable that represents the degree of security when faced with a threat that creates a circumstance, condition or event that can lead to economic hardship, for example, data destruction, disclosure, modification, fraud, waste, and abuse. The strength of blockchain lies precisely in its ability to guarantee the immutability of data throughout the supply chain, providing the end consumer with a high-quality product.

Roxana Elena POPȘA

Currently, we live in a world that is constantly changing, one in which the digital environment is constantly evolving. In the travel and tourism industry, there is practically no management decision that is not related to digitization. Additionally, this is not only when thinking about the ease of interaction with consumers of tourist services, but also when considering optimizing and reducing costs or when we refer to the management of the business itself. Therefore, this paper aims to analyze the evolution of the online tourism market, the most visited travel and tourism websites, and the use of several digital transformation topics among accommodation businesses, such as: social media marketing, online advertising, customer relationship management, and online distribution channel management.

Chunjing Gan, Dan Yang, Binbin Hu et al.

Nowadays, the rapid development of mobile economy has promoted the flourishing of online marketing campaigns, whose success greatly hinges on the efficient matching between user preferences and desired marketing campaigns where a well-established Marketing-oriented Knowledge Graph (dubbed as MoKG) could serve as the critical "bridge" for preference propagation. In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i.e., uncontrollable relation generation of LLMs,insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs. To this end, we propose PAIR, a novel Progressive prompting Augmented mIning fRamework for harvesting marketing-oriented knowledge graph with LLMs. In particular, we reduce the pure relation generation to an LLM based adaptive relation filtering process through the knowledge-empowered prompting technique. Next, we steer LLMs for entity expansion with progressive prompting augmentation,followed by a reliable aggregation with comprehensive consideration of both self-consistency and semantic relatedness. In terms of online serving, we specialize in a small and white-box PAIR (i.e.,LightPAIR),which is fine-tuned with a high-quality corpus provided by a strong teacher-LLM. Extensive experiments and practical applications in audience targeting verify the effectiveness of the proposed (Light)PAIR.

Manijeh Alipour, Omid Alizadeh-Mousavi

Renewable energy productions and electrification of mobility are promising solutions to reduce greenhouse gas emissions. Their effective integration in a power grid encounters several challenges. The uncertain nature of renewable energy productions as well as stochastic consumptions of electric vehicles introduce remarkable intermittency to a distribution grid and results in bi-uncertain characteristics of both supply and demand sides. One way to verify the secure grid operation within acceptable voltage and loading levels is to assess its required flexibility considering possible boundaries of uncertain variables. In this paper, first a comprehensive linear model of distribution grid considering all pertaining constraints is presented. Then, a flexibility estimation technique is proposed based on the feasibility study of the uncertain space of photovoltaic power productions and load containing electric vehicles. The proposed methodology uses grid monitoring data to determine grid state and to model uncertain parameters. The model is applied on a real low voltage (LV) system equipped with grid monitoring devices.

Andrii Voshchepynets, Oleksiy Agapitov, Lynn Wilson et al.

We present the results of processing the effects of the powerful Gamma Ray Burst GRB221009A captured by the charged particle detectors (electrostatic analyzers and solid-state detectors) onboard spacecraft at different points in the heliosphere on October 9, 2022. To follow the GRB221009A propagation through the heliosphere we used the electron and proton flux measurements from solar missions Solar Orbiter and STEREO-A; Earth magnetosphere and the solar wind missions THEMIS and Wind; meteorological satellites POES15, POES19, MetOp3; and MAVEN - a NASA mission orbiting Mars. GRB221009A had a structure of four bursts: less intense Pulse 1 - the triggering impulse - was detected by gamma-ray observatories at 131659 UT (near the Earth); the most intense Pulses 2 and 3 were detected on board all the spacecraft from the list, and Pulse 4 detected in more than 500 s after Pulse 1. Due to their different scientific objectives, the spacecraft, which data was used in this study, were separated by more than 1 AU (Solar Orbiter and MAVEN). This enabled tracking GRB221009A as it was propagating across the heliosphere. STEREO-A was the first to register Pulse 2 and 3 of the GRB, almost 100 seconds before their detection by spacecraft in the vicinity of Earth. MAVEN detected GRB221009A Pulses 2, 3, and 4 at the orbit of Mars about 237 seconds after their detection near Earth. By processing the time delays observed we show that the source location of the GRB221009A was at RA 288.5 degrees, Dec 18.5 degrees (J2000) with an error cone of 2 degrees

Szabolcs Nagy, Gergo Hajdu

Digitalization is making a significant impact on marketing. New marketing approaches and tools are emerging which are not always clearly categorised. This article seeks to investigate the relationship between one of the novel marketing tools, content marketing, and the five elements of the traditional marketing communication mix. Based on an extensive literature review, this paper analyses the main differences and similarities between them. This article aims to generate a debate on the status of content marketing. According to the authors' opinion, content marketing can be considered as the sixth marketing communication mix element. However, further research is needed to fill in the existing knowledge gap.

Nathaniel Francis Golding

In the marketing of video games made by self-publishing, independent developers, there is common advice to market a game using the social media platform Twitter. However, the advice that stems from industry sources is somewhat contradictory, and many self-publishing independent developers elect not to use Twitter at all. This presents an opportunity for researchers to investigate this tension and determine if using Twitter does have a causal influence on the successful release of a game by using relatively recent developments in causal data science techniques. In this sense, this paper highlights these causal inference developments while simultaneously informing self-publishing independent developers whether they should indeed market their games using Twitter. It was found that using Twitter results in an average increase of 85.4 reviews during release week, corresponding to a 314% positive difference. Using Twitter also doubles the chance of reaching a critical 10-review inflection point threshold on release week. These effects, however, are moderated by the characteristics of the game, expressed as 'tags', yet in no case is the effect of Twitter use reduced to 0. Based on these findings, it is advised that new, self-publishing video game developers do use Twitter to market their games.

Negar Rostamzadeh, Ben Hutchinson, Christina Greer et al.

Testing practices within the machine learning (ML) community have centered around assessing a learned model's predictive performance measured against a test dataset, often drawn from the same distribution as the training dataset. While recent work on robustness and fairness testing within the ML community has pointed to the importance of testing against distributional shifts, these efforts also focus on estimating the likelihood of the model making an error against a reference dataset/distribution. We argue that this view of testing actively discourages researchers and developers from looking into other sources of robustness failures, for instance corner cases which may have severe undesirable impacts. We draw parallels with decades of work within software engineering testing focused on assessing a software system against various stress conditions, including corner cases, as opposed to solely focusing on average-case behaviour. Finally, we put forth a set of recommendations to broaden the view of machine learning testing to a rigorous practice.

Sarah Spiekermann, Matthias Rothensee, Michael Klafft

Purpose: In 2009, US coupons set a new record of 367 billion coupons distributed. Yet, while coupon distribution is on the rise, redemption rates remain below 1 percent. This paper aims to show how recognizing context variables, such as proximity, weather, part of town and financial incentives interplay to determine a coupon campaigns success. Design/methodology/approach. The paper reports an empirical study conducted in co-operation with a restaurant chain 9.880 Subway coupons were distributed under different experimental context conditions. Redemption behavior was analyzed with the help of logistic regressions. Findings: It was found that even though proximity drives coupon redemption, city center campaigns seem to be much more sensitive to distance than suburban areas. The further away the distribution place from the restaurant, the less does the amount of monetary incentive determine the motivation to redeem. Practical implications. When designing a coupon campaign for a company, coupon distribution should not follow a -- one is good for all strategy -- even for one marketer within one product category. Instead each coupon strategy should carefully consider contextual influence. Originality/value. This paper is the first to the authors knowledge that systematically investigates the impact of context variables on coupon redemption. It focuses on context variables that electronic marketing channels will be able to easily incorporate into personalized mobile marketing campaigns.

Mengting Wan, Jianmo Ni, Rishabh Misra et al.

Modern collaborative filtering algorithms seek to provide personalized product recommendations by uncovering patterns in consumer-product interactions. However, these interactions can be biased by how the product is marketed, for example due to the selection of a particular human model in a product image. These correlations may result in the underrepresentation of particular niche markets in the interaction data; for example, a female user who would potentially like motorcycle products may be less likely to interact with them if they are promoted using stereotypically 'male' images. In this paper, we first investigate this correlation between users' interaction feedback and products' marketing images on two real-world e-commerce datasets. We further examine the response of several standard collaborative filtering algorithms to the distribution of consumer-product market segments in the input interaction data, revealing that marketing strategy can be a source of bias for modern recommender systems. In order to protect recommendation performance on underrepresented market segments, we develop a framework to address this potential marketing bias. Quantitative results demonstrate that the proposed approach significantly improves the recommendation fairness across different market segments, with a negligible loss (or better) recommendation accuracy.