Rank Product Research Articles

Formulation and Evaluation of Lactose-Free Probiotic Milk Beverage with Added Dried Figs

Digestive issues have become widespread due to factors such as imbalanced nutrition and the increase in fast-food consumption. The foods which are available to meet the daily probiotic consumption of individuals are limited. To overcome this nutritional need through the consumed foods, a newly formulated food product called "lactose-free milk beverage with dried fig and probiotic supplements" has been developed. It is formulated with lactose-free milk to be suitable for consumption by all individuals. Dried figs produced and exported in our country are functional ingredients not only for their nutritional values but also for their natural prebiotic effect. In addition to the prebiotic role of dried figs, Bacillus clausii bacteria has been used to impart probiotic properties to the product. Different ratios of dried fig puree and probiotic ratio were studied, and in vitro digestion analyses were conducted to prove the probiotic effect. Sensory evaluation of the novel functional milk beverage was also carried out. According to the preferences of a total of 60 panelists, the product ranking results indicated that the developed formulation enriched with probiotics and dried fig was the most preferred sample. Furthermore, during the storage of the developed lactose-free milk beverage with dried fig and probiotic supplements at 4°C, color, total bacteria count, and probiotic bacteria count were determined. The research provided a new functional product designed to support individuals with digestive comfort, promote an increased probiotic population in the intestines, and address concerns related to lactose intolerance.

Studi Komparasi Regulasi Sertifikasi Halal oleh Majelis Ulama Indonesia (MUI) dan Badan Penyelenggara Jaminan Produk Halal (BPJPH)

Halal products are increasingly in demand by many people, not only in the national market but also in the international market. Indonesia has experienced an increase in the consumption rank of halal products from year to year. One of the indicators is the implementation of halal certificates. Halal certificates cannot be issued just like that without procedures and checks from the authorities. However, looking at the dynamics, halal certificates have experienced developments from time to time. These changes can be seen after the existence of Law No. 33 of 2014 concerning Halal Product Guarantee. The formulation of this problem is how is the comparison between the regulation of halal certificates by MUI and BPJPH? The purpose of this study is to determine the comparison of halal certificate regulations by MUI and BPJPH, namely before and after the issuance of the JPH Law. This research uses the Library study method with various sources that support the object of research. The primary data source of this research is Law No.33 of 2014, MUI Fatwa, and its official website. Secondary data sources are from journals, theses, and related theses. The result of this research is that there are similarities and differences between the regulation of halal certificates by MUI and BPJPH. The difference is not only in the content of the regulation, but also in the halal logo issued. This logo is not just a picture, but more than that, it has a deep philosophy. Keywords: BPJPH, Halal, Certificate, MUI Halal products are increasingly in demand by many people, not only in the national market but also in the international market. Indonesia has experienced an increase in the consumption rank of halal products from year to year. One of the indicators is the implementation of halal certificates. Halal certificates cannot be issued just like that without procedures and checks from the authorities. However, looking at the dynamics, halal certificates have experienced developments from time to time. These changes can be seen after the existence of Law No. 33 of 2014 concerning Halal Product Guarantee. The formulation of this problem is how is the comparison between the regulation of halal certificates by MUI and BPJPH? The purpose of this study is to determine the comparison of halal certificate regulations by MUI and BPJPH, namely before and after the issuance of the JPH Law. This research uses the Library study method with various sources that support the object of research. The primary data source of this research is Law No.33 of 2014, MUI Fatwa, and its official website. Secondary data sources are from journals, theses, and related theses. The result of this research is that there are similarities and differences between the regulation of halal certificates by MUI and BPJPH. The difference is not only in the content of the regulation, but also in the halal logo issued. This logo is not just a picture, but more than that, it has a deep philosophy.

Use of multiple reference data sources to cross-validate gridded snow water equivalent products over North America

Abstract. We use snow course and airborne gamma data available over North America to compare the validation of gridded snow water equivalent (SWE) products when evaluated with one reference dataset versus the other. We assess product performance across both non-mountainous and mountainous regions, determining the sensitivity of relative product rankings and absolute performance measures. In non-mountainous areas, product performance is insensitive to the choice of SWE reference dataset (snow course or airborne gamma): the validation statistics (bias, unbiased root mean squared error, and correlation) are consistent with one another. In mountainous areas, the choice of reference dataset has little impact on relative product ranking but a large impact on assessed error magnitudes (bias and unbiased root mean squared error). Further analysis indicates the agreement in non-mountainous regions occurs because the reference SWE estimates themselves agree up to spatial scales of at least 50 km, comparable to the grid spacing of most available SWE products. In mountain areas, there is poor agreement between the reference datasets, even at short distances (< 5 km). We determine that differences in assessed error magnitudes result primarily from the range of SWE magnitudes sampled by each method, although their respective spatiotemporal distribution and elevation differences between the reference measurements and grid centroids also play a role. We use this understanding to produce a combined reference SWE dataset for North America, applicable to future gridded SWE product evaluations and other applications.

The size of higher education, R&D expenditure, academic documents and international rankings

This study explored the correlations among the number of higher education institutions, R&D expenditures, international academic publications, and international rankings in Taiwan on an annual basis. The case study is based on Taiwan's 1996 education reform policy, which allowed the establishment of new universities and colleges, followed by a decline in birth rates affecting the educational environment. The study covers the period from 1996 to 2023, spanning 28 years. Data were obtained from the Scientific Journal Rankings (SJR) by the Scimago Journal & Country Rank and statistics published by local authorities. Spearman's rank correlation coefficients revealed a significant relationship between the number of higher education institutions and improvements in international rankings, as evidenced by the increase in international academic publications. This finding underscores the crucial role of universities and colleges in enhancing international rankings and academic output. The strong positive correlation between R&D expenditure and academic output further emphasizes the importance of research funding. However, the regressive effect on the country's rankings suggests a need for more effective allocation of R&D funds. The Granger causality tests highlighted the bidirectional feedback between changes in academic production and international rankings. This finding underscores the dynamic nature of academic performance and rankings and the potential impact of R&D expenditure on these outcomes. This highlights the need for higher education institutions to strengthen industry-academia cooperation models, diversify their financial resources, and increase R&D investment. By optimizing the use of research personnel and expanding research departments, institutions can mitigate the negative impact of a declining number of institutions on academic performance.

A Novel Product Ranking Approach Considering Sentiment Intensity Distribution of Online Reviews

Online reviews of products have a significant impact on consumers' purchasing decisions, making it important for both platform retailers and consumers to rank products, and eventually purchase products. With respect to the problem of product ranking that consists of the information contained in online reviews; by considering the sentiment intensity distribution of online reviews, we establish a fine-grained sentiment intensity analysis and then exploit grey incidence analysis and TOPSIS to establish a multi-attribute approach for product ranking. Finally, a case study of laptop purchases verifies the applicability and effectiveness of the proposed approach.

Evaluation of wearable device technology in terms of health and safety in firefighters

Objectives Firefighting is one of the tasks that is physically difficult to perform and carries a high risk of injury and death. A better understanding of the underlying factors that influence the causes of fire scene injuries can improve firefighters’ safety. For this reason, the study aimed to determine the importance of Smart Personal Protective Equipment and wearable technology in protecting the health and safety of firefighters by using them instead of traditional equipment and systems. According to expert opinions and literature reviews, the dangers faced by firefighters have been determined to be thermal, physical, biological, environmental, and chemical. Methods Analytical Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) methods were used in the study. The AHP method was preferred because it is a systematic decision-making method that includes both ranking and comparison techniques. The PROMETHEE method was preferred because it provides the opportunity to make effective decisions in a very short time by basing the decision-making process on a scientific basis. In addition to the graphical representation of the ranking of alternatives, it offers decision-makers the opportunity to make various statistical analyses. Results The weights of the hazards were calculated using the AHP method. Physical hazards accounted for the highest weight. PROMETHEE was used in the ranking of wearable smart technological products to protect the health and safety of firefighters. Conclusions Products are listed as Personal Protection System, PROeTEX PPE, Wearable IoT Device, Flame Resistant Shirt, Fall Detection Systems, Smart Wearable Underwear, and WASP. With the study, it was concluded that the risk of firefighters being trapped would play an essential role in the prevention of death and injury. Improvements in wearable technological products used in the fire department will yield better results and increase safety.

Evolutive multi-attribute decision making with online consumer reviews

In the digital age, the sheer volume of online consumer reviews imposes a cognitive burden on consumers, complicating their purchasing decisions. Many studies have integrated consumer opinions to provide consumers with clear and concise information. However, these studies often prioritize mainstream opinions, overlooking the diversity and timeliness of other important perspectives. To address this challenge, we propose an evolutive decision-making method. Firstly, we propose an attribute rating evolution algorithm to address the online reviews based on the iterative self-organizing data analysis technique and time decay. This algorithm enables real-time analysis of the diverse opinions expressed in review data. Then, taking into account consumer attribute preferences and decision-making psychology, we formulate multiple product ranking strategies to offer personalized decisions based on the evolutive opinions. Our method decreases the bias towards review quantity, ensuring that significant opinions are not overshadowed by more frequent ones. Through data experiments and an application on OpenTable.com, we demonstrate that our method can provides effective decision recommendation for consumers.

Product ranking considering differences across online review platforms: a method based on intuitionistic fuzzy soft sets

Numerous online review platforms have become flooded with a large number of textual reviews, allowing consumers to compare and select products based on online reviews from multiple platforms. Existing studies have found that there may be certain differences in the content focus of online reviews from different platforms and that the evaluation indicators of different platforms may be different. Ranking products by considering differences in platforms and integrating online reviews from multiple platforms is of great significance in supporting consumer purchase decisions; however, relevant studies are relatively scarce. Therefore, this study proposes a product ranking method using intuitionistic fuzzy soft sets, considering the differences in platforms. First, online reviews were preprocessed to obtain product indicator parameters on different platforms. Second, a sentiment analysis algorithm was used to determine the sentiment strengths of the indicator parameters. Subsequently, an intuitionistic fuzzy soft set for each platform concerning the indicator parameters was constructed. Furthermore, a comprehensive intuitionistic fuzzy soft set was obtained by the AND operation, and the ranking of the products was determined. Finally, a case study was conducted to illustrate the use of the proposed method. Through a comparative analysis, the characteristics and advantages of the proposed method were further illustrated.

Bibliometric Analysis of Educative Communication Habituation: An Efforts in Shaping the Morals of Learners in Indonesia

This research was conducted in March 2024 through a search of the Google Scholar database with the title Educational Communication. Specifically, the maximum number of results was 500 articles as a sample, and the publication range was the last 10 years, starting from 2014-2024. Metadata retrieval using the Publish or Perish (PoP) application version 8.12 was then analyzed descriptively based on the year of publication, publication source, researcher productivity, and journal rank. To get an accurate map of research development, PoP data is exported to the PoPCites format, which will then be mapped simply using Excel and Result as RIS. PoPCites data is mapped by researchers, who are entered into the previously created table, and RIS data is analyzed using the VOSViewer (VV) application version 1.6.20. The results showed that the publication of research results on educational communication from 2014-2024 experienced fluctuations, and the largest source of publication was through e-journals, with as many as 406 articles. While the researchers published their research results 4 people, namely Rr. Tutik Sri Hariyati (4 articles), Ratih Kurniasari (15 articles), Hadi Pratomo (4 articles), and Heni Pujiastuti (6 articles). The VV visualization application shows that the development map of educational communication research consists of 5 clusters. Cluster 1 (5 items), cluster (3 item), cluster 3 (2 items), cluster 4 (2 items), and cluster 5 (2 items) with the most research including literature review, Indonesian systematic literature review, and systematic review.

PENERAPAN METODE WASPAS DAN ROC DALAM PEMILIHAN BANK TERBAIK UNTUK PELAYANAN MOBILE BANKING

Technological developments encourage increased use of banking services via mobile devices. Choosing the optimal bank for mobile banking services is an important challenge in meeting customer needs. This research uses a Decision Support System with the application of the Weighted Aggregated Sum Product Assessment (WASPAS) and Rank Order Centroid (ROC) methods. The WASPAS method helps find positive and negative ideal solutions in solving problems. The ROC method was chosen to reduce the level of subjectivity and uncertainty in determining weights. The weights in ROC are determined based on priority criteria. In this research, 5 alternatives are used, namely BCA Mobile, Livin' by Mandiri, BSI Mobile, BNI Mobile Banking, BRImo BRI, and apply criteria such as security, many enthusiasts, user friendly, rating, and cost. This research aims to provide a comprehensive picture of the quality of mobile banking services provided by various banks so as to help customers choose mobile banking according to the criteria they need. The research results show that BCA Mobile owned by BCA bank is the best alternative with the highest value of 0.940, followed by BRImo with a value of 0.613, BNI Mobile with a value of 0.612, Livin' by Mandiri 0.611, and the lowest value of 0.323 for BSI Mobile. Thus, BCA Mobile is declared the best choice for mobile banking services.

Algorithmic agencing in platform markets

Algorithms are central in shaping platform markets, impacting not only calculation processes but also influencing market actors’ relationships and market structures more broadly. As “autonomous” market devices that are executable or operate without the need for human intervention, algorithms continuously organize, prioritize, and rank product profiles, resulting in personalized exchange offers based on data processing. However, different market actors also actively attempt to influence these processes, turning platform markets into what could be described as algorithmic battlefields. Despite the growing significance of market-based algorithms, research on their role in market shaping remains scarce. This paper explores the role of algorithms in shaping platform markets. By drawing together literatures from market studies and critical algorithm studies, four key algorithmic characteristics in “agencing” markets are identified: as market actors, targets of market contestations, boundary objects, and market systems connectors. Based on these roles, the paper formulates a research agenda for studying algorithmic agencing in platform markets.

Research on Improving the Accuracy of SIF Data in Estimating Gross Primary Productivity in Arid Regions

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of “carbon neutrality” in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted “U” shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals.

Dynamic grey relational analysis-based supplier selection in a health-care unit

Purpose In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem. Design/methodology/approach In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores. Findings Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A2 and A11 occupy the top two positions having almost the same performance with respect to the considered financial metrics. Originality/value Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

Large-Scale Satisfaction Rating-Driven Selection of New Energy Vehicles: A Basic Uncertain Linguistic Information Bonferroni Mean-Based MCGDM Approach Considering Criteria Interaction

The continuous revolution of new energy technologies and the introduction of subsidy policies have promoted green consumers’ willingness to purchase new energy vehicles and automotive online service platforms have disclosed vehicle reputation and consumer satisfaction ratings information. However, due to issues such as uncertain data quality, large data volumes, and the emergence of positive reviews, the cost for potential car buyers to acquire useful decision-making knowledge has increased. Therefore, it is necessary to develop a scientific decision-making method that leverages the advantages of large-scale consumer satisfaction ratings to support potential car buyers in efficiently acquiring credible decision-making knowledge. In this context, the Bonferroni mean (BM) is a prominent operator for aggregating associated attribute information, while basic uncertain linguistic information (BULI) represents both information and its credibility in an integrated manner. This study proposes an embedded-criteria association learning BM operator tailored to large-scale consumer satisfaction ratings-driven scenarios and extends it to the BULI environment to address online ratings aggregation problems. Firstly, to overcome the limitations of BM with weighted interaction (WIBM) when dealing with independent criteria, we introduce an adjusted WIBM operator and extend it to the BULI environment as the BULIWIBM operator. We discuss fundamental properties such as idempotence, monotonicity, boundedness, and degeneracy. Secondly, addressing the constraints on interaction coefficients in BM due to subjective settings, we leverage expert knowledge to explore potential temporal characteristics hidden within large-scale consumer satisfaction ratings and develop a method for learning criteria and interaction coefficients. Finally, we propose a conversion method between user credibility-based ratings and BULI. By combining this method with the proposed adjusted BM operator, we construct a multi-criteria group decision-making (MCGDM) approach for product ranking driven by large-scale consumer satisfaction ratings. The effectiveness and scientific rigor of our proposed methods are demonstrated through solving a new energy vehicle selection problem on an online service platform and conducting comparative analysis. The case analysis and comparative analysis results demonstrate that the interaction coefficients, derived from expert knowledge and 42,520 user ratings, respectively, fell within the ranges of [0.2391, 0.7857] and [0.6546, 1.0]. The comprehensive interaction coefficient lay within the range of [0.4674, 0.7965], effectively mitigating any potential biases caused by subjective or objective factors. In comparison to online service platforms, our approach excels in distinguishing between alternative vehicles and significantly impacts their ranking based on credibility considerations.

Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking

Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking

Forecasting synergistic pathways between rare earth elements, renewable energy, and product and economic complexities in achieving a low-carbon future

Recent decades have witnessed an unprecedented transformation in the global energy landscape, driven by environmental concerns and the quest for sustainable economic growth. As the world grapples with the urgent need for decarbonization, the utilization of renewable energy technologies with the instrumental role of rare earth elements (REEs) has come to the forefront. However, empirical investigations into their synergistic pathways for product and economic complexities concerning achieving a low-carbon future remain scarce. Therefore, we forecast synergistic pathways between the REE supply, renewable energy, economic and product complexities, and GDP growth using a panel dataset of 11 REE-producing countries from 1990 to 2023. We used Common Correlated Effects and Temporal Causal Models as primary methods to estimate panel long-run elasticities and subsequently forecast mutual causal synergies between the variables. The results indicated that REE supply led to renewable energy and economic growth that further elevated the countries’ product and economic complexities rankings. GDP growth increased REE production, economic complexity, and renewable energy directly, and consequently, product complexity and REE production through them. This underscores the positive role of REE production coupled with renewable energy technologies in achieving a low-carbon future based on economic diversification, enhanced industrial capabilities, and technological sophistication.

Global research trends on systemic lupus erythematosus and thyroid cancers (1964-2023): A scientometric and visualized study.

Over the past 3 decades numerous studies have reported an association between systemic lupus erythematosus (SLE) and thyroid cancers. However, there has been no scientometric analysis in this area of research. To perform a comprehensive scientometric analysis of the global literature published on the association between SLE and thyroid cancers. The data on publications within thyroid cancers in SLE patients were retrieved from the Scopus database using a defined search strategy from its first publication in 1964 to 2023. To conduct a collaboration mapping analysis among keywords, authors, journals, and territories, VOSviewer was utilized. Our final research resulted in 246 scientific publications with 8072 citations, which were published in 198 journals affiliated to 48 countries. A global upward trend has been observed in the last 20 years, with the highest number of publications in the year 2022 (n = 28; 11.4%). The United States led the global productivity ranking with 74 publications (30.1%), followed by China with 25 publications (10.2%). The most popular journals in this field were "Arthritis Research and Therapy" and "Frontiers in Endocrinology," while the most co-cited journal was "Autoimmunity Reviews." The top 3 most prolific authors were Bernatsky, S., Clarke, A.E., and Ramsey-Goldman, R with 9 publications each. This first scientometric study comprehensively offered an overview of the status of thyroid cancers in SLE patients, assessing scholarly productivity in this domain over a period of 50 years.

Evolution of Pediatric Critical Care Medicine Physicians Clinical and Academic Profile by Gender.

To examine career trajectory and academic profile of Pediatric Critical Care Medicine (PCCM) physicians, with special focus on gender differences. Observational cross-sectional study of PCCM fellowship graduates using publicly available data. Publicly available databases including National Provider Identifier registry, American Board of Pediatrics, Doximity, official hospital websites, and Scopus. Two thousand one hundred twenty-nine PCCM fellowship graduates employed in clinical positions in U.S. practice locations. None. Physician demographic characteristics included gender and time since fellowship completion. Current career and academic characteristics included employment data, publication productivity (h-index), and academic rank. Data from 2129 career PCCM physicians was included, with recent graduates showing a notable increase in female representation. Workplace characteristics revealed that most PCCM physicians worked in university-affiliated hospitals, with higher percentages of female physicians working in university-affiliated hospitals compared with male physicians. The study also highlighted significant gender disparities in academic metrics, with male physicians having higher h-indices (3 vs. 2; p < 0.001) and more publications (6 vs. 4; p < 0.001) than their female counterparts across various career phases. Additionally, the analysis showed gender differences in academic rank, with a higher proportion of female faculty holding assistant and associate professor ranks (58.2% vs. 47.5%; p < 0.001), while a greater percentage of male faculty held the rank of professor (20.2% vs. 11.1%; p < 0.001). Multiple variable regression analysis identified both male gender and time since fellowship graduation as independently associated with a physician's h-index, while only time since fellowship graduation was linked to current academic rank. This is the first analysis of career and academic characteristics of practicing PCCM physicians, additionally studying the association of gender and career trajectory. Gender discrepancy was seen in employment hospital characteristics, h-indices, and academic rank. Additional studies are required to further explore the impact of gender on career trajectory.

E–WOM and app stores in Iran: how online reviews influence purchase intention of paid mobile apps

PurposeThis study aims to examine the relative importance of the impact of central and peripheral route processing of online reviews (ORs) on the acceptance of paid mobile apps among consumers in a fully Islamic market. The authors used the elaboration likelihood model (ELM) and technology acceptance model (TAM) to explain how ORs influence users’ perception and purchase intention of paid mobile apps.Design/methodology/approachAn online survey of 384 Iranian paid mobile app users was conducted to compare the role of overall product ranking, argument quality and information quantity on the perceived usefulness (PU) and perceived ease of use (PEU) of mobile apps and purchase intentions of paid mobile apps.FindingsResults show that only overall product ranking and argument quality influence perceived PEU and PU of paid mobile apps. The quantity of reviews on the app store does not affect perception of the app. The path model supports the mediating role of PU and PEU on information processing and attitude toward the app and purchase intention.Practical implicationsTo Islamic markets such as Iran, this study shows that paid mobile app marketers need to monitor closely the reviews in the app stores and make prompt response to negative reviews even if there are only a few reviews because users did not pay attention to the quantity of reviews. The overall ranking and argument quality strongly affect perception and purchase intention of paid apps.Originality/valueTo the best of the authors’ knowledge, this is the first study that combines ELM and TAM in determining the relative importance of central and peripheral processing of information in online product reviews, specifically for paid mobile apps in app stores in a large Islamic market.

Sensitivity Analysis of Various AHP Process: A Case Study on Consumption Fish Farming

The utilization of a decision support system has successfully helped many businesses in increasing their product sales. By conducting product evaluations, the sales potential of each product will be seen more accurately, thereby helping strategic decision-makers. As one of the algorithms in product selection, AHP has been proven to solve complex problems involving multi-criteria, as many studies have successfully used it to rank products. However, in AHP implementation there are two different ways of calculating weights and consistency ratios. Due to the various AHP processes available, this paper performs testing with the most frequently used variations to determine product potential and compare the methods for multi-criteria decision-making. The criteria are harvest duration, selling price, feed production, weather conditions, and target market. The research results show that the weights of the two methods are different, but the resulting ranks are the same. The best choice type of fish to be farmed by fish farmers is catfish with the highest weight and the most difficult type of fish to farm is giant gourami. The result also show that the best way of the normalization process is squares of comparison matrices because its sensitivity does not easily change the ranking order.