Research Paper
Data, information and knowledge management in the field of smart business
seyed rasoul hoseini; sahel Farokhian; Hadi Taghavi
Abstract
IntroductionCurrent global statistics indicate that 80% of startups fail within a short period, with one of the primary reasons being weak branding strategies. Startups often lack precise knowledge of branding, which increases the risk of failure. To reduce this risk, marketers need a phenomenon called ...
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IntroductionCurrent global statistics indicate that 80% of startups fail within a short period, with one of the primary reasons being weak branding strategies. Startups often lack precise knowledge of branding, which increases the risk of failure. To reduce this risk, marketers need a phenomenon called co-creation branding.Branding in startups can increase access to suppliers, customer purchases, and innovative business models (Drakoulis & Lipovsek, 2015). Despite these advantages, startups face challenges such as gaining consumer trust, creating demand for their products and services, establishing an identity, and providing unique and differentiated value to consumers (Sonja et al., 2022). Therefore, to reduce these challenges and the risk of failure, marketers need co-creation branding in startups (Bonamigo et al., 2022). Co-creation branding involves active customer and company participation and interaction to improve brand image, increase brand value and awareness, and ultimately increase customer loyalty, achieving a competitive position in the market (Dehdashti Shahrokh et al., 2022).Unfortunately, very few studies have been conducted on both co-creation branding and startups, and extensive research is needed (Wong & Merrilees, 2005; Lagerstedt & Mademlis, 2016). Therefore, this research aims to identify the factors affecting on co-creation branding in startups. The main question of this research is defined as follows: What are the factors affecting on co-creation branding in startups?Literature ReviewThe literature review of startups offers various definitions for the term. For instance, Avnimelech and Teubal (2006) define startups as young companies with advanced technology whose primary activity, from idea to initial sales, lasts between one to five years.Brand co-creation is a recent trend in branding (Hatch & Schultz, 2010), which is largely based on the dominant logic of service (Vargo & Lusch, 2008) and co-creation of value (Prahalad & Ramaswamy, 2004), starting with the identification of customer value creation processes (Juntunen, 2012). Co-creation leads to offering more suitable products and services to consumers and encouraging their participation (Nadeem et al., 2020). The theory of brand co-creation assumes that the consumer is no longer a passive brand buyer but desires and seeks active participation in creating brand experiences (Kamboj et al., 2018), and therefore, customers can play a vital role in determining the success of brands. Brand co-creation begins with the relationship between shareholders and customers (Prahalad & Ramaswamy, 2004; Snyder, 2019), where shareholders define and create their brand identity through this relationship. Finally, it can be said that brand co-creation, in addition to strengthening a company's innovation capability, is also a reliable way to enhance brand relationships (Chang & Hsieh, 2016).Broeke and Paparoidamis (2021) demonstrate in their research that the co-creation of brand value occurs when customers are more sensitive to quality and less sensitive to price, and there is high demand for the product. Under such conditions, product quality is enhanced, and the company's flexibility increases. Nadeem et al. (2020) show in their research that social support affects ethical perception, and both are effective in co-creation. Ethical understanding also has an impact on consumers' trust, satisfaction, and commitment. However, trust and commitment do not have a significant impact on the co-creation of value. Tajvidi et al. (2020) demonstrate that concerns about privacy can disrupt the effects of brand co-creation, and social support, quality of relationships, and information sharing on social media have a positive impact on consumers' intention to co-create brand value on social media. Additionally, there is a meaningful relationship between customer participation in brand communities on social media and the quality of the relationship.MethodologyThis study is objective in nature and employs a qualitative approach. Its aim is to identify the factors that affect co-creation branding in startups. To achieve this, a meta-synthesis approach is used to examine existing articles in the field and extract the relevant factors. The statistical population of the research is credible and relevant articles published between 2007 and 2022 (a 15-year time span). Meta-synthesis involves reviewing previous studies and reframing concepts through interpretive integration of previous results. In this research, the seven-stage Sandelowski & Barroso (2006) method is used to carry out the meta-synthesis, as it is the most commonly used method for meta-synthesis in recent university research studies.ResultsThis research conducted a systematic review of 41 research studies to identify the factors influencing co-branding in startups. The meta-synthesis method was used to analyze the research literature. After studying and extracting text, key codes were clustered using MAXQDA software and organized into concepts and components. Ultimately, the factors influencing co-branding in startups were extracted and classified into four themes, eight concepts, and 33 distinct codes. These themes include environmental factors (financial and social factors), strategic brand management factors (brand value and brand creation), marketing factors (promotional activities and customer-related factors), and individual entrepreneurial factors (entrepreneurial personal characteristics and entrepreneurial skills).Discussion and ConclusionThe objective of this research is to identify the factors that influence co-branding in startups using a meta-synthesis method. To accomplish this objective, the scattered factors mentioned in various studies and case studies in this field were collected and classified into similar categories as concepts and themes using the meta-synthesis method and following the seven steps proposed by Sandelowski and Barroso. Startups can fulfill their responsibility and duty to society by engaging in activities that help the community, which has a significant impact on co-branding in the startup ecosystem (Kennedy & Guzman, 2016). Moreover, the social position of companies has been shown to influence co-branding (Twrsnick, 2016; Kennedy & Guzman, 2016). The availability of financial resources has a critical impact on co-branding activities in startups. Financial performance in this context refers to the extent to which the resources under the company's control generate profitability, which is vital for accepting and developing co-branding programs in the future. Therefore, it is considered one of the influential factors (Hatch & Schultz, 2010; Huang & Lai, 2011; Todor, 2014; Setiyati & Wijaya, 2015; Du Plessis et al., 2015; Tavares, 2015; Twrsnick, 2016; Kennedy & Guzman, 2016). The process of brand creation refers to a set of factors that lead to the development of a brand, encompassing brand design, brand strategy, brand identity, brand positioning, and brand objectives. These factors have been examined in most studies conducted in this area (Spence & Essoussi, 2008; Bresciani & Eppler, 2010; Bergström et al., 2010; Huang & Lai, 2011; Dai & Pietrobon, 2012; Sonja et al., 2022). Understanding the value-creating factors of a brand is a requirement for creating a strong brand. A brand's value is defined as a set of assets related to the brand name and company symbol that depend on the name or symbol of a brand and the increase in value created by the company's products or services. The value-creating factors of a brand include brand awareness, perceived brand quality, brand associations, brand image, brand experience, brand value, brand trust, brand commitment, and brand love (Boyle, 2007; Carvalho, 2007; Spence & Essoussi, 2008; Hamidi et al., 2021; Sonja et al., 2022; Bahagir et al., 2022). Promotional activities are all actions taken to raise awareness and persuade customers and the target audience to use a product or service and represent the fourth element of the marketing mix (Hagili et al., 2017; Kamboj et al., 2018; Rialti et al., 2018; Tajvidi et al., 2020; Sonja et al., 2022; Bahagir et al., 2022). To implement and execute the co-creation approach, companies create their own channels to establish connections with customers, which is essentially the fundamental aspect of co-creation, involving individuals' participation in creating valuable experiences together. By employing this approach, companies cause customers to feel a sense of belonging to the brand and develop loyalty towards the brand (France et al., 2015; Setiyati & Wijaya, 2015; Du Plessis et al., 2015; Twrsnick, 2016; Kauffman et al., 2016). Previous research has shown that the personal characteristics and traits of entrepreneurs have an impact on their success and the success of their startup companies. Therefore, knowledge and experience play a significant role in branding, and many entrepreneurs have been able to use their previous knowledge and experience to pave the way for their future (Carvalho, 2007; Juntunen, 2012; Tavares, 2015; Lagerstedt & Mademlis, 2016; Twrsnick, 2016; Giannopoulos et al., 2021). The role of entrepreneurs in guiding and integrating the branding approach in startup companies has been emphasized in previous studies, which can be achieved in line with the innovation of entrepreneurs (Spence & Essoussi, 2008; Payne et al., 2009; Tavares, 2015; Setiyati & Wijaya, 2015; Twrsnick, 2016; Giannopoulos et al., 2021). Hence, it is advisable for entrepreneurs to place significant emphasis on networking and bolstering their social networks, as well as improving communication with their customers, to foster increased and superior engagement with them, and ideally, to capitalize on enhanced brand credibility. In this regard, startup firms can enhance and expedite their brand acceptance process by encouraging customers to partake in and collaborate on the branding process through co-creation. Moreover, considering the frequent reiteration of brand identity in numerous studies, it is recommended that startup company executives devote greater attention to establishing and reinforcing brand identity in the minds of customers. : Brand, Branding, Co-Creation, Start-Up, Meta-Synthesis
Research Paper
Data science, intelligence and future analysis
Elmira Darzi; Mehrdad Agha Mohammad Ali Kermani; Mostafa Jafari
Abstract
Due to their temporary nature and precise time and cost planning, project organizations are more involved in the relationship between data and operational processes, which requires the correctness of the actual processes of the organization. On the other hand, one of the essential issues for managing ...
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Due to their temporary nature and precise time and cost planning, project organizations are more involved in the relationship between data and operational processes, which requires the correctness of the actual processes of the organization. On the other hand, one of the essential issues for managing project-oriented organizations is its business process management, but due to the dynamic behavior and complexity of the nature of a project-oriented organization, identifying the processes through the traditional modeling of business process management is not reliable. The emerging solution to this problem is called "process mining." The paper introduces a framework that employs accurate process identification to measure the performance of business units relative to reality. This comprehensive framework undertakes the prerequisite steps of identification, including monitoring and cleaning the process-aware information systems' data to discover the process's current state and examine it from different perspectives based on the selected process. The primary purpose of this paper is to develop a framework for improving the P2P process in Chavosh Rah Company through process mining. The paper presents a framework to enhance the P2P process in project-oriented organization by implementing and extracting knowledge from the process, discovering unexpected and hidden relationships, and finding bottlenecks by employing process mining.
Introduction
Today, organizations must identify and manage their current processes for an effective approach. Workflow management systems are used to support business processes. Although current workflow management systems support the design, configuration, execution, and control of the processes under their control, there are deficiencies in the troubleshooting phase. Process mining is used to fill these gaps. Process mining is a bridge between data science and process science. The main aspects of process mining are the "discovery, monitoring, and improvement of real processes by extracting knowledge from event information" that is accessible in today's systems.
By evaluating real behaviors, process analysis provides a realistic view of operational processes, which is useful and important in developing support systems or redesigning previous processes. The purpose of process mining is to extract non-obvious and practical information related to processes from the event graph. The event log is actually the recorded data related to the events of the execution of a business process in an organization. One of the most important characteristics of an event diagram is that it is formed based on the events that happen. This means that regardless of how an organization's business process is planned or designed, the event graph contains data on how the process is implemented in reality.
Applications of process mining have been covering articles in the fields of health, information technology, finance, education, government affairs, energy, agriculture, logistics, public relations, media, and tourism. The purchase request process with the process analysis approach in the project organization is the innovation center of this article because no research has been done in line with this point of view. Of course, this article is a scientific and practical project. Naturally, the analyzes and results are based on the real data of each organization, which is usually different from other organizations, but by doing such a project, the obtained results can be generalized for organizations that have similar performance.
After the preparation of the event diagram, it is possible to define the APQC-approved relevant indicators in parallel with the start of the process analysis and analyze the organization from the perspective of these indicators. Then, with the help of interviews with the organization's experts who are involved in the purchasing process, improvement suggestions are collected and announced to the organization's management unit. The case study in this article is about the purchasing process of a contracting company. Chavosh Rah Bana Company was established in order to implement infrastructure projects in the fields of road construction, construction, and facilities. Shopping in Chavosh Rah Bana company includes the steps of registering a request, checking the request, checking the warehouse by the warehouse of the available goods, requesting a non-existent purchase, asking the price by the procurement unit, management approval, choosing the payment method and issuing a valid check or purchase, and finally registering a debt or registration It is creditable.
Research Question(s)
In this article, the following questions are raised, which we will try to answer by advancing the goals of the article had:
1) Does the mining process have a direct impact on the purchase request process?
2) Is time optimization effective in planning based on process analysis?
3) Is there a logical and acceptable answer in planning based on the use of real data? Will we reach the mining process?
4) Which is the most common path in the process?
5) In what order are the items (cases) distributed in the process?
6) How much do the cases conform to the process model? What problems are there?
7) What is the average/minimum/maximum operation time of the process?
8) Which of the tasks takes more time?
9) How are the cases actually implemented?
Literature Review
In the field of the purchasing process, two articles were studied, which are related to 2019 and 2018. The first article with the topic "Using process mining to find the main factors of delay in the internal purchasing process" was prepared by Virginia Eitzel Contras, Jesus Andres Portillo, and Fernando Gonzalez. In this article, the internal purchasing process of Quintal company was investigated. The software used in this article is Fluxicon Disco software. In this article, 608 cases (9199 events) were analyzed. The purpose of this paper was to increase the efficiency of Quintal's internal purchasing department through recommendations based on the analysis of their process reports.
The second paper "Process Mining Analysis of Purchasing Process in a Heavy Manufacturing Industry" was prepared by Chiwon Chu and Hind Rebigid. In this article, the purchasing process in a marine and ship parts manufacturing company in Korea was investigated. The software used in this article is Fluxicon Disco software. In this article, 663 cases (9829 events) were analyzed. This article identified the activities in which the process consumes a lot of time and also rework occurs in them.
In the review article on the application of process mining by Dakik et al., a review of the researches conducted on the subject of the applications of process mining until 2018 was done and the result was that the main use of process mining was in the fields of health, information technology, finance, production and It is education.
In 2018, Baykazoglu et al. published an article entitled "An approach based on process analysis to evaluate students' performance in computer tests". In this article, by tracing the logs of the students' journeys on the computer, the process of answering them has been discovered and analyzed.
The first study that used process mining to explore and analyze an inter-organizational process was conducted by VanderAalst in 2000. During this research, workflows between different organizations were modeled and analyzed. After that, an article on supply chain processes in the field of discovery of distribution processes in the supply chain was done by Maroster et al. in 2003.
In 2009, Garek et al. analyzed the RFID-oriented supply chain process. In this supply chain, the position of each item is tracked by its special code, and this makes it possible to get the most out of the mining process.
In 2014, Bernardi et al. discovered inter-organizational business rules through the data available in cloud data and by process mining. In 2014, Klaze et al. presented research on the integration of the event diagram of several different organizations to start process analysis.
Many researches have been conducted on the application of process mining for the three main actions of discovery, compliance review, and improvement. The literature review of this section includes all the books and articles published in the journal and some theses that have accurately used the words process analysis and performance or efficiency in their title. The first time that process mining has been introduced as a performance measurement methodology, Park et al. compared 19 block production processes in a Korean shipbuilding company by DEA. The main contribution and goal of their research is the development of one of the DEA models, and they used automatic process analysis results only to measure the 5 performance indicators they considered. The review goes under these subheadings.
In 2015, a part of Leer et al.'s book was published in Germany called Process Performance Evaluation. In this section, the process performance evaluation procedure is described as a part of the BPM cycle by introducing the generalities of process analysis and DEA along with an application example. Then in the same year in 2016, in his senior thesis at the University of Eindhoven in the Netherlands, van den Ing measured the performance of different paths of purchase-to-payment process in an organization.
Many articles have been published in the field of health in this regard. In 2019, Rojas et al. analyzed the performance of emergency room departments to help decision-makers improve the quality of medical center services. Also, using a case study of process mining, by extracting data from a hospital information system, Bettinni et al. The performance of this system was evaluated using the time indicators available in the process analysis tool. In 2020, Anastasia Pika and colleagues studied process mining to protect the privacy of people's information recorded in healthcare and analyzed data privacy and application requirements for healthcare process data.
In the field of the food industry, in 2021, Mathew Mastella investigated the process of mining in this industry. Also, in 2020, Peyman Badakhshan and his colleagues investigated the purchase order process with the help of mining in the paint industry.
Methodology
The main methodology proposed in this article is briefly and clearly presented in Figure 1. As can be seen, the access to the raw data available in the current software in the company is the starting point of this article. After that, the image of the event, which is considered the input of any process mining tool, should be extracted by monitoring the raw data of the systems, so that various process mining techniques can be applied to it. Discovery and analysis of the process in order to see the details of the process paths in the studied period by Behfaleb software is the next step. After preparing the event diagram, in parallel with the start of the process analysis, the relevant APQC-approved indicators can be defined and the organization can be analyzed from the perspective of these indicators. Then, with the help of interviews with the organization's experts who are involved in the purchasing process, improvement suggestions are collected and announced to the organization's management unit.
Figure 1. Methodology
Conclusion
In this article, it is focused on the application of process mining in the purchasing process of a project-oriented organization. The competitive conditions have forced contractor companies (project oriented) to manage their processes completely and to get help from strategic and operational tools to improve their performance. In this regard, the main goal of this article is to examine one of the important processes of the project-oriented company (purchasing process). For the case study, the data obtained from the purchase process of Chavosh Rah Bana's project-oriented company has been used. With the help of the obtained data, the purchase process of the company was extracted and analyzed from different perspectives. With the help of these analyses and the review of the time indicators introduced in APQC, suggestions for improvement were presented with the help of the company's expert group. Of course, these suggestions can be used in other project-oriented organizations that have a similar function to this type of organization. The suggestions are as follows:
1) Correct purchase planning
2) Having a vendor list of suppliers with relevant indicators
3) The flow of systemic thinking in the organization
4) Using people with expertise
5) Using the warning system to implement activities on time
6) periodic reporting and timely registration in the system
7) Increasing the number of personnel in the procurement unit
8) Teaching the principles and techniques of negotiation
Acknowledgments
We are very grateful to Behin Sazan Farayand Amin Knowledge Based Company, the developer of the first Iranian mining process tool (Bahfalab) for supporting this research. We also thank Mr. Engin
nization, Purchasing Process.
Research Paper
Data science, intelligence and future analysis
Monireh Hosseini; Elnaz Galavi
Abstract
Community detection is an important topic for social network analysis and is also essential to understanding complex networks structure. In community detection, the goal is to determine the groups in which the group nodes are densely connected to each other. In this research, deep learning techniques ...
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Community detection is an important topic for social network analysis and is also essential to understanding complex networks structure. In community detection, the goal is to determine the groups in which the group nodes are densely connected to each other. In this research, deep learning techniques have been used to control graph data with high dimensions, while presenting a comprehensive and integrated architecture of community recognition methods with deep learning. Community detection classic approaches are suitable for networks with low dimensions. Therefore, the reduction of complex network dimensions is counted as a significant topic in community detection. In this paper, in order to reveal the direct and indirect connections among nodes, first a new similarity matrix of network topology is built. Then, a stacked auto-encoder is designed to decrease dimensions based on unsupervised learning. In order to detect communities, various clustering algorithms are then tested and utilized. Evaluation of the proposed research model is performed by surveying various experiments on standard criteria and six real data sets of Karate, Dolphins, Football, Polbooks, Cora and Citeseer. The proposed method evaluation outcomes show a higher accuracy in the identification of communities in the football data set compared to the twelve proposed algorithms used in past researches, and show a significant improvement in other data sets compared to the thirteen algorithms.
Introduction
Today, due to the increasing use of the Internet, social networks have found an important role in the real life of people. In social networks, some nodes are more connected than the entire network nodes, which are called communities(Sperli, 2019). Community Detection is an important topic for social network analysis and is also essential to understanding complex network structure In community detection, the goal is to determine the groups in which the group nodes are densely connected.
There are many methods for community detection, but deep learning has shown excellent performance in a wide range of research fields, such as social networks, graph embedding, etc.
In this research, deep learning techniques have been used to control graph data with high dimensions, while presenting a comprehensive and integrated architecture of community detection methods with deep learning.
Research Questions
Is it possible to create a new similarity matrix from the graph of complex networks that fully reveals the similarity relationships between network nodes?
What is the appropriate method of deep learning to represent the features of complex networks in low dimensions?
Is it possible to provide a suitable framework with model flexibility for networks of different sizes for community detection using the deep learning method?
Can more accurate clustering results be achieved for community detection?
Literature Review
2.1.Community detection classic approaches are suitable for networks with low dimensions. Therefore, the reduction of complex network dimensions is counted as a significant topic in community detection. The disadvantage of the high-dimensional network is the huge computational costs incurred by community detection methods. Therefore, a method is needed to transform high-dimensional graphs into a lower-dimensional space, where important information about network structure and node properties is still preserved. According to past research, autoencoders are the dominant method for mapping data points in lower-dimensional spaces (Souravlas et al, 2021).
2.2.To display the network, using the proximity matrix as the network similarity matrix can describe the similarity relationship between the nodes in the network. But the relationship between nodes in a social network is complex. On the other hand, in addition to the similarity between nodes that are directly connected, there are different degrees of similarity between nodes that are not directly connected (Su et al., 2020).
2.3. Wu et al. (2020) and Geng et al. (2020) reconstructed the adjacency matrix to represent the network. Dhilber and Bhavani (2020) used a cubic matrix for the input of the stack autoencoders, as did the work of Yang et al. (2016). Xie et al. (2018) first proposed a new representation of network similarity and then fed it with a sparse filtering model to extract meaningful features of network nodes. But in addition to the problem of lack of neighbor information in the proximity matrix based on Su et al.'s (2020) research, using only one function to check the similarity between nodes cannot fully reveal the topological information of the network. Therefore, a similarity matrix should be presented that can solve the proposed gaps.
Methodology
In this paper, to reveal the direct and indirect connections among nodes, first, a new similarity matrix of network topology is built. To construct the new similarity matrix, two matrices are used, i.e. proximity matrix and S∅rensen–Dice's (S∅) similarity matrix in Xie et al. (2018) 's research. In the next step to extract low-dimensional graph features, the new similarity matrix is given as input to the stack autoencoder networks, which have several hidden layers for unsupervised training. Then, using the newly learned features that are in the low-dimensional matrix with the help of K-means, DBSCAN, and SNNDPC clustering algorithms, communities are detected.
Conclusion
Evaluation of the proposed research model is performed by surveying various experiments on standard criteria and six real data sets of Karate, Dolphins, Football, Polbooks, Cora, and Citeseer. The proposed method evaluation outcomes show a higher accuracy in the detection of communities in the football data set compared to the twelve proposed algorithms used in past research and show a significant improvement in other data sets compared to the thirteen algorithms. In addition to these cases, the superiority of the similarity matrix used in this research was proved as a key prerequisite for community detection.
Keywords: Community Detection, Deep Learning, Autoencoder, Complex Networks.
Research Paper
Data, information and knowledge management in the field of smart business
Samaneh Sheibani; Hassan Shakeri; Reza Sheibani
Abstract
Among the various applications of recommender systems, their use in estimating and suggesting points of interest (POIs) for tourists has expanded significantly in recent years. A common approach to identify user interests is to use collaborative filtering (CF) technique. However, the accuracy and efficiency ...
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Among the various applications of recommender systems, their use in estimating and suggesting points of interest (POIs) for tourists has expanded significantly in recent years. A common approach to identify user interests is to use collaborative filtering (CF) technique. However, the accuracy and efficiency of CF can be improved by applying different parameters and complementary approaches. In this paper, a new solution for promoting POI offers to tourists is presented, which uses a five-dimensional time model including the dimensions of day and night hours, days of the week, days of the month, months of the year, and occasions, and by calculating the Euclidean distance between the time of recommendation and the time of previous experiences of the active user and his similar users identifies and suggests suitable venues. The proposed solution also uses the trust parameter to increase the accuracy of POI suggestion. To improve the accuracy of trust evaluation, a new criterion based on a similarity tree structure between contexts is introduced. The results of experiments conducted on three well-known datasets show that the proposed model outperforms the state-of-the-art methods in term of efficiency and accuracy.
Introduction
Recommender systems estimate the interests and preferences of each user and suggest items and services to them, thus helping users to make a quick and favorable choice. Among the various applications of these systems, their use in estimating and suggesting points of interest (POIs) for tourists has expanded significantly in recent years. A common approach to identifying user interests is to use the collaborative filtering (CF) technique. However, the accuracy and efficiency of CF can be improved by applying different parameters and complementary approaches. In this research, a new solution for promoting POI offers to tourists is presented, which uses a five-dimensional time model including the dimensions of day and night hours, days of the week, days of the month, months of the year, and occasions, and by calculating the Euclidean distance between the time of recommendation and the time of previous experiences of the active user and his similar users identifies and suggests suitable venues. The proposed solution also uses the trust parameter to increase the accuracy of POI suggestions. To improve the accuracy of trust evaluation, a new criterion based on a similarity tree structure between contexts is introduced. The results of experiments conducted on three well-known datasets show that the proposed model outperforms the state-of-the-art methods in terms of efficiency and accuracy.
Research Question(s)
The main question of the current research is whether considering the different dimensions of the time parameter in touristic place recommendation systems, along with the trust parameter between users, can significantly increase the accuracy of the system's recommendations.
Literature Review
Various research works have been done with the aim of investigating the impact of social relations, time, place, and context on the efficiency of recommender systems. Savage et al. (2012) presented a location-based recommendation algorithm to improve the accuracy of recommended items based on learning according to the analysis of the user's profile in social networks and his location. Bedi (2020) presents a cross-domain approach for group recommender systems. In this approach, the suggestions provided by reliable and well-known users in the group improve the acceptance of recommendations compared to the suggestions of other people in the group. The system is designed in such a way that it takes into account the information of different sub-domains of the tourism domain. El Yebdri et al. (2021) proposed a context-aware trust-based post-refining approach to overcome the problems of data sparsity and cold start in recommender systems. This approach uses the average relative difference between fields. The authors first calculate the average score for each contextual condition and balance all evaluations based on the contextual condition of each tuple.
On the other hand, in the new era, which is known as the post-Fordism era, the supply and demand patterns in the field of tourism have faced significant changes which should be considered in the strategies of tourism service providers (Liasidou, 2022).
Methodology
According to the main goal of the current research, which is to increase the accuracy of systems recommending points of interest to tourists by introducing the influence of time dimensions, the research includes several stages. At first, a new approach to represent time in terms of hours, days of the week, days of the month, months of the year, and occasions is presented. Then, this time representation approach is combined with a trust computing model and a context-aware collaborative filtering technique to build a computational model for extracting and recommending points of interest to tourists. In the next stage of the research, to evaluate the effectiveness of the proposed model in increasing the accuracy of the system's recommendations and the level of user satisfaction, the presented model was implemented on several datasets in the field of tourism.
Results
In this research, several experiments have been performed to evaluate the performance of the proposed model. Experiments have been conducted on three real public datasets in the field of tourism, namely Yelp, Foursquare, and Gowalla. Some common criteria have been used to evaluate the proposed approach and compare its accuracy and efficiency with the existing methods:
Precision: the ratio of the number of relevant items in the list of top N items to N.
Recall: the ratio of the number of relevant items in the list of N suggested items to the total number of relevant items.
The results of the proposed model in this research were compared with three existing similar research works, including USSTC, MEAP-T, and LOCABAL+, which were respectively conducted by Kefalas and Manolopoulos (2017), Ying et al. (2019) and Ardisono and Mauro (2020).
The first experiment was performed to analyze the sensitivity of the proposed model in terms of precision and recall criteria to changes in the value of N for the top N item suggestion. As expected, the precision decreases as the number of suggested venues increases. On the other hand, as N increases, the recall increases as well.
Subsequent experiments were conducted to measure and compare the accuracy and recall criteria and showed that the proposed method provides the best accuracy values for different datasets compared to existing research works.
Discussion
The results of the evaluations based on three well-known data sets in the field of tourism-related recommendation systems showed that the application of these parameters significantly improves the accuracy of the system's recommendations, and therefore they should be considered more seriously in the recommender systems.
It is worth noting that if the absolute values of the results are evaluated, the improvement of the results in the proposed model may seem insignificant compared to the previous models. But if the relative amount of the improvement of the results is considered, for example, in the case of the Yelp dataset, it can be seen that the proposed model has provided a significant increase in precision and recall criteria even compared to its closest competitor, LOCABAL+.
Conclusion
In this research, with the aim of improving the performance of systems recommending venues to tourists, a model based on the estimation of trust between people was presented and evaluated. In the proposed model, the level of trust between two users in choosing their favorite places to visit is estimated based on the similarity level of their feedback and previous comments. In this regard, in the proposed model, parameters of time, location of the tourist, and classification of POIs were considered. In the proposed solution, a five-dimensional time model is used, and suitable venues are identified and suggested by calculating the distance between the time of recommendation and the time of previous experiences of similar tourists. The improvement of the results of this approach, which is evident in the results of this research, shows that systems that apply different dimensions of time in offering places to tourists, provide more accurate recommendations and a higher level of satisfaction for users.
Keywords: Tourism Recommender System, POI, Location-Based Services, Time-Aware Recommendation, Trust-Based Recommendation, Context-Aware Recommendation.
Research Paper
Management approaches in the field of smart
Fatemeh Mohammadnezhad Chari; Jahanyar Bamdadsoofi; Iman Raeisi Vanani; Maghsoud Amiri
Abstract
The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy ...
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The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy approach that is first introduced by Nickerson et al.(2013).Mixing of a systematic way on current literatures along with structured interviews by some experts who are involved in this area is applied to gain the main objectives.Our results provide the main bloc of the presented archetype with three sub blocs ،attributes and specifications that is titled value proposition. The sub blocs are named value creation، value capture and value delivery.
Introduction
Recently many online data trading platforms have emerged as a new business paradigm to respond to society’s fundamental needs and rights for specific data. On these data marketplaces, service providers buy raw data from device and application owners or collect it from contributors to offer enriched and value-added data to data consumers such as scientists, businesses, etc. The aim of this study is to develop an architecture of business model for data marketplaces in order to get better a understanding of their business logic.
Hence, the research questions are as follows:
1-What are the attributes of construct blocs of the data marketplace business model?
2- What are the specifications of each attribute in any construct bloc of data marketplace business model?
Literature Review
The concept of business model has evolved during recent years by refining its components. There are different types of business model constructs across the literature, from 9 blocs of Osterwalder and Pigneur (2010) to the business model with 3 blocs proposed by “Hautes Etudes Commerciales de Paris” called Odyssey 3.14. The most famous business model construct includes four components (blocs) with “value proposition” as a core component which refers to the benefits that customers receive and why the company is the best choice for them. (Magretta, 2002; Casadesus et al.,2010). The three sub-constructs include “value creation”, “value delivery”, and “value capture” (Teece, 2010). “Value creation” reflects the products and services offered by the company and also the key activities, resources and processes, and partners. “Value delivery” refers to the corporate interactions with the market and “Value capture” concerns the revenue streams and cost structures which make the profit equation.
Methodology:
The present study is conducted through exploratory and inductive approach to achieve an archetype of a business model for data marketplaces. To the best of our knowledge, this research paper could be considered a first attempt in the field of data marketplaces business model design in Iran.
The methodological orientation of this research is based on two iterative taxonomy approaches that is first introduced by Nickerson et., al (2013). Mixing of a systematic way on current works of literature along with structured interviews by some experts who are involved in this area is applied to gain the main objective and answer the research questions. Through this approach, three following steps are taken in a systematic and repetitive manner.
Systematic literature review of 43 scientific documents and their content analysis
Conducting structured interviews with 5 experts
Visiting 4 online data platforms and data marketplaces websites
Results and discussion:
Findings indicate that the data marketplace business model archetype consists of “value proposition” as a main component with 8 attributes including data goods, technological products, infrastructural services, brokery and curation services, operating services, supporting services, the domain of activities, and proprietary forms. The three sub-components’ attributes concerning the data marketplace business model are figured out as follows:
“Value creation” as a sub-construct with six attributes including key partners, key activities, key processes, key products and services, transaction orientations, data sourcing and data origin, and data time -frame.
“Value delivery” as a second sub-component includes five attributes such as data accessibility, output frames, target audiences, trustworthy mechanisms, and privacy preservation mechanisms.
“Value capture” with five attributes including price discovery mechanisms, payment mechanisms, revenue streams, costing mechanisms, and pricing models.
To sum up, these 24 attributes include more than 100 specifications. All of these specifications are profoundly described in detail across the article. Some attributes have more than 8 specifications such as key partners, key activities, or key processes while others have fewer. Most of the specifications are not exclusive, since a particular platform’s attributes may include one or multiple specifications. For example, a particular data platform could have multiple pricing models such as “pay-per-use”, “freemium” or “flat rate”.
Conclusion
Our taxonomy of the data marketplace business model could be extended by four major concerns of data platforms which are data quality evaluation, data pricing mechanisms, secure data trading and truthfulness, and privacy protection mechanisms. Some aspects of the data marketplace business model are inherently contradictory and a trade-off has to be applied between them. For example, European General Data Protection Regulation (GDPR) tries to make a trade-off between data trading transparency and individual privacy protection. Furthermore, participants’ conflicting interests in order to gain a win-win result have to be considered in all online data platform business models. We suggest future researchers in computer science and IT management science, and data scientists extend our archetype by using methods such as text mining techniques and web crawling.
Keywords: Data Marketplace, Business Model, Archetype, Taxonomy.
Research Paper
Data, information and knowledge management in the field of smart business
nafiseh rafiei; Zahra Zakeri Nasrabadi; Nikta Rey Shahrizadeh
Abstract
The purpose of this research was to design a model of the job competencies of online business consultants. The research method was qualitative with a contextual approach. The samples were first selected purposefully and then through snowball method. The interviews were conducted in an in-depth, semi-structured ...
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The purpose of this research was to design a model of the job competencies of online business consultants. The research method was qualitative with a contextual approach. The samples were first selected purposefully and then through snowball method. The interviews were conducted in an in-depth, semi-structured manner. With the process of open, central, and selective coding, 5 selective categories and 24 central categories were extracted. Causal conditions (the effort to acquire specialized knowledge, mastery of information and communication technology, experience in online business, having discipline in work and flexible management), background conditions (management and structural performance of the government, development of consulting culture in society), intervening conditions (non The stability and lack of structure of the country's economy, inefficient and cumbersome bureaucracy, lack of laws supporting business owners and weaknesses within the job, strategies (strategies planned by the government, acquiring up-to-date knowledge and information in the field Online business, practical training of online business, having discernment, use of reference groups) and consequences (strategic thinking, self-empowerment, civil ethics in work, performance management) were extracted. The obtained categories, while differentiating the job of consultants, achieved a model that can be the basis of the performance of online business consultants.
Introduction
Competence in its best definition is a combination of visible and measurable knowledge, skills, abilities, and characteristics that help improve employee performance and ultimately lead to organizational success. (Müller-Frommeyer, 2017). Therefore, having proficiency in the field of job competencies related to online business will help them to prosper more (Hakak et al., 2020).
One of the main reasons for the failure to survive or achieve the expected growth in online businesses is the lack of knowledge and expertise in online business management. Therefore, the presence of competent entrepreneurship consultants plays an important role in this field. (Reid et al., 2019).
Since the purpose of business consulting is specific and strategic, the chosen approach should be a combination of providing advice based on the consultant's experiences and coaching. In addition to this skill, having general business experience will play a significant role in guiding clients in aspects of strategic planning, business development, and responding to existing business challenges. Of course, these services will be efficient enough when the consultants have sufficient competence in personality, moral and skill dimensions (Rajab pour, 2020).
Research Questions
In the context of which causal, contextual, and intervening factors, job competencies of online business consultants are formed? What strategies do online business consultants take to strengthen job skills? What consequences will the adopted solutions have for improving the performance of online business consultants?
Literature Review
Researchers have identified different components of job competence including: motivation, social skills, self-awareness, empathy, self-regulation, cognitive skills (Liikamaa, 2015), strategic contribution, business knowledge, personal credit, technology (Mufti et al., 2016), systemic thinking, acceptance of interdisciplinary diversity, intrapersonal competence, practical and strategic management (Solansky, 2020).
Some researches show the competencies needed by business consultants, including the competencies of motivating and giving hope, keeping entrepreneurs' information confidential and protecting their intellectual property rights, alertness to new work opportunities, and the ability to prepare a business plan (Hatami&Azizi, 2015). Also, the job competence of employees has been identified in terms of personal characteristics, knowledge, and skills (Babashahi et al., 2017). The competencies of the consultants of organizations, in addition to specific personality competencies, were also extracted in the sub-categories of intelligence, knowledge of management and organization, strategic thinking, situational assessment, and leadership of leaders (Vakili et al., 2021).
Methodology
The method of this research was qualitative based on a contextual approach. The area investigated in the current qualitative research was formed by experts of online business consultants. In this research, the samples were selected purposefully and the sampling process continued as a snowball. Semi-structured in-depth interviews were completed with 18 experts until the theoretical saturation criterion was reached. The duration of each interview was between 50 and 80 minutes.
Results
The analysis of the research data in the three stages of open, central, and selective coding finally resulted in 24 central categories, 5 major categories, and one core category which covers all the emerged categories, which is mentioned in the table below.
Table 1. Coding results (source: findings of the current research)
Selective coding
Axial coding
First order axial code
Second order axial code
Job competency
Knowledge-oriented, skill-oriented
and ethical
Get updated information
-
Having multiple skills
practical skill
Communication skills
Speaking skill
Listening skills
Ethics of consultants
-
gaining experience
and knowledge in the age of information
and communication
Trying to acquire specialized knowledge
Mastery of information and communication technology
Online business experience
Having order and discipline at work
Correct management of programs
Background
conditions from
macro to
micro levels
Administrative performance of the government
Structural performance of the government
The growth of counseling culture in society
Economy
bureaucracy
and restrictive
culture
Instability and unstructured economy in Iran
Inefficient and cumbersome bureaucracy
Lack of laws supporting business owners
Limitations and weaknesses within the job
Strategies
structural-
operational) to
strengthen job
competence
Selective
coding
Systematic and planned government strategies
Get up-to-date business knowledge and information online
Practical and practical online business training
Having the power of discernment
Axial coding
Strategies
structural-
operational)
Use the experience of others and reference groups in your field of work
Personal,
professional
and social promotion
Strategic thinking
Self-empowerment
Civil ethics in performing job duties
performance management
Finally, during selective coding (central extraction, causal and contextual conditions, interventional conditions, strategies, and consequences), central categories in each sector, systematically related to other categories, relationships in a clear communication framework, and the research paradigm model were drawn that narrate the process of forming job competencies online. The model is illustrated in Figure1.
Figure 1. Derived contextual model
(source: findings of the present research)
Intervening conditions of economy, bureaucracy and restrictive culture
* Instability and unstructured economy
* Inefficient and cumbersome bureaucracy
*Lack of laws supporting business owners *Limitations and weaknesses within the job
Causal conditions for gaining experience and knowledge in the age of information and communication
* Trying to acquire specialized knowledge
* Mastery of technology
Information and communication
* Online business experience
* Having order and discipline at work
*Flexible management
Background conditions from macro to micro levels
* Administrative performance of the government
* The structural function of the government
* Growth of counseling culture in the society
Consequences of personal, professional and social promotion:
* Strategic thinking
* Empowering yourself
* Civil ethics in
Performing job duties
*performance management
The central phenomenon of online business consulting job competencies model
* science-oriented
* Skill oriented
* Moral oriented
Strategies structural-operational) tostrengthen job competence
* Systematic and planned strategies of the government
*Acquiring up-to-date knowledge and information in the field of online business
*Practical and practical online business training
* Having the ability to recognize
*Using the experience of others
Conclusion
The main goal of this research was to design a model of the job competencies of online business consultants. According to the obtained results, the conceptual model of the research was extracted in six main sections, including causal, contextual, intervening, strategic, consequences, and central conditions. The extracted model shows that for the formation of a science-oriented, skill-oriented, and ethical multi-dimensional desirable occupational competency model, both the implementation of strategies at the macro-management and structural levels of society and the agency and active role of actors in this field are needed. It is important to achieve this by acquiring the necessary specialized knowledge and strengthening one's civic capabilities. In this regard, inefficient and cumbersome bureaucracy, lack of sufficient supporting laws for business owners, instability, and unstructured economy in the society are the most important limitations in the model based on the mentality and immediate experiences of the interview. Those involved in online business consulting are depicted. Therefore, if the competency model of online business consultants of this research is implemented, it can play a significant role in improving the performance management of consultants to their clients in a scientific way, not based on personal experiences and based on trial and error.
Keywords: Job Competency, Consultant, Online Business.
Research Paper
Leila Samimi-Dehkordi; Abbas Horri
Abstract
In the last few years, we have witnessed a significant growth of "low-code development platforms" (LCDPs) in attracting the attention of both the market and the academia. LCDPs are visual development platforms that typically run on the cloud, reducing the need for manual coding. They are also used by ...
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In the last few years, we have witnessed a significant growth of "low-code development platforms" (LCDPs) in attracting the attention of both the market and the academia. LCDPs are visual development platforms that typically run on the cloud, reducing the need for manual coding. They are also used by non-professional developers with limited knowledge in programming to construct applications. In this paper, the characteristics of well-known LCDPs are first studied to evaluate the advantages of this approach. Given that the low-code platforms have many goals and features in common with the model-driven engineering (MDE) approaches, it is necessary to examine the position of these platforms in comparison with the MDE approaches and identify the strengths and weaknesses of both. One of the reasons for the popularity of the LCDP platforms is the use of cloud computing, which most model-driven engineering approaches have failed to achieve. Therefore, in this article, we review the solutions for using cloud computing in MDE to apply these approaches to develop low-code platforms and apply the approach on a modeling language for smart contracts.
Introduction
Software engineering is an engineering system that aims to educate, research, and apply methods to develop applications for increasing software productivity and quality and reducing the cost and production time (Kung, 2013). One of the software engineering methods that has received attention in recent years is using Low-Code Development Platforms (LCDP) (Alamin et.al, 2023). LCDPs use a graphical user interface to develop software instead of traditional programming. These types of platforms are suitable tools for organizational companies to reduce development costs and time to market (Tisi et al., 2020). Increasing the level of abstraction in order to reduce the cost of development is exactly the same goal that "Model-Driven Software Engineering" (MDSE) pursues. MDSE is an approach in software engineering where models are used not only as documentation but also for automatic code generation (Brambilla et al., 2017). The MDSE methodology has matured and its best practices can be used for the new field of LCDP (Verbruggen and Snoeck, 2023). At the same time, LCDP has been of great interest in the last few years, and migrating from Model-Driven Engineering to cloud spaces to create LCDPs can be applicable and appropriate (Ruscio et al., 2022).
Research Question(s)
What features can be considered for common LCDPs?
What is the position of LCDPs compared to MDSE?
What are the prerequisites for migrating modeling languages from MDSE to LCDP?
Literature Review
2.1. Theoretical foundations of research
Model-Driven Engineering and a Low-Code Development Platform have both been introduced with the goal of rapid product delivery with minimal programming (Ruscio et al., 2022). However, MDSE is more mature than LCDP (Verbruggen and Snoeck, 2023). Mendix, one of the pioneers in the LCDP field, has presented a LCDP manifesto including 9 principles, which are model-driven development, collaboration, agility, cloud computing, openness, multi-user development, experimentation, governance, and community (Kenneweg et al., 2021). To investigate the features of the LCDP platforms, six stages for developing an application has introduced, which are domain modeling, user interface definition, business logic specification, integration with external services, deployment, and maintenance (Sahay et al., 2020).
2.2. Related Work
2.2.1. Researches related to LCDP and MDSE
Cabot stated that the LCDP approach is the same as MDSE and it has been changed only with the aim of attracting the audience and better understanding the name of the approach (Cabot, 2020). Khorram et al. stated that LCDPs are based on MDSE, in which system design with visual modeling and automatic production of the final executable system has been introduced as a common feature of both approaches (Khorram et al., 2020). In the Locomote framework research, it is stated that developers can take advantage of MDSE principles, but as scalability is one of the serious problems in MDSE, this challenge is more evident in LCDP (Tisi et al., 2020). Alamin et al. stated that LCDP is inspired by MDSE, and development is done using abstract representations instead of focusing on algorithmic calculations (Alamin et al., 2021). Ruscio et al., have classified five research areas, in which the differences between MDSE and LCDP, including end users and application scope, are stated (Ruscio et al., 2022).
2.2.2. Well-known LCDPs
In Appsheet, a variety of tools and services, including data-driven applications, can be easily developed on top of the Google cloud database (Käss et al., 2023). In SwiftUI, it is possible to create a user interface for any Apple device by means of a declarative syntax, drag-and-drop support, and real-time preview (Nekras, 2022). Honeycode is a spreadsheet component and a set of templates for creating simple web-based applications (ElBatanony and Succi, 2021). PowerApps is a no-code platform for business users that starts with the data model and business processes and goes on to automatically generate responsive portable applications. (Gürcan and Taentzer, 2021). OutSystems is a low-code development platform that enables the development of desktop and mobile applications (Martins et al., 2020). Mendix is a low-code development platform where all features can be accessed via drag-and-drop functionality (Gürcan and Taentzer, 2021). KissFlow is a cloud-based workflow automation software platform that helps users build and modify automated enterprise applications (Hili and Oliveira, 2022). Appian is one of the oldest LCDPs that enables the creation of mobile and web applications through personalization tools, built-in team collaboration tools, task management, and social networking (Vincent et al., 2019).
Methodology
The present research method is practical in terms of purpose and descriptive survey in terms of nature. The purpose of this research is to investigate various approaches in the field of LCDPs and compare them with MDSE approaches. Based on this, three research questions were designed. The first question is to examine the characteristics of LCDPs. To answer the first question, two types of studies have been conducted to collect information about these platforms: (1) a review of articles from 2014 to 2023 and (2) a review of LCDP tools.
The second question is to examine the position of LCDPs compared to the MDSE approaches. The review of articles has been from 2014 onwards and the articles that have addressed both issues have been taken into consideration.
The main challenge for LCDPs is the commercial nature of these platforms, which makes a limited community of users able to use them. MDSE tools are often academic and free. Consequently, the third question examined the requirements for moving from MDSE to LCDP. To answer this question, by introducing a case, the requirements of migration have been studied.
Conclusion
In this paper, the emerging approach of LCDP has been introduced. First, the important features of LCDPs have been reviewed and seven different tools were compared based on the reviewed features. Also, due to the common goal with the MDSE field, a comparison between these two fields has been presented and the position of LCDPs has been determined. Based on the MDSE benefits, the migration of modeling languages from the model-driven approach to the low-code development platform has been studied, and an example of migration has been investigated. One of the important limitations of this research is the lack of sufficient resources in the modern LCDP field. Most of the common platforms are commercial and there are few free platforms currently. Consequently, we suggested using the experiences of the model-driven field in the development of these types of platforms. For future work, it is necessary to study the characteristics and complexity of applications built using LCDPs, with the aim of evaluating the performance status of these platforms and reasoning about criteria such as their scalability and efficiency.
Acknowledgments
This article is derived from the results of the research project implemented under the contract number 5497/141 from the funds of Shahrekord University Research and Technology Vice-Chancellor.
Keywords: Model-Driven Engineering, Low-Code Development Platform, Cloud Computing.
Research Paper
Management approaches in the field of smart
Sahar Masah Choolabi; Kambiz Shahroodi; Narges Delafrooz; Yalda Rahmati
Abstract
Today, virtual businesses need to innovate in order to have a better market performance. But the ability of companies to acquire innovation is one of the serious challenges of online stores; Based on this, the present research has been done with the aim of designing a model for innovation on the market ...
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Today, virtual businesses need to innovate in order to have a better market performance. But the ability of companies to acquire innovation is one of the serious challenges of online stores; Based on this, the present research has been done with the aim of designing a model for innovation on the market performance of online shopping websites with the approach of narrative analysis. This study has a qualitative approach. The statistical population included all narratives related to the dimensions of innovation and market performance of prominent online shopping websites in the country. With the help of judgmental sampling method, 69 narratives were examined. Narratives have been analyzed by searching websites and blogs and using the three-step open, central and selective coding method in the form of a topic network. The results indicated 7 organizing themes and 161 basic themes. The identified dimensions include the business model innovation dimension (the sub-dimension of business model type and revenue flow), the organizational innovation dimension (the structure innovation sub-dimension, strategic innovation and innovation culture), the marketing innovation dimension (the product innovation sub-dimension, price innovation, distribution innovation , promotion innovation, advertising innovation, customer segmentation innovation, value proposition innovation and customer relationship innovation), process innovation dimension (technology innovation sub-dimension and safety innovation), credibility innovation dimension (trust innovation sub-dimension and expertise innovation) and market performance dimension ( sub-dimensions of financial performance and non-financial performance) which were included under the overarching themes of innovation and performance of prominent online shopping websites in the country.
Introduction
Market performance is a critical factor for any organization as companies that perform well can generate value over time. In light of the current coronavirus crisis, it is particularly important for online shopping websites to operate efficiently and compete with each other. For these businesses to thrive, they must continuously innovate and adopt new technologies. However, the significant challenge faced by marketers is not merely understanding the constituents of innovation. While previous studies have proposed different models in this area, fewer studies have presented a comprehensive framework that analyzes how various aspects of innovation impact the market performance of online shopping websites. Thus, to address this research gap, the current study introduces a novel framework that views innovation from a fresh perspective. The present study aims to develop a model that examines the effects of innovation on the market performance of online shopping websites, using a narrative analysis approach. Accordingly, the research question that needs to be answered is: what are the different dimensions of innovation in online shopping websites?
Literature Review
2.1. Innovation and market performance of internet businesses
Although research has been conducted on the relationship between innovation and market performance in various industries, there have been relatively few studies specifically focused on the electronic business sector. For instance, Saifullahi and Hamidzadeh Arbabi (2021) conducted internal research at Tejarat Bank, Fallah et al. (2021) examined companies in the petrochemical industry, Malek Mahmoudi et al. (2021) investigated sports clubs, and Hosseinpour et al. (2020) studied food industry exporters, while Dehghani Soltani et al. (2019) explored the hotel industry. All of these studies were conducted on both public and private sector enterprises. In addition, there are foreign studies that have examined the relationship between innovation and market performance in different industries. For instance, Fatonah, S (2022) researched small and medium-sized companies, Lartey et al. (2020) studied telecommunication companies, and Efrata et al. (2020) investigated the clothing industry, while Thi Canh et al. (2019) explored manufacturing companies. However, these studies did not specifically focus on stores or electronic businesses.
2.2. Literature review
Fatonah’s (2022) research revealed a positive correlation between market orientation, product innovation, and competitive advantage. Similarly, Muharam, Andria & Tosida (2020) found that disruptive technology moderates the connection between process innovation and financial performance. However, it was observed that this technology does not have a moderating impact on the relationship between market innovation and financial performance. Moulai, Yazdani, and Kazemi (2022) found that organizational innovation positively impacts export performance and is also related to both radical technological innovation and technological innovation. Ultimately, both types of technological innovation were seen to have a positive impact on export performance. According to Hosseinpour et al. (2019), strategic innovation has a significant impact on the performance of small and medium-sized businesses. In highly competitive environments, where there is only a slight difference in market conditions, strategic innovation has a greater influence on innovative performance, indicating that the stronger the competition, the more significant the impact of strategic innovation on innovative performance.
Considering the theoretical foundations and literature review, it can be concluded that while previous studies have identified different dimensions of innovation, only a few have presented a framework that includes other significant aspects such as business model and credibility. Thus, there is a need to develop a comprehensive framework that encompasses all aspects of innovation.
Methodology
This study employed a qualitative approach with a practical objective and utilized narrative analysis as the research strategy. The target population comprised all narratives related to the dimensions of innovation and market performance of popular online shopping websites in the country, including Digikala, Mediseh, Zanbil, Digistyle, MoTenRo, Khanomi, Baneh.com, Okala, Mobit, Faradars, Dindengar, Uzdellamart, Shipour, Divar, Tasufan, Torob, Filimo, Pol Ticket, totaling 83 narratives. The sample size was determined using judgmental sampling logic. To achieve this goal, all stories published on the websites were examined to ensure that they had specific authors and editing dates. These websites were then categorized accordingly. Afterward, each website was evaluated based on several criteria such as the presence of a communication channel, accessibility to managers and audiences, author and date of text insertion, number of visits, and an electronic trust symbol. Sites that did not meet these criteria were excluded from the study, leaving only 69 narratives for analysis. The data for this study was collected from textual and written stories found on online shopping websites. These texts were analyzed using narrative analysis with the aid of theme analysis. The analysis involved manually coding the narratives using the three-step open, central, and selective coding method, which then formed a network of themes.
Results
Using a comprehensive approach, this study developed a model by analyzing narratives gathered from credible domestic websites and applying the open, central, and selective three-stage coding method. In response to the research question, seven organizing themes and 161 basic themes were identified. The identified dimensions of innovation and performance include business model innovation (with sub-dimensions of business model type and revenue flow), organizational innovation (with sub-dimensions of structure innovation, strategic innovation, and innovation culture), marketing innovation (with sub-dimensions of product innovation, price innovation, distribution innovation, promotion innovation, advertising innovation, customer segmentation innovation, value proposition innovation, and customer relationship innovation), process innovation (with sub-dimensions of technology innovation and safety innovation), credit innovation (with sub-dimensions of trust innovation and expertise innovation), and market performance (with sub-dimensions of financial performance and non-financial performance). These were categorized under the comprehensive themes of innovation and performance of popular online shopping websites in the country and presented in a theme network.
Discussion
According to the findings, business model innovation - specifically the type of business model and income flow sub-dimensions - can impact the market's performance (both financial and non-financial). These results align with previous research in this area. Additionally, organizational innovation was found to have an effect on performance similar to previous studies, but only when considering sub-dimensions such as structure innovation, strategic innovation, and innovation culture. Thus, the identification of various sub-dimensions can be viewed as a novel aspect of this study. The marketing innovation dimension, which includes product innovation, price innovation, distribution innovation, promotion innovation, advertising innovation, customer segmentation innovation, value proposition innovation, and customer relationship innovation sub-dimensions, aligns with prior research results. Meanwhile, process innovation was found to impact performance in a similar way to previous studies, but only when considering sub-dimensions such as technological innovation and safety innovation among other dimensions identified in this study that affect the performance of online shopping websites. Thus, the novelty of this study is emphasized from this perspective. Ultimately, it was found that the credit innovation dimension, which includes sub-dimensions such as trust innovation and expertise innovation, is one of the other dimensions identified in this study that impact the performance of online shopping websites. As previous research has not focused much on this dimension of innovation, it can be concluded that this study's innovation lies in this aspect.
Conclusion
Overall, the findings indicate that online shopping websites looking to enhance their performance can do so by implementing various dimensions of innovation. These include business model innovation, organizational innovation, marketing innovation, process innovation, and credit innovation.
Acknowledgments
We would like to express our gratitude to the professors, experts, and online shopping website managers who assisted us with data collection.
Keywords: Market Performance, Innovation, Website, Online Shopping, Online Stores.
