Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T08:01:03.516Z Has data issue: false hasContentIssue false
  • English
  • Français

Efficient customer relationship management systems for online retailing: The investigation of the influential factors

Published online by Cambridge University Press:  10 August 2022

Kimia Latifi Open the ORCID record for Kimia Latifi [Opens in a new window] ,
Ahoo Ebrahimi ,
Mehdi Ranjbaran ,
Arman Mirzaei  and
Zahra Fakhri
Kimia Latifi*
Affiliation:
Department of Management, Science and Technology, Amirkabir University of Technology, Tehran, Iran
Ahoo Ebrahimi
Affiliation:
Department of Business Administration, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
Mehdi Ranjbaran
Affiliation:
Management and Accounting Department, Allameh Tabatab'i University (ATU), Tehran, Iran
Arman Mirzaei
Affiliation:
Department of Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Zahra Fakhri
Affiliation:
Department of Marketing and Logistics, Florida International University, Miami, USA
*
*Corresponding author: Kimia Latifi, E-mail: kimia.ltf@aut.ac.ir
Rights & Permissions [Opens in a new window]

Abstract

In business processes, gaining customer satisfaction is vital for online retailing. In this way, it is necessary to identify the important factors for attracting and retaining customers in online retailing. Many factors are involved in efficient customer relationship management (CRM) systems. Their efficient implementation needs a deep focus on key customers, proper organization, knowledge management, technology, market, sales, ordering, support, and many other factors. Therefore, this paper examines the effective factors (including innovation management, market knowledge, competitive intelligence, entrepreneurship management, stakeholder management, project management, and business project management) on the efficiency of the CRM systems for online retailing. The data were collected from Digikala employees in Iran. Based on Morgan's table (Table A2 (Appendix)), 248 samples were randomly selected, and questionnaires were sent to employees, where 234 were fully answered. Research data and assumptions were evaluated using SPSS and PLS software. Research findings showed that innovation management (ß = .390, t = 6.426, p < .001), market knowledge (ß = .296, t = 4.864, p < .001), and competitive intelligence (ß = .115, t = 2.190, p < .005) directly impact the efficient CRM systems for online retailing. Stakeholder management (ß = .283, t = 9.713, p < .001), project management (ß = .229, t = 5.458, p < .001), and business project management (ß = .544, t = 11.351, p < .001) are also effective through the mediator variable of entrepreneurial management (ß = .143, t = 2.314, p < .005) on the efficient CRM systems for online retailing.

Keywords

Business project managementcompetitive intelligenceentrepreneurship managementinnovation managementmarket knowledgeproject managementstakeholder management
Type
Research Article
Information
Journal of Management & Organization , Volume 29 , Issue 4: The Role of New ICT-based Systems in Modern Management , July 2023 , pp. 763 - 798
Copyright
© The Author(s), 2022. Published by Cambridge University Press in association with the Australian and New Zealand Academy of Management

Introduction

The need for Information Technology (IT) evolution, especially the Internet and e-commerce, has created an opportunity to improve customer relationships over existing opportunities in today's competitive markets (Bratu, Reference Bratu2019; Ipang, Suroso, & Novitasari, Reference Ipang, Suroso and Novitasari2021; Sanchez, Reference Sanchez2018). The ultimate goal of transforming these communications and interactions is to maximize profitability by increasing duplicate purchases and reducing customer acquisition costs (Riquelme, Román, Cuestas, & Iacobucci, Reference Riquelme, Román, Cuestas and Iacobucci2019; Roggeveen, Sethuraman, Shankar, Dekimpe, Geyskens, & Roggeveen, Reference Roggeveen, Sethuraman, Shankar, Dekimpe, Geyskens and Roggeveen2019). In fact, this evolution of customer relationship management (CRM) systems is a new marketing theory (Farmania, Elsyah, & Tuori, Reference Farmania, Elsyah and Tuori2021; Sota, Chaudhry, Chamaria, & Chauhan, Reference Sota, Chaudhry, Chamaria and Chauhan2018). CRM is used for close and deep contact with customers (Dewnarain, Ramkissoon, & Mavondo, Reference Dewnarain, Ramkissoon and Mavondo2019; Drugău-Constantin, Reference Drugău-Constantin2019). It is a tool for collecting information for their targeted exploitation (Zerbino, Aloini, Dulmin, & Mininno, Reference Zerbino, Aloini, Dulmin and Mininno2018). Using CRM, customers' relationship with retail and information and their needs are analyzed (Das, Mishra, & Mohanty, Reference Das, Mishra and Mohanty2018; Graessley, Horak, Kovacova, Valaskova, & Poliak, Reference Graessley, Horak, Kovacova, Valaskova and Poliak2019).

Understanding the virtual purchasing mechanism and CRM in the online environment is one of the priorities of all retail outlets that intend to expand their activities and presence in virtual markets (Kasemsap, Reference Kasemsap2019; Zhang, Chen, & Li, Reference Zhang, Chen and Li2019). Therefore, online retailers can identify customers' needs and requirements through CRM by checking customer information (Hollowell, Rowland, Kliestik, Kliestikova, & Dengov, Reference Hollowell, Rowland, Kliestik, Kliestikova and Dengov2019; Khan, Salamzadeh, Iqbal, & Yang, Reference Khan, Salamzadeh, Iqbal and Yang2022; Maggon & Chaudhry, Reference Maggon and Chaudhry2018).

On the other hand, customer engagement is significant in online retailing (Harun, Rokonuzzaman, Prybutok, & Prybutok, Reference Harun, Rokonuzzaman, Prybutok and Prybutok2018; Meilhan, Reference Meilhan2019). E-commerce should retain previous customers and attract new customers (Cai, He, Dai, & Zhu, Reference Cai, He, Dai and Zhu2018; Nguyen, de Leeuw, Dullaert, & Foubert, Reference Nguyen, de Leeuw, Dullaert and Foubert2019). Also, if online retailers can find new customers through their previous customers, they will be the market leader (Mathauer & Hofmann, Reference Mathauer and Hofmann2019). Even on the Internet, no business will succeed through advertising, product development, or service provision (Lim, Jin, & Srai, Reference Lim, Jin and Srai2018). It requires using a set of management tools such as CRM (Pagoti, Mathew, & Manoharan, Reference Pagoti, Mathew and Manoharan2018). CRM helps the online retailing systems to capture customers (Kasemsap, Reference Kasemsap2018). Also, customer loyalty is affected as customer relationships become more online, and the importance of efficient CRM systems is enhanced (Li, Ota, Dong, & Guo, Reference Li, Ota, Dong and Guo2017; Nure, Reference Nure2018). It requires identifying the factors influencing efficient CRM systems for online retailing (Wilcox & Gurău, Reference Wilcox and Gurău2003).

Efficient CRM factors allow online retailing to create business models of flexibility that can quickly adapt to swift market developments (Gurău, Ranchhod, & Hackney, Reference Gurău, Ranchhod and Hackney2003; Khan, Rasheed, Alsheshtawi, Ahmed, & Jan, Reference Khan, Rasheed, Alsheshtawi, Ahmed and Jan2020; Mircică, Reference Mircică2020). This flexibility will help retailers collect and analyze service information through market knowledge, recognize new opportunities, and thus maintain customized services and long-term relationships with customers (Li, Dong, Ota, & Guo, Reference Li, Dong, Ota and Guo2016; Meyer, Gonzalez Hernandez, & Toldos Romero, Reference Meyer, Gonzalez Hernandez and Toldos Romero2016; Mirica, Reference Mirica2019; Popescu & Ciurlău, Reference Popescu and Ciurlău2019). Innovation management of the critical factors in efficient CRM systems improves customer service, identify new products, and reduces customers' escape (Pour, Mamani, & Rahimzadeh, Reference Pour, Mamani and Rahimzadeh2018; Wang, Yang, Wang, Sherratt, & Zhang, Reference Wang, Yang, Wang, Sherratt and Zhang2020). Also, online retailing through CRM identifies competitive information and can turn it into an opportunity for further development (Algharabat, Reference Algharabat2014; Budler & Trkman, Reference Budler and Trkman2019). On the other hand, entrepreneurship management helps online retailing take more options than they have in the past (Singh & Gaur, Reference Singh and Gaur2018) by being risk-taking, creative, and leading; that is why retailers will continue to work with new strategies (Fathi, Reference Fathi2019). Hence, we tried to examine the factors influencing efficient CRM systems for online retailing. In summary, the goals of this research are:

  • • Investigating the impact of innovation management on efficient CRM Systems for online retailing.

  • • Investigating the impact of market knowledge on efficient CRM Systems for online retailing.

  • • Investigating the impact of competitive intelligence on efficient CRM Systems for online retailing.

  • • Investigating the impact of stakeholder management through the mediator variable of entrepreneurial management on efficient CRM Systems for online retailing.

  • • Investigating the impact of project management through the mediator variable of entrepreneurial management on efficient CRM Systems for online retailing.

  • • Investigating the impact of business project management through the mediator variable of entrepreneurial management on efficient CRM Systems for online retailing.

In the continuation of the research, in section ‘Theoretical background’, we review the literature related to customer relationship management and online retail. In section ‘Research model and hypotheses’, we present the research model and hypotheses. In section ‘Methods and measurements’, section ‘Analysis of data and results’, sections ‘Discussion’ and ‘ Conclusions, limitations, and future research’, provide future discussions, results, constraints and suggestions, respectively. Also, in Figure 1, we present the overall processing architecture of CRM systems. Table A1 shows the commonly used mathematical symbols in the paper.

Figure 1. A general processing architecture of CRM.

Theoretical background

This paper proposes a framework and a new model to examine the effective factors in efficient CRM systems for online retailing. There have been widespread personal, organizational, and national life changes through the Internet (Alhussain, Kurdi, & Altoaimy, Reference Alhussain, Kurdi and Altoaimy2019; Lei, Xu, & Jin, Reference Lei, Xu and Jin2021). Today, purchasing products and services from online retailing is commonplace for customers (Pantano & Gandini, Reference Pantano and Gandini2018; Wu & Zhu, Reference Wu and Zhu2021). Since the required information in relation to products and services is easily accessible to users (Hult, Sharma, Morgeson, & Zhang, Reference Hult, Sharma, Morgeson and Zhang2019), they have the opportunity to compare and make better decisions (Benitez, Ray, & Henseler, Reference Benitez, Ray and Henseler2018; Fazal-e-Hasan, Ahmadi, Mortimer, Grimmer, & Kelly, Reference Fazal-e-Hasan, Ahmadi, Mortimer, Grimmer and Kelly2018). In an economy that is not sustainable, businesses need to consolidate their position with the help of innovation and seek new markets (Geissdoerfer, Vladimirova, & Evans, Reference Geissdoerfer, Vladimirova and Evans2018). Innovation management can help speed up control of the challenges of innovation processes (Albors-Garrigos, Igartua, & Peiro, Reference Albors-Garrigos, Igartua and Peiro2018). Meanwhile, should have the right information and knowledge about markets, products, services, processes, customers, and competitors to increase profits and sales retail and establish an online environment (Gao, Shi, & Zhao, Reference Gao, Shi and Zhao2021; Goel, Reference Goel2018; Zheng, Han, & Yang, Reference Zheng, Han and Yang2021). It has been shown that entrepreneurship is the main driving engine for economic development in countries (Henrekson & Sanandaji, Reference Henrekson and Sanandaji2020). The entrepreneur finds an idea and turns it into an economic opportunity (Lipset, Reference Lipset2018). In the business environment, the opportunity is a new idea that can be commercialized (Liu, Zhang, Ge, Chen, Wu, & Tian, Reference Liu, Zhang, Ge, Chen, Wu and Tian2019; Teece, Reference Teece2018). Awareness of the market environment and customer needs along with entrepreneurial attitudes will help online retailer find opportunities and how to deal with them (Cooper, Reference Cooper2017; Zhang, Zhong, Wang, Chao, & Wang, Reference Zhang, Zhong, Wang, Chao and Wang2020). Therefore, entrepreneurship management can be effective as a mediator among project management, business project management and stakeholder management on the efficient CRM systems for online retailing. The rest of this section delivers a summary of the most important studies on online retailing, CRM and related conceptual framework.

Online retailing

Mogili and Natarajan (Reference Mogili and Natarajan2021) have focused on online retailing and customer engagement: The changing CRM paradigm. They examined the pattern of changing CRM. They stated that exchanging and building relationships is a fundamental framework for understanding marketing. Marketing can take place in social or economic relationships, including between retailers or between end consumers. The results of a survey show that most people make their purchases online. Therefore, using CRM, experience, and management should be combined, and a database should be created to achieve the goals. The results of their study showed that in online retail businesses, factors such as personalized services, social monitoring, networking, integration, product information, customer loyalty, community building, management through the sales unit channel should be considered.

Gautam and Sharma (Reference Gautam and Sharma2019) have examined the mediating role of company information in the relationships between perceived risks and purchase intentions in online retailing. This research investigated the direct and indirect effects of perceived risks on consumer purchasing purposes in online shopping. The results showed that performance, financial and physical risks had a negative impact on purchasing purposes. The results will help online retailers understand their customers. Accordingly, online marketers can design contemporary approaches to attract retail customers, leading to greater profitability for the organization.

Also, the development and confirmation of a tool to measure online retailing ethics were investigated by Agag, El-masry, Alharbi, and Ahmed Almamy (Reference Agag, El-masry, Alharbi and Ahmed Almamy2016). Their goal is to identify the dimensions of e-commerce ethics from consumers' points of view and establish a valid measuring instrument. The results have revealed that seller ethics is dependent on privacy, security, reliability, deception, service improvement, and subscription value. These factors will lead to customer satisfaction. The research limitation was to identify just some of the seller's ethics.

Rafiq, Fulford, and Lu (Reference Rafiq, Fulford and Lu2013) have focused on building customer loyalty in online retailing. Their goal is to examine the effective customer relationship's role in attracting customers on the Internet. The model was tested using structural equations modeling. The results have shown that satisfaction with the relationship, trust, and commitment is also affected, and the proper understanding of customer needs affects emotional loyalty. The limitation of this research was investigating customer relationships in the Internet environment.

Xing, Grant, McKinnon, and Fernie (Reference Xing, Grant, McKinnon and Fernie2010) have examined physical distribution service quality in online retailing. Their goal is to provide a two-stage model for scale development, which is proposed in a framework using order fulfillment. Findings have indicated that the price is the essential criterion for online shopping because consumers buy after the price appraisal. Their research presented the difference between multichannel retailers and broadcast retailers.

Also, Ahn, Ryu, and Han (Reference Ahn, Ryu and Han2007) have studied proposed the impact of Web quality and playfulness on user acceptance of online retailing. Their goal was to assess the impact of playfulness on online retail and the relationship between the quality of the web and the user's acceptable behavior. They have provided a research model, and LISERL software was used to test the model. The results have shown that playfulness has an essential role in increasing the user's attitude and behavioral intention to use a site. Also, Web quality, information, and quality of classified services significantly impact the ease of use, perceived performance, and usefulness. As a result, the use of the web is very Suitable for retail.

Wilcox and Gurău (Reference Wilcox and Gurău2003) have examined business modeling and implementing CRM systems for online retailing. CRM technology has received considerable attention from researchers as a facilitator of organizational performance. Although companies have made large investments in their application technologies, they stated that empirical research suggests that implementing CRM technology alone cannot improve organizational performance. There are several factors to consider when implementing CRM. They stated that Unified Modelling Language (UML) provides a tool for addressing problems, finding solutions, and improving systems. Their findings show that although UML cannot assurance success in online retail companies, it creates a consistent, standard, and supportive modeling language that allows you to focus on specific frameworks.

Finally, Holloway and Beatty (Reference Holloway and Beatty2003) have examined service failure in online retailing. This research aimed to study customers' viewpoints of online service failures. The research consists of two studies that were conducted using qualitative and quantitative methods with examples of online buyers to provide initial testing through the management of online retailer services that was carried out over two years. The first period is related to the quality of the services, and the second period is due to the reasons for the failure of retail online. The results have provided a typology of online service failures and confirmed many areas in which online retailers fail to manage the service recoveries.

Customer relationship management

Dewnarain, Ramkissoon, and Mavondo (Reference Dewnarain, Ramkissoon and Mavondo2019) have examined social CRM and offered a conceptual model for addressing the relationship between CRM, social intervention techniques, customer participation, positive speech, and brand loyalty. The results have shown that in the case of an emotional encounter with a customer, customer loyalty and increases the company's lifespan. Also, creating innovative service experiences can lead to increased communication, reduce marketing costs and increase income. The limitation of this research is ignoring the negative relationship with the client and its consequences.

Also, modeling CRM patterns using the human factors approach in the hospitals has been investigated by Taghavi, Riahi, Nasiripour, and Jahangiri (Reference Taghavi, Riahi, Nasiripour and Jahangiri2017). Their goal is to design a CRM protocol with a human resource approach. Their findings have shown that CRM directly affects company performance, and human resource management plays a vital role in CRM. Also, creating commitment, happiness, empowerment, and building self-confidence in employees will increase the positive relationship with customers.

Azevedo (Reference Azevedo2013) has focused on CRM adoption in firms. Using factor identification, they have developed a research model. Data is gathered from 209 companies in Portugal, and the PLS is used to evaluate eleven hypotheses. Their research results showed that adaptability and competitive pressure are the most enablers of CRM acceptance. Also, complexity is a powerful inhibitor for approving CRM.

Also, Becker, Greve, and Albers (Reference Becker, Greve and Albers2009) have studied the influence of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention. They have provided a conceptual model. To test their model, they have used PLS tools. The results of empirical studies have been conducted by various industries and ten European countries and showed that CRM implementation does not affect the quality of performance for different aspects of the CRM process. Implementing CRM only affects performance when supported significantly by the company's shareholders.

Finally, Chalmeta (Reference Chalmeta2006) has examined new ways to explore CRM in the case of failures and described a formal way to guide the CRM process. Samples were collected from 26 companies through interviews and questionnaires, and this information was put together and processed. The research results have shown that the development and implementation of CRM involve various aspects such as human resource management, computer system, management change, continuous improvement, and implementing CRM will be successful only if the organization's aspects and functions are properly implemented.

In this section, by reviewing previous studies and conducting detailed and extensive studies, the factors affecting efficient CRM systems for online retailing were identified and introduced. The reviewed articles did not fully address these factors, and these studies were conducted in two separate sections, online retailing and CRM systems. In this article, we tried to examine these factors simultaneously. Table 1 summarizes and compares the studied works in the field of online retailing and CRM.

Table 1. Summarization and comparison of the mentioned related works in the field of online retailing and CRM.

Research model and hypotheses

This paper reviews the factors influencing efficient CRM systems for online retailing. Today, closer communication with the customer is done more through the Internet (Alam, Al Karim, & Habiba, Reference Alam, Al Karim and Habiba2021), and contact and presence in stores and retailers have become much less (Chatterjee, Rana, Tamilmani, & Sharma, Reference Chatterjee, Rana, Tamilmani and Sharma2021; Gautam, Reference Gautam2018). Therefore, how to connect with customers affects income, profitability, and value has become a factor for competition (Pantano, Priporas, & Dennis, Reference Pantano, Priporas and Dennis2018a; Szmigin, Canning, & Reppel, Reference Szmigin, Canning and Reppel2005; Thomas & Jadeja, Reference Thomas and Jadeja2021). Online retailing aims to achieve a growing relationship that involves more customer satisfaction and a demand for increased service volumes (Aminoff & Hakanen, Reference Aminoff and Hakanen2018; Kosiba, Boateng, Okoe Amartey, Boakye, & Hinson, Reference Kosiba, Boateng, Okoe Amartey, Boakye and Hinson2018; Richards, Hamilton, & Empen, Reference Richards, Hamilton and Empen2016). On the other hand, innovation is associated with developing new services and products (Edvardsson et al., Reference Edvardsson, Frow, Jaakkola, Keiningham, Koskela-Huotari, Mele and Tombs2018). Therefore, innovation management is an essential component in determining the competitiveness of businesses for online retailing (Shams, Vrontis, Weber, & Tsoukatos, Reference Shams, Vrontis, Weber and Tsoukatos2018). Meanwhile, knowledge in any business can improve decision-making, creativity, and alignment with change (De Cock, Bruneel, & Bobelyn, Reference De Cock, Bruneel and Bobelyn2018). For this reason, market knowledge plays a significant role in developing marketing strategies and attracting customers (Watson, Weaven, Perkins, Sardana, & Palmatier, Reference Watson, Weaven, Perkins, Sardana and Palmatier2018). In this regard, gaining awareness of competitive intelligence about competitors in the market will enable online retailers to deliver better services than competitors to customers (Bergeron & Hiller, Reference Bergeron and Hiller2002; Guerola-Navarro, Oltra-Badenes, Gil-Gomez, & Gil-Gomez, Reference Guerola-Navarro, Oltra-Badenes, Gil-Gomez and Gil-Gomez2021b). Also, entrepreneurship management shows the existence of the initiative and offers an idea of change or a new potential concept that can succeed in the complex market (Cheng & Wang, Reference Cheng and Wang2022). Entrepreneurship management as a mediator variable in establishing a link between stakeholder management, project management, and business project management for online retailing can help maintain a leadership position. This is an effective factor in efficient CRM systems for success in online retailing. This section plans to present a new model for evaluating the affective factor in efficient CRM systems for online retailing. Figure 2 shows the proposed model with seven variables. It also defines these variables and assumptions. Also, the sources in Table 2 are used to mention the sub-factors in the article.

Figure 2. The effective factors on efficient CRM systems for online retailing.

Table 2. Sources of sub-factors

Innovation management

The innovation process involves evaluating potential ideas to reach the product and delivering to the market (Hermens, Reference Hermens2018; Salerno, de Vasconcelos Gomes, da Silva, Bagno, & Freitas, Reference Salerno, de Vasconcelos Gomes, da Silva, Bagno and Freitas2015). Once the innovation strategy is set up correctly with the business's goals, it is the next step in managing it (Amling & Daugherty, Reference Amling and Daugherty2018; Guo, Zhou, Zhang, Hu, & Song, Reference Guo, Zhou, Zhang, Hu and Song2020; Nambisan, Lyytinen, Majchrzak, & Song, Reference Nambisan, Lyytinen, Majchrzak and Song2017). Businesses usually seek to use innovation to improve current products and reach attractive offers for their customers (Caputo & Ayoko, Reference Caputo and Ayoko2021; Hausman, Reference Hausman2005). In an economy in which conditions are changing, businesses need to consolidate their position with the help of innovation and always seek new markets (Bocken, de Pauw, Bakker, & van der Grinten, Reference Bocken, de Pauw, Bakker and van der Grinten2016). In general, some of the benefits of innovation management include improving productivity, guaranteeing long-term success, increasing market success, and reducing costs (Kahn, Reference Kahn2018; Kiron, Kruschwitz, Reeves, & Goh, Reference Kiron, Kruschwitz, Reeves and Goh2013; Kumar, Mokha, & Pattnaik, Reference Kumar, Mokha and Pattnaik2021). For this reason, innovation management will positively impact the efficient CRM systems for online retailing. Innovation management involves productivity, long-term operations, market sharing, and cost-benefit in this paper. Therefore, innovation management in the present research model can assess the effectiveness of efficient CRM systems for online retailing. Therefore, the innovation management-related hypothesis is:

H1. The effectiveness of efficient CRM systems for online retailing is influenced by innovation management.

Market knowledge

Knowledge is applied and organized to solve problems (Charband & Jafari Navimipour, Reference Charband and Jafari Navimipour2018). Knowledge is a perception and understanding that is achieved through experience, reasoning, direct understanding, and learning (Zagzebski, Reference Zagzebski2017; Zareie & Navimipour, Reference Zareie and Navimipour2016). Market knowledge is a process that helps retailers find, select, organize, and publish important information (Pantano, Priporas, & Stylos, Reference Pantano, Priporas and Stylos2018b). The primary purpose of using market knowledge for online retailing is to adapt quickly to changes in the environment in order to enhance its efficiency and profitability (Jin, Shu, & Zhou, Reference Jin, Shu and Zhou2019). For online retailing success, market information should always be up-to-date (Bos, Breza, & Liberman, Reference Bos, Breza and Liberman2018). Accordingly, retailers will use market knowledge in relation to customer needs, competitors' behavior, market changes, and whole knowledge to gain more profit (Panda, Reference Panda2013). For this reason, market knowledge will positively impact H1. The effectiveness of efficient CRM systems for online retailing is influenced by innovation management. This paper's market knowledge includes customer needs, competitors' behavior, and market changes. Therefore, market knowledge in the present model is used to evaluate the effectiveness of H1. The effectiveness of efficient CRM systems for online retailing is influenced by innovation management. So, the related hypothesis is:

H2. The effectiveness of efficient CRM systems for online retailing is influenced by market knowledge.

Competitive intelligence

Competitive intelligence is the information that can provide a competitive advantage to online retailers (Popescu, Reference Popescu2015). Typically, by assessing the strengths and weaknesses of competitors and exploring ways to fill existing gaps, solutions can be created to create a competitive advantage (Namada, Reference Namada2018). If online retailing can have better features than competitors, it creates a competition boundary (Nandamuri, Rao, & Mishra, Reference Nandamuri, Rao and Mishra2020). Ignoring rivals can increase the share of competitors (Tallman, Luo, & Buckley, Reference Tallman, Luo and Buckley2018). Identifying the factors related to competitor information will increase the online retailing performance for greater profits and portion (Lederman, Olivares, & Van Ryzin, Reference Lederman, Olivares and Van Ryzin2014; Zins, Reference Zins2014). For this reason, competitive intelligence will have a positive impact on efficient CRM systems for online retailing. In this paper, competitive intelligence includes investigating the website of rivals, the position of rivals in the shared market, analysis of competitors' pricing policy, analysis of competitors' advertisements, and registration in rival companies. Therefore, competitive intelligence in the present model is used to evaluate the effectiveness of implementing CRM systems for online retailing. So, the related hypothesis is:

H3. The effectiveness of efficient CRM systems for online retailing is affected by competitive intelligence.

Entrepreneurship management

Entrepreneurship refers to the characteristics and actions of individuals that include the risks of joint ventures in new and unknown areas of work (Venkataraman, Reference Venkataraman, Katz and Corbet2019). Generally, it involves creating or imposing new specializations related to a type of activity that is different from other activities around it (Kuratko & Morris, Reference Kuratko and Morris2018). In the case of correct investment, timely decision making, and the development of new strategies, entrepreneurship management will lead to the success of online retailing (Djafri, Reference Djafri2018). They are also pursuing opportunities and turning them into social and economic values and the ability to deal with problems, limitations, and failures (Chowdhury, Audretsch, & Belitski, Reference Chowdhury, Audretsch and Belitski2019). For this reason, entrepreneurship management will have a positive impact on efficient CRM systems for online retailing. In this article, entrepreneurship management involves innovation, new trends, and new products and software assets. Therefore, entrepreneurship management in the present model is used as a mediator variable for assessing the effective factor in efficient CRM systems for online retailing. So, the related hypothesis is:

H4. The effectiveness of efficient CRM systems for online retailing is influenced by entrepreneurship management.

Stakeholder management

The stakeholder is individuals and groups that somehow contribute to the success or failure (Jones, Harrison, & Felps, Reference Jones, Harrison and Felps2018). Identification and stakeholder participation can be a big part of guaranteeing the success of an activity (Pan, Tan, & Lim, Reference Pan, Tan and Lim2006). Stakeholder management means keeping, co-operating, and supporting on their behalf (Nguyen, Mohamed, & Panuwatwanich, Reference Nguyen, Mohamed and Panuwatwanich2018). Stakeholder management through entrepreneurship management can create new opportunities for online retailing activities (Arnold & Narang Luthra, Reference Arnold and Narang Luthra2000). For this reason, stakeholder management through the mediator variable of entrepreneurship management will positively influence the efficient CRM systems for online retailing. This paper's stakeholder management involves co-working, material supplying, resellers, and banking. Therefore, stakeholder management is used in the present model using the entrepreneurship management mediator variable to evaluate the effectiveness of the efficient CRM systems for online retailing. Therefore, the stakeholder management's hypothesis is:

H5. The effectiveness of efficient CRM systems for online retailing is influenced by stakeholder management through the mediator variable of entrepreneurship management.

Project management

Project management is a set of activities that involve starting, planning, implementing, controlling, and finishing projects (Garwood & Poole, Reference Garwood and Poole2018). Each entrepreneur has a strategy and plans to start a project (Mansoori, Karlsson, & Lundqvist, Reference Mansoori, Karlsson and Lundqvist2019). Starting a new project means accepting big risks (Lee, Reference Lee2019). Through leadership and the creation of new projects, entrepreneurship will advance the goals and benefits of retailing (Muhammad, Reference Muhammad2018). For this reason, project management through the entrepreneurship management mediator variable will positively influence the efficient CRM systems for online retailing. In this paper, project management involves identifying needed resources, benefiting from effective collaboration, and providing a framework for project activities. Therefore, project management is used in the present model using the entrepreneurship management mediator variable to evaluate the effectiveness of efficient CRM systems for online retailing. So, the project management's related hypothesis is:

H6. The effectiveness of efficient CRM systems for online retailing is influenced by project management through the mediator variable of entrepreneurship management.

Business project management

A business project is a project that provides business goals, the reasons for realizing the goals, and programs that are used to pursue to achieve the goals (Artto & Wikström, Reference Artto and Wikström2005). The business project contains detailed reviews of the products or services of a company, market and customers, competitors, human resources, funding, technology, and technical dimensions of the product or service (Wikström, Artto, Kujala, & Söderlund, Reference Wikström, Artto, Kujala and Söderlund2010). One of the most important functions of a business project is that by compiling it, the entrepreneur can identify the internal and external factors involved in a business and examine its effect on the project activity (Görög, Reference Görög2016). Also, facilitate judgment about investing or not investing in a plan for investors or project lenders (Smyth, Gustafsson, & Ganskau, Reference Smyth, Gustafsson and Ganskau2010). For this reason, business project management through the mediator variable of entrepreneurship management will positively impact the efficient CRM systems for online retailing. In this paper, business project management includes business project description, business environment analysis, analyzing a marketing plan, analyzing the operational plan, and analyzing a financial plan. Therefore, project business management in the present model is used through the mediator variable of entrepreneurship management to evaluate the effectiveness of efficient CRM systems for online retailing. Therefore, the hypothesis related to the business project management is:

H7. The effectiveness of efficient CRM systems for online retailing is influenced by business project management through the mediator variable of entrepreneurship management.

Methods and measurements

In order to ensure that the questionnaire questions have content validity, the designed questionnaire was approved by several experienced experts in online retail. After preparing the questionnaire, in order to ensure the correct design of the questionnaire and increase the degree of validity and reliability, after the approval of experienced experts, the questionnaires were piloted and tested among 30 employees of Digi Kala; If there is a defect, it should be corrected. Also, to further ensure the degree of validity and reliability, Cronbach's alpha coefficient and Average Variance Extracted (AVE) and the combined reliability of the questionnaire were determined using two SPSS 22Footnote 1 and SMART-PLSFootnote 2. Questionnaires were sent to employees via email. Standard and authoritative references have been used to evaluate the questionnaire. The distributed questionnaires used a five-point Likert scale (Mohd Thas Thaker, Allah Pitchay, Mohd Thas Thaker, & Amin, Reference Mohd Thas Thaker, Allah Pitchay, Mohd Thas Thaker and Amin2019). In this questionnaire, sample employees of Digikala (the biggest online market in Iran) were selected for review. The purpose of the questionnaire was to evaluate the research hypotheses. The collected assumptions in this article are based on research, literature review, and data collection. The data were collected from Digikala employees in Iran. Since the advancement of information and communications technologies has become more important to customer orientation, we must identify the factors that affect the efficient CRM systems for online retailing. Based on Morgan's table (Table A2 (Appendix)), 248 samples were randomly selected, and questionnaires were sent to employees, where 234 of them were fully answered. The questions raised were related to the factors on implementing CRM systems for online retailing. Research hypotheses are evaluated using data collection through questionnaires. Table A3 (Appendix) is a questionnaire related to efficient CRM systems for online retailing.

Descriptive statistics regarding age, gender, and work experience in the Digikala were analyzed by SPSS software. The analysis results showed that 52.8% of the respondents were female 25.6% of the respondents, according to the evaluation of the questionnaire, have worked in Digikala for 11 to 13 years. Cronbach's alpha value is more than .7. Therefore, this questionnaire has acceptable reliability. Tables 3 and 4 show analytical measurements, including age frequency and work experience frequency, in the Digikala.

Table 3. The frequency of age

Table 4. The frequency of work experience in Digikala

Analysis of data and results

In humanities and social sciences, research data analysis is carried out according to a process with a specific and identical format related to that method of different statistical analysis (Rouf & Akhtaruddin, Reference Rouf and Akhtaruddin2018; Wang, Ramamoorthy, Xi, Rajagopal, Zhang, & Jafari, Reference Wang, Ramamoorthy, Xi, Rajagopal, Zhang and Jafari2022b). In the meantime, we are modeling the structural equations of PLS-SEM, a tool to examine the relationship between several variables in a model (Munir, Reference Munir2018). PLS is used in many limited assumptions based on SEM-based modeling techniques (Tajuddin, Abdullah, Jabar, Jusoh, & Arbaiy, Reference Tajuddin, Abdullah, Jabar, Jusoh and Arbaiy2017). Therefore, this study, which includes constructive constructs, uses PLS as the basis for statistical analysis in data analysis. The most important reasons for the high use of PLS-SEM by researchers is the ability to test theories in the form of equations between variables and appropriate predictive power, the complexity of the model, exploratory research, use of classified variables, divergence, convergence, and assumptions testing including moderator variables (Avkiran, Reference Avkiran2018; Wang, Ramamoorthy, Xi, & Namazi, Reference Wang, Ramamoorthy, Xi and Namazi2022a). Like the Cronbach's Alpha, internal consistency is .7 or greater than this amount (Bujang, Omar, & Baharum, Reference Bujang, Omar and Baharum2018). The convergence validity in the model was evaluated using AVE (Choi, Wang, Zhu, & Lai, Reference Choi, Wang, Zhu and Lai2021). This size should be at least .5 (Ismail, Idris, Ramli, Rooshdi, & Sahamir, Reference Ismail, Idris, Ramli, Rooshdi and Sahamir2018). Reliability was assessed using composite reliability and Cronbach's alpha. Composite reliability and Cronbach's alpha should be .7 (Cronbach, Reference Cronbach1951) to be accepted. As shown in Table 5, the amounts of AVE for all variables are acceptable in this study. The Cronbach's Alpha and the composite reliability are also at an adequate level. As a result, these three conditions are consistent with good convergence. This work is calculated by comparing the root AVE of each construct with the values of the coefficients of correlation between the constructs (Cable & DeRue, Reference Cable and DeRue2002). As shown in Table 6, the root AVE in any construct and the correlation of the construct with other constructs have been increased (Hofenk, van Birgelen, Bloemer, & Semeijn, Reference Hofenk, van Birgelen, Bloemer and Semeijn2019). It suggests the acceptability of the discriminant validity of constructs. PLS is a component-based approach that evaluates the validity of the structures and estimates the relationships between them (He et al., Reference He, Mu, Jean, Zhang, Wu, Zhou and Bu2022; Khan, Sarstedt, Shiau, Hair, Ringle, & Fritze, Reference Khan, Sarstedt, Shiau, Hair, Ringle and Fritze2019). Table 7 provides the estimation of parameters and T-values for the hypothesized relationships. As it is clear, innovation management, market knowledge, and competitive intelligence positively impact the efficient CRM systems for online retailing. Stakeholder management, project management, and business project management through the mediator variable entrepreneurship management affects efficient CRM systems for online retailing.

Table 5. The convergent validity and reliability of measurement for the measurement model

Table 6. The discriminant validity of the measurement model

Table 7. T-value and path analysis of the structural model

p < .001**, p* < .005**.

In PLS, R 2 is a statistical measure that can be interpreted as ‘the relative amount of variance of the dependent variable explained or accounted for by the explanatory variables jointly (Zareie & Navimipour, Reference Zareie and Navimipour2016)’. The range of R 2 is from 0 to 1Footnote 3 where the higher the R 2, the higher the variance that can be explained. Also, GOFFootnote 4 (0 < GOF < 1) is defined as the geometric mean of the average commonality and average R 2 value. GOF small = .1, GOF medium = .25, and GOF large = .36; these may serve as baseline values for confirming the PLS model globally (Reghenzani, Massari, Santinelli, & Fornaciari, Reference Reghenzani, Massari, Santinelli and Fornaciari2019). The GOF index was calculated by (1) (Museli & Jafari Navimipour, Reference Museli and Jafari Navimipour2018):

R 2(Entrepreneurship Management) = .896, R 2 = Higher.

R 2(online retailing) = .653, R 2 = medium.

(1)$${\rm GOF}\,{\rm} = \sqrt{\rm AVE\,\times \,R^ 2}$$
$${\rm GOF}\,{\rm} = \sqrt { .742 \times .774\,{\rm} = } .667$$

We obtained a GOF value of about .667, which exceeds the cut-off value of .36 for large effect sizes of R 2 that concludes that the model performs well compared to the baseline values defined above. Also, in this paper, the path models related to the path coefficients (ß) and R 2 are shown in Figure 3 and T- Value in Figure 4.

Figure 3. Path model of ß and r R 2.

Figure 4. Path model of T- Value.

Discussion

Nowadays, with the increase of e-commerce, new changes in people's lives and their needs have been created. It is quite evident that during the last century, the models of marketing and sales of products from the production phase to the customer-oriented phase have evolved, and retailers of goods and services cannot meet the needs of their customers by using traditional models. The retailers who have embraced the digital revolution are the main winners of the competition. Innovation management in the technological space is imperative for business. With the help of innovation management, online retailing can respond more quickly and better to environmental challenges and adapt to them. The continuous introduction of new products creates a sustainable competitive edge for online retailing. Acquiring information and knowledge is useful for business survival and efficiency, and effectiveness. Understanding the market and consumers, laws, regulations, and restrictions is essential for retailers who intend to market. Online retailers have developed flexible business models using market knowledge that can quickly adapt to swift market changes. Competitive intelligence is the recognition of the strengths and weaknesses of competitors to further market penetration. Identifying, collecting, and analyzing information about products, competitors, and other environmental aspects help online retailers make strategic decisions. Entrepreneurship management organizes the risks of economic activity. In fact, the market opportunities are turning into wealth and, in this regard, creating sustainable value for ecommerce. Managing stakeholders through the intermediation of entrepreneurship management for online retailing with the management of individuals and groups will affect the outlook and mission of a business. It also affects the strategic achievements and outcomes of the business. Also, through the mediator variable of entrepreneurial management for online retailing, the project is checked to ensure it runs properly within the existing timeframe and financial range. Meanwhile, management business projects are a tool to better predict and manage existing or new businesses through prioritization, monitoring, and evaluation of work progress and helping achieve predetermined goals of entrepreneurship management. Such a plan enables business management to prepare in the various stages of the business to promote the programs. Therefore, project management, stakeholder management, and business project management affect the efficient CRM systems for online retailing through the mediator of business management. This paper provides a framework for examining the effective factors in efficient CRM systems for online retailing. The sample was randomly selected from the employees Digikala.

  • • The identified factors affect CRM systems in online retailing based on the research results. These results are consistent with some previous research. Mogili and Natarajan (Reference Mogili and Natarajan2021) showed that trust and service are essential in online retailing. The common denominator of this research with the present research was innovation management and market knowledge. The study of Gautam and Sharma (Reference Gautam and Sharma2019) was conducted to examine market risks and financial risks to try to help retailers for better marketing, which has nothing in common with our research. Findings by Agag et al., (Reference Agag, El-masry, Alharbi and Ahmed Almamy2016), Rafiq, Fulford, and Lu (Reference Rafiq, Fulford and Lu2013), Xing et al., (Reference Xing, Grant, McKinnon and Fernie2010), and Ahn, Ryu, and Han (Reference Ahn, Ryu and Han2007) showed that customer-related factors affect the online retailing experience. Also, service improvement, customer loyalty, trust, and reasonable price affect online retail; however, these factors are not consistent with the factors examined in our research. Wilcox and Gurău (Reference Wilcox and Gurău2003) examined business modeling and efficient CRM systems for online retailing. Customer relationship management technology has been introduced as a facilitator in implementation. Some of the factors considered are consistent with the present study. Finally, Holloway and Beatty (Reference Holloway and Beatty2003) examined service failure in online retailing. The results of their findings effectively determined the factors of this research.

  • • Dewnarain, Ramkissoon, and Mavondo (Reference Dewnarain, Ramkissoon and Mavondo2019) examined social CRM. Factors such as social intervention techniques, customer engagement, positive speech, brand loyalty, knowledge, and innovation provided better customer relationships. Studies show that some of these factors have common factors with the present study. Also, modeling CRM patterns using the human factors approach in the hospitals was investigated by Taghavi et al., (Reference Taghavi, Riahi, Nasiripour and Jahangiri2017). Their research was descriptive using PLS software and presented a conceptual model. Azevedo (Reference Azevedo2013) focused on CRM adoption in firms. There are common factors in our research in the factors studied by them; in their research, the research hypotheses were tested using PLS. The results were also obtained through a questionnaire consisting of 37 questions to evaluate the company's performance and CRM.

Also, Becker, Greve, and Albers (Reference Becker, Greve and Albers2009) studied the influence of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention. They presented a conceptual model for implementing customer relationship management. They also helped the PLS software present the results, but the sub-factors presented had nothing in common with the present study. Finally, Chalmeta (Reference Chalmeta2006) examined new ways to explore CRM in the case of failures and described a formal way to guide the CRM process. There is something in common with some of the factors presented by our research. These factors include project management, entrepreneurship management, and competitive intelligence.

Considering the stated factors, the present study also deals with the issue that in examining the effective factors in efficient CRM systems for online retailing in a particular company, what factors and indicators should be important for future success, and what factors should be taken to strengthen it. Our research findings showed that innovation management, market knowledge, and competitive intelligence have a positive and direct relationship with efficient CRM systems for online retailing. Stakeholder management, project management, and business project management also positively impact the mediator variable of entrepreneurial management on efficient CRM systems for online retailing.

Hypothesis' results

As shown in Table 7, using PLS regression, the sample and path test coefficient results showed that innovation management has a meaningful relationship with efficient CRM systems for online retailing. These results are based on t-test and path coefficient (ß = .390, t = 6.426, p < .001), which supports hypothesis 1. In hypothesis 2, market knowledge has an effective relationship with implementing CRM systems for online retailing; this relationship is supported (ß = .296, t = 4.864, p < .001). As suggested in Hypothesis 3, competitive intelligence affects CRM systems for online retailing (ß = .115, t = 2.190, p < .005). The findings show that there is a positive relationship between these two. Hypothesis 4 (ß = .143, t = 2.314, p < .005) as a mediating variable positively affects CRM systems for online retailing. Hypothesis 5 (ß = .283, t = 9.713, p < .001) shows that stakeholder management through the mediator variable of entrepreneurship management has a significant relationship with efficient CRM systems for online retailing. Also, in hypothesis 6 and 7, project management and business project management have a positive and significant relationship with the efficient CRM systems for online retailing (ß = .229, t = 5.458, p < .001), (ß = .544, t = 11.351, p < .001) through the intermediary variable of entrepreneurial management. In summary, our research findings showed that innovation management, market knowledge, and competitive intelligence have a positive and direct relationship with efficient CRM systems for online retailing. Stakeholder management, project management, and business project management also positively impact the mediator variable of entrepreneurial management on efficient CRM systems for online retailing.

Conclusions, limitations, and future research

The world is confronted with changes in technology, information, people's demands, and global markets. Serious attention to customers to continue activity in competitive markets and gain customer satisfaction is critical. With the help of market knowledge, online retailers identify and monitor customer behaviors and priorities daily and, based on customer preferences, offer their services. Through innovation management, they provide new services in line with customers' needs through the Internet. Competitive intelligence provides solutions for determining the top goals. Entrepreneurship management will increase profits, reduce costs, and increase customer relationships for online retailing as a mediator variable for stakeholder management, project management, and business project management. Therefore, considering the factors affecting the efficient CRM systems for online retailing is affected by several factors. This paper aims to assess innovation management, market knowledge, competitive intelligence, and entrepreneurial management as the mediator variable between project management, stakeholder management, and business project management on efficient CRM systems for online retailing. This research has shown that these factors positively affect the efficient CRM systems for online retailing.

The deep analysis showed that innovation management positively and significantly impacts efficient CRM systems for online retailing. Innovation management can guarantee that online retailing will stay stable in an environment where markets change rapidly by identifying new markets and introducing new products, features, and ways to organize and trade online retailing. Correct relationships with a customer without market knowledge cannot be formed. Market knowledge provides knowledge of business matching with the new market conditions and requirements, leading to the success of efficient CRM systems for online retailing. Employees can increase competitive intelligence in online retail by asking business partners (acting in the role of the customer).

The obtained results showed that entrepreneurial management has a positive effect as a mediator variable on efficient CRM systems for online retailing. Entrepreneurship management creates innovative and appropriate ideas for exploiting existing opportunities and resources, identifying and implementing them, and selecting products and services. With the help of human resources, hardware, and software, it provides a good platform for efficient CRM systems for online retailing. Any entrepreneurial management is a relationship with various stakeholders during its life cycle; it benefits some stakeholders in the form of a financial or business relationship. Some others receive services or benefits. Therefore, stakeholder management in online retailing can have many benefits. By designing new projects and business plans for customer satisfaction, entrepreneurship management can help succeed in online retailing. So, project management, business project management, and stakeholder management through the mediator variable of entrepreneurial management positively affect the efficient CRM systems for online retailing. The results showed that innovation management, market knowledge, and competitive intelligence positively impact the efficient CRM systems for online retailing. Also, project management, business project management, and stakeholder management through the mediator variable of entrepreneurship management have a positive relationship with efficient CRM systems for online retailing.

The main limitation of this study is a limited sample of research for customers in online retailing. The sample size is small and may not reflect negative or general results. Second: the study is limited to one region. It cannot be guaranteed that the factors under investigation are effective in other areas. The research design for this study is cross-sectional. It represents the static relationship between the variables. Because cross-sectional data from the relationship of variables are taken at a single point, they are collected in other periods. Third: in this study, because of lack of time and money, research data is collected using a sample in one place; Fourth: used variables to illustrate the factors affecting the efficient CRM systems for online retailing may not be comprehensive. These variables can be exploratory. Moreover, other factors must be considered for model review. Therefore, considering different dimensions to examine the efficient CRM systems for online retailing is very interesting for future research. In addition, a complete sample for gathering data can be used to determine other factors affecting the efficient CRM systems for online retailing. We also encourage larger instances to do cross-validation of the model. In a nutshell, the following points should be considered in future studies:

  • • Innovation management: In designing and producing a new product or service, there is a need for marketing. It means that it is necessary to survey customers about this product and make sure that the audience in the market welcomes this product. CRM system will help in this regard in two ways. First, customer information has already been collected and can now be used to evaluate survey results better. Second, by using the features of the CRM system, this survey can be done quickly and easily and at the lowest cost.

  • • Market knowledge: Efficient CRM systems for online retailing require market knowledge. Choosing the most effective sales strategies requires market knowledge. To know which strategy is best for which product, which range of customers, and which market segment, we need appropriately collected, categorized, and reportable information.

  • • Competitive intelligence: Efficient CRM systems for online retailing require competitive intelligence. Communicating with customers and being regularly informed of comments, suggestions, and criticisms will help gain strengths and weaknesses in online retail and become aware of the new opportunities offered in these interactions faster. The ability to be a leader among will have their competitors.

  • • Entrepreneurship management: The main focus of many online retailers and businesses is on pursuing opportunities. It is a significant factor in profitability. Efficient CRM systems and dealing with limitations and failures will be possible to calculate profits and costs. Through this, the most profitable customers can be identified along with the most profitable products and services and by providing better services multiplied their profits.

  • • Stakeholder management (mediator variable of entrepreneurship management): Stakeholder management is an essential factor for managing entrepreneurship and, therefore, online retailing. For today's customers, quality alone is not enough. Instead, they expect the seller to create a pleasant shopping experience for them. To make this experience, it is necessary to establish strong relationships with the customer and get to know him better. These factors effectively efficient CRM systems for online retailing and provide the facilities needed to build customer relationships and identify online retailing.

  • • Project management (entrepreneurship management mediator): Project management is important for entrepreneurship management and online retailing. Project management effectively uses marketing, sales, and after-sales service, reducing human error and increasing work speed. On the other hand, planning based on previous information reduces trial and error, thus reducing the cost of workforce, advertising, etc., in many parts of the organization. This factor is also influential in efficient CRM systems for online retailing through the variable of entrepreneurship management.

  • • Business project management (mediating variable of entrepreneurship management): Usually, business project management for entrepreneurship management is a specific goal. Business project management makes it possible to measure marketing results; we can identify the current situation, needs, expectations, and predicted results and repeat them by analyzing the results. Also, by recording and counting the complaints and protests of customers and surveying them, different performances can be evaluated, and corrective measures can be taken to improve their performance. Thus, this factor is influential in efficient CRM systems for online retailing through the variable of entrepreneurship management.

Also, investigating the organizational culture, knowledge management, innovation management on effective, efficient CRM systems for online retailing, examining the role of suppliers, government, and the media in online retailing, and developing a customer knowledge management process model using CRM systems can be studied in the future research.

Appendix

Table A1. Table of mathematical symbols used

Table A2. The Morgan table

Table A3. Questionnaire related to the efficient CRM systems for online retailing

Footnotes

1 http://www-01.ibm.com/software/analytics/spss/.

2 http://www.smartpls.com/.

3 www.forum.smartpls.com.

4 www.forum.smartpls.com.

Note – N is population size. S is sample size.

Source: Krejcie & Morgan, Reference Krejcie and Morgan1970.

References

Ab Rahman, N. A., & Ramli, A. (2014). Entrepreneurship management, competitive advantage and firm performances in the craft industry: Concepts and framework. Procedia-Social and Behavioral Sciences, 145, 129–137.CrossRefGoogle Scholar
Abrar, M., Biag, S. A., Shabbir, R., & Hussain, I. (2019). Role of CRM practices in bank's performance: Moderating role of market turbulence. Journal of Managerial Sciences, 13(2), 189–200.Google Scholar
Abyad, A. (2018). Project management, motivation theories and process management. Middle East Journal of Business, 13(4), 18–22.CrossRefGoogle Scholar
Adams, R., Bessant, J., & Phelps, R. (2006). Innovation management measurement: A review. International Journal of Management Reviews, 8(1), 21–47.CrossRefGoogle Scholar
Adidam, P. T., Gajre, S., & Kejriwal, S. (2009). Cross-cultural competitive intelligence strategies. Marketing Intelligence & Planning, 27(5), 666–680.CrossRefGoogle Scholar
Agag, G., El-masry, A., Alharbi, N. S., & Ahmed Almamy, A. (2016). Development and validation of an instrument to measure online retailing ethics: Consumers’ perspective. Internet Research, 26(5), 1158–1180.CrossRefGoogle Scholar
Ahmed, A., & McQuaid, R. W. (2005). Entrepreneurship, management, and sustainable development. World Review of Entrepreneurship, Management and Sustainable Development, 1(1), 6–30.CrossRefGoogle Scholar
Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management, 44(3), 263–275.CrossRefGoogle Scholar
Aiyer, M., Panigrahi, J. K., & Das, B. (2018). Successful customer relationship management in business process integration and development of applications for project management. International Journal of Mechanical Engineering and Technology, 9(2), 637–643.Google Scholar
Alam, M. M. D., Al Karim, R., & Habiba, W. (2021). The relationship between CRM and customer loyalty: The moderating role of customer trust. International Journal of Bank Marketing, 37(5), 12481272.Google Scholar
Albors-Garrigos, J., Igartua, J. I., & Peiro, A. (2018). Innovation management techniques and tools: Its impact on firm innovation performance. International Journal of Innovation Management, 22(06), 1850051.CrossRefGoogle Scholar
Algharabat, R. S. (2014). The role of 3D product presentation in enhancing electronic customer relationship management (e–CRM) for online retailer: University students context. International Journal of Electronic Customer Relationship Management, 8(4), 159–179.CrossRefGoogle Scholar
Alhussain, A., Kurdi, H., & Altoaimy, L. (2019). A neural network-based trust management system for edge devices in peer-to-peer networks. Computers, Materials & Continua, 59(3), 805–816.CrossRefGoogle Scholar
Aminoff, A., & Hakanen, T. (2018). Implications of product-centric servitization for global distribution channels of manufacturing companies. International Journal of Physical Distribution & Logistics Management, 48(10), 1020–1038.CrossRefGoogle Scholar
Amling, A., & Daugherty, P. J. (2018). Logistics and distribution innovation in China. International Journal of Physical Distribution & Logistics Management, 50(3), 323–332.CrossRefGoogle Scholar
Ang, L., & Buttle, F. (2006). Managing for successful customer acquisition: An exploration. Journal of Marketing Management, 22(3-4), 295–317.CrossRefGoogle Scholar
Arnold, S. J., & Narang Luthra, M. (2000). Market entry effects of large format retailers: A stakeholder analysis. International Journal of Retail & Distribution Management, 28(4/5), 139–154.CrossRefGoogle Scholar
Artto, K., & Kujala, J. (2008). Project business as a research field. International Journal of Managing Projects in Business, 1(4), 469–497.CrossRefGoogle Scholar
Artto, K. A., & Wikström, K. (2005). What is project business? International Journal of Project Management, 23(5), 343–353.CrossRefGoogle Scholar
Avkiran, N. K. (2018). Measuring the systemic risk of regional banks in Japan with PLS-SEM. Theoretical Economics Letters, 8(11), 2024.CrossRefGoogle Scholar
Azevedo, A. T. D. (2013). Customer relationship management adoption: determinants of CRM adoption by firms, http://hdl.handle.net/10362/19159.Google Scholar
Baumol, W. J., & Strom, R. J. (2007). Entrepreneurship and economic growth. Strategic Entrepreneurship Journal, 1(3-4), 233–237.CrossRefGoogle Scholar
Becker, J. U., Greve, G., & Albers, S. (2009). The impact of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention. International Journal of Research in Marketing, 26(3), 207–215.CrossRefGoogle Scholar
Bendickson, J. (2021). Building entrepreneurship research for impact: Scope, phenomenon, and translation (Vol. 59, pp. 535–543). USA: Taylor & Francis.Google Scholar
Benitez, J., Ray, G., & Henseler, J. (2018). Impact of information technology infrastructure flexibility on mergers and acquisitions. MIS Quarterly, 42(1), 25–44.CrossRefGoogle Scholar
Bergeron, P., & Hiller, C. A. (2002). Competitive intelligence. Annual Review of Information Science and Technology (Arist), 36, 353–390.CrossRefGoogle Scholar
Binci, D., Belisari, S., & Appolloni, A. (2019). BPM and change management: An ambidextrous perspective. Business Process Management Journal, 26(1), 1–23.CrossRefGoogle Scholar
Bocken, N. M., de Pauw, I., Bakker, C., & van der Grinten, B. (2016). Product design and business model strategies for a circular economy. Journal of Industrial and Production Engineering, 33(5), 308320.CrossRefGoogle Scholar
Bolton, A. D., Goosen, L., & Kritzinger, E. (2022). Emerging technologies for innovation and productivity management in the automotive industry: Impact of digital transformation on communication. In Emerging Technologies for Innovation Management in the Software Industry, 60–85. doi: 10.4018/978-1-7998-9059-1.ch003.Google Scholar
Boncella, R. J. (2003). Competitive intelligence and the web. Communications of the Association for Information Systems, 12(1), 21.CrossRefGoogle Scholar
Bos, M., Breza, E., & Liberman, A. (2018). The labor market effects of credit market information. The Review of Financial Studies, 31(6), 2005–2037.CrossRefGoogle Scholar
Bose, R. (2008). Competitive intelligence process and tools for intelligence analysis. Industrial Management & Data Systems, 108(4), 510–528.CrossRefGoogle Scholar
Bratu, S. (2019). Can social media influencers shape corporate brand reputation? Online followers’ trust, value creation, and purchase intentions. Review of Contemporary Philosophy, 18, 157–163. doi: 10.22381/RCP18201910.Google Scholar
Buchbinder, H., & Newson, J. (2021). Social knowledge and market knowledge: Universities in the information age. In Higher education in the information age (pp. 21–31): Routledge.CrossRefGoogle Scholar
Budler, M., & Trkman, P. (2019). The nature of management frameworks. Journal of Management & Organization, 135, 1–18.Google Scholar
Bujang, M. A., Omar, E. D., & Baharum, N. A. (2018). A review on sample size determination for Cronbach's alpha test: A simple guide for researchers. The Malaysian Journal of Medical Sciences: MJMS, 25(6), 85.CrossRefGoogle Scholar
Cable, D. M., & DeRue, D. S. (2002). The convergent and discriminant validity of subjective fit perceptions. Journal of Applied Psychology, 87(5), 875.CrossRefGoogle ScholarPubMed
Cai, L., He, X., Dai, Y., & Zhu, K. (2018). Research on B2B2C E-commerce Website Design Based on User Experience. Paper presented at the Journal of Physics: Conference Series, 1087(6), p. 062043.Google Scholar
Calof, J., Arcos, R., & Sewdass, N. (2018). Competitive intelligence practices of European firms. Technology Analysis & Strategic Management, 30(6), 658–671.CrossRefGoogle Scholar
Caputo, A., & Ayoko, O. B. (2021). Entrepreneurship, innovation and organizational behaviour. Journal of Management & Organization, 27(4), 621–625.CrossRefGoogle Scholar
Chalmeta, R. (2006). Methodology for customer relationship management. Journal of Systems and Software, 79(7), 1015–1024.CrossRefGoogle Scholar
Charband, Y., & Jafari Navimipour, N. (2018). Knowledge sharing mechanisms in the education: A systematic review of the state of the art literature and recommendations for future research. Kybernetes, 47(7), 1456–1490.CrossRefGoogle Scholar
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219.CrossRefGoogle Scholar
Chawla, V., Chanda, A., Angra, S., & Chawla, G. (2018). The sustainable project management: A review and future possibilities. Journal of Project Management, 3(3), 157–170.CrossRefGoogle Scholar
Chen, H., Chau, M., & Zeng, D. (2002). CI Spider: A tool for competitive intelligence on the web. Decision Support Systems, 34(1), 1–17.CrossRefGoogle Scholar
Cheng, C., & Wang, L. (2022). How companies configure digital innovation attributes for business model innovation? A configurational view. Technovation, 112, 102398.CrossRefGoogle Scholar
Choi, Y., Wang, J., Zhu, Y., & Lai, W.-F. (2021). Student's perception and expectation towards pharmacy education: A qualitative study of pharmacy students in a developing country. Indian Journal of Pharmaceutical Education and Research, 55(1), 63–69.CrossRefGoogle Scholar
Chowdhury, F., Audretsch, D. B., & Belitski, M. (2019). Institutions and entrepreneurship quality. Entrepreneurship Theory and Practice, 43(1), 51–81.CrossRefGoogle Scholar
Cooper, A. C. (2017). Networks, alliances, and entrepreneurship. In Strategic Entrepreneurship: Creating a New Mindset (pp. 201–222). Oxford, UK: Blackwell Publishers.Google Scholar
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297334.CrossRefGoogle Scholar
Cuijpers, M., Guenter, H., & Hussinger, K. (2011). Costs and benefits of inter-departmental innovation collaboration. Research Policy, 40(4), 565–575.CrossRefGoogle Scholar
Dabrowski, D. (2019). Market knowledge and new product performance: The mediating effects of new product creativity. Journal of Business Economics and Management, 20(6), 1168–1188.CrossRefGoogle Scholar
Das, S., Mishra, M., & Mohanty, P. (2018). The impact of customer relationship management (CRM) practices on customer retention and the mediating effect of customer satisfaction. International Journal of Management Studies, 5(1 (4)), 95. doi: 10.18843/ijms/v5i1(4)/15.CrossRefGoogle Scholar
Davison, L. (2001). Measuring competitive intelligence effectiveness: Insights from the advertising industry. Competitive Intelligence Review: Published in Cooperation with the Society of Competitive Intelligence Professionals, 12(4), 25–38.CrossRefGoogle Scholar
De Cock, R., Bruneel, J., & Bobelyn, A. (2018). Making the lean start-up method work: The role of prior market knowledge. Journal of Small Business Managemen, 58(5), 975–1002.CrossRefGoogle Scholar
de Guimarães, J. C. F., Severo, E. A., & de Vasconcelos, C. R. M. (2018). The influence of entrepreneurial, market, knowledge management orientations on cleaner production and the sustainable competitive advantage. Journal of Cleaner Production, 174, 1653–1663.CrossRefGoogle Scholar
Del Giudice, M., Soto-Acosta, P., Carayannis, E., & Scuotto, V. (2018). Emerging perspectives on business process management (BPM): IT-based processes and ambidextrous organizations, theory and practice. Business Process Management Journal, 24(5), 1070–1076.CrossRefGoogle Scholar
De Luca, L. M., & Atuahene-Gima, K. (2007). Market knowledge dimensions and cross-functional collaboration: Examining the different routes to product innovation performance. Journal of Marketing, 71(1), 95–112.CrossRefGoogle Scholar
de Oliveira, G. F., & Rabechini, R. Jr. (2019). Stakeholder management influence on trust in a project: A quantitative study. International Journal of Project Management, 37(1), 131–144.CrossRefGoogle Scholar
Derakhshan, R., Turner, R., & Mancini, M. (2019). Project governance and stakeholders: A literature review. International Journal of Project Management, 37(1), 98–116.CrossRefGoogle Scholar
Dewi, M. V. K., & Darma, G. S. (2019). The role of marketing & competitive intelligence in industrial revolution 4.0. Jurnal Manajemen Bisnis, 16(1), 1–12.CrossRefGoogle Scholar
Dewnarain, S., Ramkissoon, H., & Mavondo, F. (2019). Social customer relationship management: An integrated conceptual framework. Journal of Hospitality Marketing & Management, 28(2), 172188.CrossRefGoogle Scholar
DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation: New estimates of drug development costs. Journal of Health Economics, 22(2), 151–185.CrossRefGoogle Scholar
Djafri, N. (2018). The strategy of independent entrepreneurship management. European Research Studies Journal, 21(3), 362–370.CrossRefGoogle Scholar
Drugău-Constantin, A. L. (2019). Is consumer cognition reducible to neurophysiological functioning? Economics, Management, and Financial Markets, 14(1), 9–15.Google Scholar
Dusuki, A. W. (2008). Understanding the objectives of Islamic banking: A survey of stakeholders’ perspectives. International Journal of Islamic and Middle Eastern Finance and Management, 1(2), 132148.Google Scholar
Du Toit, A. S. (2015). Competitive intelligence research: An investigation of trends in the literature. Journal of Intelligence Studies in Business, 5(2), 14–21.CrossRefGoogle Scholar
Edvardsson, B., Frow, P., Jaakkola, E., Keiningham, T. L., Koskela-Huotari, K., Mele, C., & Tombs, A. (2018). Examining how context change foster service innovation. Journal of Service Management, 29(5), 932–955.CrossRefGoogle Scholar
Eriksson, K., & Chetty, S. (2003). The effect of experience and absorptive capacity on foreign market knowledge. International Business Review, 12(6), 673–695.CrossRefGoogle Scholar
Erramilli, M. K., & Rao, C. (1990). Choice of foreign market entry modes by service firms: Role of market knowledge. MIR: Management International Review, 30(2), 135–150.Google Scholar
Ershadi, M., Jefferies, M., Davis, P., & Mojtahedi, M. (2020). Towards successful establishment of a project portfolio management system: Business process management approach. The Journal of Modern Project Management, 8(1), 23–41.Google Scholar
Ezenwa, O., Stella, A., & Agu, A. O. (2018). Effect of competitive intelligence on competitive advantage in Innoson technical and industry limited, Enugu state, Nigeria. International Journal of Business, Economics & Management (IJBEM), 1(1), 28–39.Google Scholar
Farmania, A., Elsyah, R. D., & Tuori, M. A. (2021). Transformation of CRM activities into e-CRM: The generating e-loyalty and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 109.CrossRefGoogle Scholar
Fathi, A. U. (2019). Entrepreneurship marketing within the employment of CRM case study in korek telecom. Tikrit Journal Of Administrative and Economic Sciences, 1(41), 276–303.Google Scholar
Fazal-e-Hasan, S. M., Ahmadi, H., Mortimer, G., Grimmer, M., & Kelly, L. (2018). Examining the role of consumer hope in explaining the impact of perceived brand value on customer–brand relationship outcomes in an online retailing environment. Journal of Retailing and Consumer Services, 41, 101–111.CrossRefGoogle Scholar
Frishammar, J., Richtnér, A., Brattström, A., Magnusson, M., & Björk, J. (2019). Opportunities and challenges in the new innovation landscape: Implications for innovation auditing and innovation management. European Management Journal, 37(2), 151–164.CrossRefGoogle Scholar
Gallini, N. T. (1984). Deterrence by market sharing: A strategic incentive for licensing. The American Economic Review, 74(5), 931–941.Google Scholar
Gao, H., Shi, D., & Zhao, B. (2021). Does good luck make people overconfident? Evidence from a natural experiment in the stock market. Journal of Corporate Finance, 68, 101933.CrossRefGoogle Scholar
Garwood, D. A., & Poole, A. H. (2018). Project management as information management in interdisciplinary research: “Lots of different pieces working together”. International Journal of Information Management, 41, 14–22.CrossRefGoogle Scholar
Gasemagha, A. A., & Kowang, T. O. (2021). Project manager role in project management success. International Journal of Academic Research in Business and Social Sciences, 11(13), 1345–1355.CrossRefGoogle Scholar
Gautam, V. (2018). Shopping convenience: A case of online retailing. Review of Professional Management, 16(1), 1–16.Google Scholar
Gautam, V., & Sharma, V. (2019). Mediating role of company information in the relationships among perceived risks and purchase intentions in an online retailing context. Journal of Relationship Marketing, 18(1), 1–16.CrossRefGoogle Scholar
Geissdoerfer, M., Vladimirova, D., & Evans, S. (2018). Sustainable business model innovation: A review. Journal of Cleaner Production, 198, 401–416.CrossRefGoogle Scholar
Goel, P. (2018). From e-quality to e-loyalty in online retailing: SEM approach. ANVESHAK-International Journal of Management, 7(2), 195–210.CrossRefGoogle Scholar
Gogokhia, T., & Berulava, G. (2021). Business environment reforms, innovation and firm productivity in transition economies. Eurasian Business Review, 11(2), 221–245.CrossRefGoogle Scholar
Görög, M. (2016). Market positions as perceived by project-based organisations in the typical project business segment. International Journal of Project Management, 34(2), 187–201.CrossRefGoogle Scholar
Graessley, S., Horak, J., Kovacova, M., Valaskova, K., & Poliak, M. (2019). Consumer attitudes and behaviors in the technology-driven sharing economy: Motivations for participating in collaborative consumption. Journal of Self-Governance and Management Economics, 7(1), 25–30.Google Scholar
Greco, M., Grimaldi, M., & Cricelli, L. (2019). Benefits and costs of open innovation: The BeCO framework. Technology Analysis & Strategic Management, 31(1), 53–66.CrossRefGoogle Scholar
Groom, J. R., & David, F. R. (2001). Competitive intelligence activity among small firms. SAM Advanced Management Journal, 66(1), 12.Google Scholar
Guerola-Navarro, V., Oltra-Badenes, R., Gil-Gomez, H., & Fernández, A. I. (2021a). Customer relationship management (CRM) and innovation: A qualitative comparative analysis (QCA) in the search for improvements on the firm performance in winery sector. Technological Forecasting and Social Change, 169, 120838.CrossRefGoogle Scholar
Guerola-Navarro, V., Oltra-Badenes, R., Gil-Gomez, H., & Gil-Gomez, J. A. (2021b). Research model for measuring the impact of customer relationship management (CRM) on performance indicators. Economic Research-Ekonomska Istraživanja, 34(1), 2669–2691.CrossRefGoogle Scholar
Guo, K., & Zhang, L. (2022). Multi-objective optimization for improved project management: Current status and future directions. Automation in Construction, 139, 104256.CrossRefGoogle Scholar
Guo, J., Zhou, B., Zhang, H., Hu, C., & Song, M. (2020). Does strategic planning help firms translate slack resources into better performance? Journal of Management & Organization, 26(4), 395–407.CrossRefGoogle Scholar
Gurău, C., Ranchhod, A., & Hackney, R. (2003). Customer-centric strategic planning: Integrating CRM in online business systems. Information Technology and Management, 4(2–3), 199–214.CrossRefGoogle Scholar
Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change, 162, 120392.CrossRefGoogle Scholar
Hammer, M. (2015). What is business process management? In Handbook on business process management (Vol. 1, pp. 3–16). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
Harun, A., Rokonuzzaman, M., Prybutok, G., & Prybutok, V. R. (2018). How to influence consumer mindset: A perspective from service recovery. Journal of Retailing and Consumer Services, 42, 65–77.CrossRefGoogle Scholar
Hausman, A. (2005). Innovativeness among small businesses: Theory and propositions for future research. Industrial Marketing Management, 34(8), 773–782.CrossRefGoogle Scholar
Hayes, J., Rahman, A., & Islam, M. (2020). Shaping the future of multidimensional project management in retail industry using statistical and Big-data theories. In Rahman, A. (Ed.), Statistics for data science and policy analysis (pp. 347–360). Rahman: Springer. doi: 10.1007/978-981-15-1735-8_25.CrossRefGoogle Scholar
He, L., Mu, L., Jean, J. A., Zhang, L., Wu, H., Zhou, T., & Bu, H. (2022). Contributions and challenges of public health social work practice during the initial 2020 COVID-19 outbreak in China. The British Journal of Social Work. doi: 10.1093/bjsw/bcac077.CrossRefGoogle Scholar
Henrekson, M., & Sanandaji, T. (2020). Measuring entrepreneurship: Do established metrics capture Schumpeterian entrepreneurship? Entrepreneurship Theory and Practice, 44(4), 733–760.CrossRefGoogle Scholar
Henrie, M., & Sousa-Poza, A. (2005). Project management: A cultural literary review. Project Management Journal, 36(2), 5–14.CrossRefGoogle Scholar
Hermens, A. (2018). Creativity and innovation management: The complex dynamics of shaping forces. Journal of Management & Organization, 24(4), 451–455.CrossRefGoogle Scholar
Hofenk, D., van Birgelen, M., Bloemer, J., & Semeijn, J. (2019). How and when retailers’ sustainability efforts translate into positive consumer responses: The interplay between personal and social factors. Journal of Business Ethics, 156(2), 473–492.CrossRefGoogle Scholar
Holloway, B. B., & Beatty, S. E. (2003). Service failure in online retailing: A recovery opportunity. Journal of Service Research, 6(1), 92–105.CrossRefGoogle Scholar
Hollowell, J. C., Rowland, Z., Kliestik, T., Kliestikova, J., & Dengov, V. V. (2019). Customer loyalty in the sharing economy platforms: How digital personal reputation and feedback systems facilitate interaction and trust between strangers. Journal of Self-Governance and Management Economics, 7(1), 13–18.Google Scholar
Hou, J.-J., & Chien, Y.-T. (2010). The effect of market knowledge management competence on business performance: A dynamic capabilities perspective. International Journal of Electronic Business Management, 8(2), 96–109.Google Scholar
Hult, G. T. M., Sharma, P. N., Morgeson, F. V. III, & Zhang, Y. (2019). Antecedents and consequences of customer satisfaction: Do they differ across online and offline purchases? Journal of Retailing, 95(1), 10–23.CrossRefGoogle Scholar
Hyväri, I. (2006). Project management effectiveness in project-oriented business organizations. International Journal of Project Management, 24(3), 216–225.CrossRefGoogle Scholar
Ifeanyi, O. E., Oge, M. G., & Gozie, A. C. (2016). Effective stakeholders management in banking industries. International Journal of Information, Business and Management, 8(3), 112.Google Scholar
Ika, L. A., Söderlund, J., Munro, L. T., & Landoni, P. (2020). Cross-learning between project management and international development: Analysis and research agenda. International Journal of Project Management, 38(8), 548–558.CrossRefGoogle Scholar
Ilinova, A., Cherepovitsyn, A., & Evseeva, O. (2018). Stakeholder management: An approach in CCS projects. Resources, 7(4), 83.CrossRefGoogle Scholar
Ipang, I., Suroso, S., & Novitasari, D. (2021). A study on the relationship of e-marketing, e-CRM, and eloyalty: Evidence from Indonesia. International Journal of Data and Network Science, 5(2), 115120.Google Scholar
Isik, Z., Arditi, D., Dikmen, I., & Birgonul, M. T. (2009). Impact of corporate strengths/weaknesses on project management competencies. International Journal of Project Management, 27(6), 629637.CrossRefGoogle Scholar
Ismail, N., Idris, N., Ramli, H., Rooshdi, R. R. M., & Sahamir, S. (2018). The relationship between cost estimates reliability and BIM adoption: SEM analysis. Paper presented at the IOP Conference Series: Earth and Environmental Science, 117(1), p. 012045. IOP Publishing.Google Scholar
Ivanova, M., & Selentyeva, T. (2019). The impact of compliance costs on innovative development. Paper presented at the International Conference on Innovation and Entrepreneurship.Google Scholar
Izogo, E. E., & Jayawardhena, C. (2018). Online shopping experience in an emerging e-retailing market. Journal of Research in Interactive Marketing, 12(2), 193–214.CrossRefGoogle Scholar
Jin, J. L., Shu, C., & Zhou, K. Z. (2019). Product newness and product performance in new ventures: Contingent roles of market knowledge breadth and tacitness. Industrial Marketing Management, 76, 231–241.CrossRefGoogle Scholar
Johnson-Cramer, M. E., Berman, S. L., & Post, J. E. (2017). Re-examining the concept of ‘stakeholder management’. Unfolding stakeholder thinking (Vol. 2, pp. 145–161): Routledge.CrossRefGoogle Scholar
Jones, T. M., Harrison, J. S., & Felps, W. (2018). How applying instrumental stakeholder theory can provide sustainable competitive advantage. Academy of Management Review, 43(3), 371–391.CrossRefGoogle Scholar
Junming, Z. L. F. (2010). Perspective on the competitors from the view of commercial advertisement— —competitive intelligence based on commercial advertisement. Library and Information Service, 54(04), 91.Google Scholar
Kahn, K. B. (2018). Understanding innovation. Business Horizons, 61(3), 453–460.CrossRefGoogle Scholar
Karlsen, J. T. (2002). Project stakeholder management. Engineering Management Journal, 14(4), 19–24.CrossRefGoogle Scholar
Kasemsap, K. (2018). The role of information system within enterprise architecture and their impact on business performance. In Global business expansion: Concepts, methodologies, tools, and applications (pp. 1078–1102). Thailand: IGI Global.CrossRefGoogle Scholar
Kasemsap, K. (2019). Facilitating customer relationship management in modern business. In Advanced methodologies and technologies in digital marketing and entrepreneurship (pp. 44–56). Thailand: IGI Global. doi: 10.4018/978-1-5225-7766-9.ch004.CrossRefGoogle Scholar
Kavurmacioglu, E., Alanyali, M., & Starobinski, D. (2012). Competition in secondary spectrum markets: Price war or market sharing? Paper presented at the 2012 IEEE International Symposium on Dynamic Spectrum Access Networks.CrossRefGoogle Scholar
Kerrin, M., Mamabolo, M. A., & Kele, T. (2017). Entrepreneurship management skills requirements in an emerging economy: A South African outlook. The Southern African Journal of Entrepreneurship and Small Business Management, 9(1), 1–10.Google Scholar
Khan, F. Q., Rasheed, S., Alsheshtawi, M., Ahmed, T. M., & Jan, S. (2020). A comparative analysis of RAD and agile technique for management of computing graduation projects. Computers, Materials and Continua, 64(2), 777–796.CrossRefGoogle Scholar
Khan, R. U., Salamzadeh, Y., Iqbal, Q., & Yang, S. (2022). The impact of customer relationship management and company reputation on customer loyalty: The mediating role of customer satisfaction. Journal of Relationship Marketing, 21(1), 1–26.CrossRefGoogle Scholar
Khan, G. F., Sarstedt, M., Shiau, W.-L., Hair, J. F., Ringle, C. M., & Fritze, M. P. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM) An analysis based on social network approaches. Internet Research, 29(3), 407–429.CrossRefGoogle Scholar
Kiron, D., Kruschwitz, N., Reeves, M., & Goh, E. (2013). The benefits of sustainability-driven innovation. MIT Sloan Management Review, 54(2), 69.Google Scholar
Ko, R. K., Lee, S. S., & Lee, E. W. (2009). Business process management (BPM) standards: A survey. Business Process Management Journal, 15(5), 744–791. doi: 10.1108/14637150910987937.CrossRefGoogle Scholar
Komarova, V., Nekrasova, O., Zlobina, O., & Milaia, A. (2020). Principles and Methods of Project Management in Organization. Paper presented at the International Scientific Conference” Far East Con”(ISCFEC 2020), 128.CrossRefGoogle Scholar
Kosiba, J. P. B., Boateng, H., Okoe Amartey, A. F., Boakye, R. O., & Hinson, R. (2018). Examining customer engagement and brand loyalty in retail banking: The trustworthiness influence. International Journal of Retail & Distribution Management, 46(8), 764–779.CrossRefGoogle Scholar
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607–610.CrossRefGoogle Scholar
Kumar, P., Mokha, A. K., & Pattnaik, S. C. (2021). Electronic customer relationship management (E-CRM), customer experience and customer satisfaction: Evidence from the banking industry. Benchmarking: An International Journal, 29(2), 551–572.CrossRefGoogle Scholar
Kuratko, D. F., & Morris, M. H. (2018). Examining the future trajectory of entrepreneurship. Journal of Small Business Management, 56(1), 11–23.CrossRefGoogle Scholar
Lambert, S. C., & Davidson, R. A. (2010). Applications of the business model in studies of enterprise success, innovation and classification: An analysis of empirical research from 1996 to 2010. European Management Journal, 31(6), 668–681.CrossRefGoogle Scholar
Laursen, K. (2011). User-producer interaction as a driver of innovation: Costs and advantages in an open innovation model. Science and Public Policy, 38(9), 713–723.CrossRefGoogle Scholar
Lederman, R., Olivares, M., & Van Ryzin, G. (2014). Identifying competitors in markets with fixed product offerings. Columbia Business School Research Paper (14–10). Available at SSRN: https://ssrn.com/abstract=2374497Google Scholar
Lee, B. (2019). Human capital and labor: The effect of entrepreneur characteristics on venture success. International Journal of Entrepreneurial Behavior & Research, 25(1), 29–49.CrossRefGoogle Scholar
Lee, R. (2021). The effects of smart factory operational strategies and system management on the innovative performance of small-and medium-sized manufacturing firms. Sustainability, 13(6), 3087.CrossRefGoogle Scholar
Lee, J. W., & Xuan, Y. (2019). Effects of technology and innovation management and total factor productivity on the economic growth of China. The Journal of Asian Finance, Economics and Business, 6(2), 63–73.CrossRefGoogle Scholar
Lei, X. T., Xu, Q. Y., & Jin, C. Z. (2021). Nature of property right and the motives for holding cash: Empirical evidence from Chinese listed companies. Managerial and Decision Economics, 43(5), 1482–1500.CrossRefGoogle Scholar
Lenfle, S. (2008). Exploration and project management. International Journal of Project Management, 26(5), 469–478.CrossRefGoogle Scholar
Leonidou, E., Christofi, M., Vrontis, D., & Thrassou, A. (2020). An integrative framework of stakeholder engagement for innovation management and entrepreneurship development. Journal of Business Research, 119, 245–258.CrossRefGoogle Scholar
Levitt, R. E. (2011). Towards project management 2.0. Engineering Project Organization Journal, 1(3), 197–210.CrossRefGoogle Scholar
Li, T., & Calantone, R. J. (1998). The impact of market knowledge competence on new product advantage: Conceptualization and empirical examination. Journal of Marketing, 62(4), 13–29.CrossRefGoogle Scholar
Li, H., Dong, M., Ota, K., & Guo, M. (2016). Pricing and repurchasing for big data processing in multiclouds. IEEE Transactions on Emerging Topics in Computing, 4(2), 266–277.CrossRefGoogle Scholar
Li, H., Ota, K., Dong, M., & Guo, M. (2017). Mobile crowdsensing in software-defined opportunistic networks. IEEE Communications Magazine, 55(6), 140–145.CrossRefGoogle Scholar
Lim, S. F. W., Jin, X., & Srai, J. S. (2018). Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models. International Journal of Physical Distribution & Logistics Management, 48(3), 308–332.CrossRefGoogle Scholar
Lipset, S. M. (2018). Values, education, and entrepreneurship. Promise Of development (pp. 39–75): Routledge.CrossRefGoogle Scholar
Liu, M., Zhang, X., Ge, S., Chen, X., Wu, J., & Tian, M. (2019). An application-oriented buffer management strategy in opportunistic networks. Computers, Materials & Continua, 60(2), 559–574.CrossRefGoogle Scholar
Lobo, S., & Samaranayake, P. (2020). An innovation management assessment framework. Benchmarking: An International Journal, 27(5), 1633–1656.CrossRefGoogle Scholar
López-Robles, J. R., Otegi-Olaso, J. R., Porto-Gómez, I., Gamboa-Rosales, H., & Gamboa-Rosales, N. K. (2020). Understanding the intellectual structure and evolution of competitive intelligence: A bibliometric analysis from 1984 to 2017. Technology Analysis & Strategic Management, 32(5), 604–619.CrossRefGoogle Scholar
Lord, M. D., & Ranft, A. L. (2000). Organizational learning about new international markets: Exploring the internal transfer of local market knowledge. Journal of International Business Studies, 31(4), 573589.CrossRefGoogle Scholar
Lowson, R. H. (2001). Retail operational strategies in complex supply chains. The International Journal of Logistics Management, 12(1), 97–111.CrossRefGoogle Scholar
Madsen, H., & Ulhøi, J. P. (2001). Integrating environmental and stakeholder management. Business strategy and the Environment, 10(2), 77–88.CrossRefGoogle Scholar
Madureira, L., Popovič, A., & Castelli, M. (2021). Competitive intelligence: A unified view and modular definition. Technological Forecasting and Social Change, 173, 121086.CrossRefGoogle Scholar
Maggon, M., & Chaudhry, H. (2018). Exploring relationships between customer satisfaction and customer attitude from customer relationship management viewpoint: An empirical study of leisure travellers. FIIB Business Review, 7(1), 57–65.CrossRefGoogle Scholar
Majava, J., Nuottila, J., Haapasalo, H., & Law, K. M. (2014). Customer needs in market-driven product development: Product management and R&D standpoints. Technology and Investment, 2014, 16–25.CrossRefGoogle Scholar
Mansoori, Y., Karlsson, T., & Lundqvist, M. (2019). The influence of the lean startup methodology on entrepreneur-coach relationships in the context of a startup accelerator. Technovation, 84, 37–47.CrossRefGoogle Scholar
Mathauer, M., & Hofmann, E. (2019). Technology adoption by logistics service provide. International Journal of Physical Distribution & Logistics Management, Elixir, 49(4), 416–434.CrossRefGoogle Scholar
Mehmood, K. K., Rasheed, R., & Jaan, J. (2020). How does stakeholders pressure affect organization performance and employee wellbeing? Study of multiple mediating roles. Journal of Business and Social Review in Emerging Economies, 6(1), 55–68.CrossRefGoogle Scholar
Mehta, A. (2011). Strategic entrepreneurship: An integrated innovative entrepreneurial process for CRM implementation at Indian SMEs. Elixir, 39, 4714–4721.Google Scholar
Meilhan, D. (2019). Customer value co-creation behavior in the online platform economy. Journal of Self-Governance and Management Economics, 7(1), 19–24.Google Scholar
Meyer, J., Gonzalez Hernandez, E., & Toldos Romero, P. (2016). Online retailing is here to stay-the effects of product-context levels on consumer decision making. ACR North American Advances, 17–44.Google Scholar
Micu, A. E., Bouzaabia, O., Bouzaabia, R., Micu, A., & Capatina, A. (2019). Online customer experience in e-retailing: Implications for web entrepreneurship. International Entrepreneurship and Management Journal, 15(2), 651–675.CrossRefGoogle Scholar
Milosevic, D. Z., & Srivannaboon, S. (2006). A theoretical framework for aligning project management with business strategy. Project Management Journal, 37(3), 98–110.CrossRefGoogle Scholar
Mircică, N. (2020). Restoring public trust in digital platform operations: Machine learning algorithmic structuring of social media content. Review of Contemporary Philosophy, 19, 85–91.Google Scholar
Mirica, C.-O. (2019). The behavioral economics of decision making: Explaining consumer choice in terms of neural events. Economics, Management, and Financial Markets, 14(1), 16–22.Google Scholar
Mitra, D., & Golder, P. N. (2002). Whose culture matters? Near-market knowledge and its impact on foreign market entry timing. Journal of Marketing Research, 39(3), 350–365.CrossRefGoogle Scholar
Mochtar, K., & Arditi, D. (2001). Role of marketing intelligence in making pricing policy in construction. Journal of Management in Engineering, 17(3), 140–148.CrossRefGoogle Scholar
Mockler, R. J. (1992). Strategic intelligence systems: Competitive intelligence systems to support strategic management decision making. SAM Advanced Management Journal, 57(1), 4.Google Scholar
Mogili, R., & Natarajan, T. (2021). Online retailing and customer engagement: The changing paradigm of customer relationship management (CRM). In Insights, innovation, and analytics for optimal customer engagement (pp. 184–195). National Institute of Technology, Tiruchirappalli, India: IGI Global.CrossRefGoogle Scholar
Mohd Thas Thaker, M. A. B., Allah Pitchay, A. B., Mohd Thas Thaker, H. B., & Amin, M. F. B. (2019). Factors influencing consumers’ adoption of Islamic mobile banking services in Malaysia: An approach of partial least squares (PLS). Journal of Islamic Marketing, 10(4), 1037–1056.CrossRefGoogle Scholar
Mohnen, P., & Hall, B. H. (2013). Innovation and productivity: An update. Eurasian Business Review, 3(1), 47–65.CrossRefGoogle Scholar
Morris, P. W. (2010). Research and the future of project management. International Journal of Managing Projects in Business, 3(1), 139–146.CrossRefGoogle Scholar
Muhammad, A. (2018). An Action Agenda for Supporting Opportunity-Driven Retailing Entrepreneurs: An Analysis of External Informal Environmental Factors–A Case Study Approach.Google Scholar
Müller, R., Drouin, N., & Sankaran, S. (2019). Modeling organizational project management. Project Management Journal, 50(4), 499–513.CrossRefGoogle Scholar
Munir, F. F. A. (2018). Reliability and validity analysis on the relationship between learning space, student's satisfaction and perceived performance using SMART-PLS. International Journal of Academic Research in Business and Social Sciences, 8(1), 775–783.CrossRefGoogle Scholar
Museli, A., & Jafari Navimipour, N. (2018). A model for examining the factors impacting the near field communication technology adoption in the organizations. Kybernetes, 47(7), 1378–1400.CrossRefGoogle Scholar
Namada, J. M. (2018). Organizational learning and competitive advantage. In Handbook of research on knowledge management for contemporary business environments (pp. 86–104). Africa, Kenya: IGI Global.CrossRefGoogle Scholar
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management: Reinventing innovation management research in a digital world. MIS Quarterly, 41(1), 223–238.CrossRefGoogle Scholar
Nandamuri, P. P., Rao, K. V. G., & Mishra, M. K. (2020). Sustainable competitive advantage through business model innovation: The Indian perspective. In Disruptive technology: Concepts, methodologies, tools, and applications (pp. 191–213). India: IGI Global. doi: 10.4018/978-1-5225-9273-0.ch010.CrossRefGoogle Scholar
Nelson, C. A., Walsh, M. F., & Cui, A. P. (2020). The role of analytical CRM on salesperson use of competitive intelligence. Journal of Business & Industrial Marketing, 35(12), 2127–2137.CrossRefGoogle Scholar
Neville, B. A., & Menguc, B. (2006). Stakeholder multiplicity: Toward an understanding of the interactions between stakeholders. Journal of Business Ethics, 66(4), 377–391.CrossRefGoogle Scholar
Nguyen, D. H., de Leeuw, S., Dullaert, W., & Foubert, B. P. (2019). What is the right delivery option for you? Consumer preferences for delivery attributes in online retailing. Journal of Business Logistics, 40(4), 299–321.CrossRefGoogle Scholar
Nguyen, T., Mohamed, S., & Panuwatwanich, K. (2018). Stakeholder management in complex project: Review of contemporary literature. Journal of Engineering, Project & Production Management, 8(2), 75–89.CrossRefGoogle Scholar
Nure, G. (2018). Customer relationship management in banking system (Case of Kosovo). Journal of Internet Banking and Commerce, 23(1), 1–7.Google Scholar
Nusraningrum, D., Jaswati, J., & Thamrin, H. (2020). The quality of IT project management: The business process and the go project lean application. Manajemen Bisnis, 10(1), 10–23.CrossRefGoogle Scholar
Ortiz-de-Urbina-Criado, M., Mora-Valentín, E.-M., & Nájera-Sánchez, J.-J. (2022). Sustainability and entrepreneurship: Emerging opportunities for business and management education. Journal of Entrepreneurship in Emerging Economies. doi: 10.1108/JEEE-12-2021-0471.CrossRefGoogle Scholar
Pagoti, D. K., Mathew, S. T., & Manoharan, K. (2018). Customer relationship management (CRM) implementation: Failures, successes, and lessons learned. International Supply Chain Technology Journal, 4(04), 2–44.Google Scholar
Pan, S.-L., Tan, C.-W., & Lim, E. T. (2006). Customer relationship management (CRM) in e-government: A relational perspective. Decision Support Systems, 42(1), 237–250.CrossRefGoogle Scholar
Panda, T. K. (2013). Search engine marketing: Does the knowledge discovery process help online retailers? IUP Journal of Knowledge Management, 11(3), 56.Google Scholar
Pantano, E., & Gandini, A. (2018). Shopping as a “networked experience”: An emerging framework in the retail industry. International Journal of Retail & Distribution Management, 46(7), 690–704.CrossRefGoogle Scholar
Pantano, E., Priporas, C. V., & Dennis, C. (2018a). A new approach to retailing for successful competition in the new smart scenario. International Journal of Retail & Distribution Management, 46(3), 264–282.CrossRefGoogle Scholar
Pantano, E., Priporas, C.-V., & Stylos, N. (2018b). Knowledge push curve (KPC) in retailing: Evidence from patented innovations analysis affecting retailers’ competitiveness. Journal of Retailing and Consumer Services, 44, 150–160.CrossRefGoogle Scholar
Pedrini, M., & Ferri, L. M. (2018). Stakeholder management: A systematic literature review. Corporate Governance: The International Journal of Business in Society, 19(1), 44–59.CrossRefGoogle Scholar
Pedron, C. D., Picoto, W. N., Colaco, M., & Araújo, C. C. (2018). CRM system: The role of dynamic capabilities in creating innovation capability. BBR. Brazilian Business Review, 15, 494–511.CrossRefGoogle Scholar
Pereira, V. R., Maximiano, A. C. A., & de Souza Bido, D. (2019). Resistance to change in BPM implementation. Business Process Management Journal, 25(7), 1564–1586.CrossRefGoogle Scholar
Perez, A., & del Bosque, I. R. (2016). The stakeholder management theory of CSR: A multidimensional approach in understanding customer identification and satisfaction. International Journal of Bank Marketing, 34(5), 731–751.CrossRefGoogle Scholar
Picciotto, R. (2020). Towards a ‘new project management’ movement? An international development perspective. International Journal of Project Management, 38(8), 474–485.CrossRefGoogle Scholar
Pollack, J. (2007). The changing paradigms of project management. International Journal of Project Management, 25(3), 266–274.CrossRefGoogle Scholar
Popescu, G. H. (2015). The competitive nature and effectiveness of online retailing. Psychosociological Issues in Human Resource Management, 3(1), 101–106.Google Scholar
Popescu, G. H., & Ciurlău, F. C. (2019). Making decisions in collaborative consumption: Digital trust and reputation systems in the sharing economy. Journal of Self-Governance and Management Economics, 7(1), 7–12.Google Scholar
Pour, M. J., Mamani, E. N., & Rahimzadeh, M. (2018). How customer relationship management (CRM) and innovation influence business performance mediating role of innovation. International Journal of Customer Relationship Marketing and Management (IJCRMM), 9(2), 1–15.CrossRefGoogle Scholar
Prantl, D., & Prantl, M. (2018). Website traffic measurement and rankings: Competitive intelligence tools examination. International Journal of Web Information Systems, 14(4), 423–437.CrossRefGoogle Scholar
Preble, J. F. (2005). Toward a comprehensive model of stakeholder management. Business and Society Review, 110(4), 407–431.CrossRefGoogle Scholar
Qin, J., van der Rhee, B., Venkataraman, V., & Ahmadi, T. (2021). The impact of IT infrastructure capability on NPD performance: The roles of market knowledge and innovation process formality. Journal of Business Research, 133, 252–264.CrossRefGoogle Scholar
Rafiq, M., Fulford, H., & Lu, X. (2013). Building customer loyalty in online retailing: The role of relationship quality. Journal of Marketing Management, 29(3–4), 494–517.CrossRefGoogle Scholar
Ramakrishnan, D. (2009). CRM and Stakeholder Management. 20th SKOCH Summit, Hyatt Regency, Mumbai, 16–17.Google Scholar
Ranängen, H. (2015). Stakeholder management in reality: Moving from conceptual frameworks to operational strategies and interactions. Sustainable Production and Consumption, 3, 21–33.CrossRefGoogle Scholar
Rapp, A., Agnihotri, R., & Baker, T. L. (2011). Conceptualizing salesperson competitive intelligence: An individual-level perspective. Journal of Personal Selling & Sales Management, 31(2), 141–155.CrossRefGoogle Scholar
Ratten, V., & Jones, P. (2021). Entrepreneurship and management education: Exploring trends and gaps. The International Journal of Management Education, 19(1), 100431.CrossRefGoogle Scholar
Ravesteyn, P., & Batenburg, R. (2010). Surveying the critical success factors of BPM-systems implementation. Business Process Management Journal, 16(3), 492–507.CrossRefGoogle Scholar
Reghenzani, F., Massari, G., Santinelli, L., & Fornaciari, W. (2019). Statistical power estimation dataset for external validation GoF tests on EVT distribution. Data in Brief, 25, 104071.CrossRefGoogle ScholarPubMed
Reijers, H. A. (2006). Implementing BPM systems: The role of process orientation. Business Process Management Journal, 12(4), 389–409.CrossRefGoogle Scholar
Richards, T. J., Hamilton, S. F., & Empen, J. (2016). Attribute search in online retailing. American Journal of Agricultural Economics, aaw085, 12(4), 389–409.Google Scholar
Riquelme, I. P., Román, S., Cuestas, P. J., & Iacobucci, D. (2019). The dark side of good reputation and loyalty in online retailing: When trust leads to retaliation through price unfairness. Journal of Interactive Marketing, 47, 35–52.CrossRefGoogle Scholar
Roggeveen, A. L., Sethuraman, R., Shankar, V., Dekimpe, M., Geyskens, I., & Roggeveen, L. (2019). Journal of retailing: New initiatives and news updates. Journal of Retailing, 95(1), 1–2.CrossRefGoogle Scholar
Rong, J., Qin, T., & An, B. (2018). Dynamic pricing for reusable resources in competitive market with stochastic demand. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11591.CrossRefGoogle Scholar
Rong, J., Qin, T., & An, B. (2019). Competitive cloud pricing for long-term revenue maximization. Journal of Computer Science and Technology, 34(3), 645–656.CrossRefGoogle Scholar
Rosemann, M., de Bruin, T., & Power, B. (2007). BPM Maturity. Business process management (p. 322338): Routledge.Google Scholar
Rouach, D., & Santi, P. (2001). Competitive intelligence adds value: Five intelligence attitudes. European Management Journal, 19(5), 552–559.CrossRefGoogle Scholar
Rouf, M. A., & Akhtaruddin, M. (2018). Factors affecting the voluntary disclosure: A study by using smart PLS-SEM approach. International Journal of Law and Management, 60(6), 1498–1508.CrossRefGoogle Scholar
Rudolph, T., Wagner, T., & Fawcett, S. (2008). Project management in retailing: Integrating the behavioral dimension. The International Review of Retail, Distribution and Consumer Research, 18(3), 325–341.CrossRefGoogle Scholar
Salerno, M. S., de Vasconcelos Gomes, L. A., da Silva, D. O., Bagno, R. B., & Freitas, S. L. T. U. (2015). Innovation processes: Which process for which project? Technovation, 35, 59–70.CrossRefGoogle Scholar
Sanchez, J. M. (2018). The information technology revolution and the unsecured credit market. Economic Inquiry, 56(2), 914–930.CrossRefGoogle Scholar
Shams, S. R., Vrontis, D., Weber, Y., & Tsoukatos, E. (2018). An integrated cross-functional model of strategic innovation management in business: The implications of ten cross-functional business areas. Business models for strategic innovation (pp. 176–187): Routledge.CrossRefGoogle Scholar
Sharma, D., Stone, M., & Ekinci, Y. (2009). IT governance and project management: A qualitative study. Journal of Database Marketing & Customer Strategy Management, 16(1), 29–50.CrossRefGoogle Scholar
Sheehan, L. R., & Ritchie, J. B. (2005). Destination stakeholders exploring identity and salience. Annals of Tourism Research, 32(3), 711–734.CrossRefGoogle Scholar
Shi, G., Mou, X., & Wan, X. (2009). Designing a network acquisition system of competitive intelligence. Paper presented at the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing.CrossRefGoogle Scholar
Silvius, A., & Schipper, R. P. (2014). Sustainability in project management: A literature review and impact analysis. Social Business, 4(1), 63–96.CrossRefGoogle Scholar
Singh, S. K., & Gaur, S. S. (2018). Entrepreneurship and innovation management in emerging economies. Management Decision, 56(1), 2–5.CrossRefGoogle Scholar
Smyth, H., Gustafsson, M., & Ganskau, E. (2010). The value of trust in project business. International Journal of Project Management, 28(2), 117–129.CrossRefGoogle Scholar
Sota, S., Chaudhry, H., Chamaria, A., & Chauhan, A. (2018). Customer relationship management research from 2007 to 2016: An academic literature review. Journal of Relationship Marketing, 17(4), 277–291.CrossRefGoogle Scholar
Stead, H. (1976). The costs of technological innovation. Research Policy, 5(1), 2–9.CrossRefGoogle Scholar
Štefániková, Ľ, & Masàrovà, G. (2014). The need of complex competitive intelligence. Procedia-Social and Behavioral Sciences, 110, 669–677.CrossRefGoogle Scholar
Stuenkel, B. (2021). Personal information and artificial intelligence: Website scraping and the California Consumer Privacy Act. Colo. Tech. LJ, 19, 429.Google Scholar
Sun, W., Nan, Y., Yang, T. Z., Hu, X. Y., & Jiang, Y. (2021). Integration Innovation of Competitive Intelligence, AI and Big Data Analysis. Paper presented at the International Conference on Artificial Intelligence and Security.CrossRefGoogle Scholar
Szmigin, I., Canning, L., & Reppel, A. E. (2005). Online community: Enhancing the relationship marketing concept through customer bonding. International Journal of Service Industry Management, 16(5), 480–496.CrossRefGoogle Scholar
Taghavi, S., Riahi, L., Nasiripour, A. A., & Jahangiri, K. (2017). Modeling customer relationship management pattern using human factors approach in the hospitals of Tehran University of Medical Sciences. Health Scope, 6(2), 37165.Google Scholar
Tajuddin, N. I. I., Abdullah, R., Jabar, M. A., Jusoh, Y. Y., & Arbaiy, N. (2017). Rasch model application in validating instrument for knowledge integration in small medium enterprises. Strategic Management Journal, 1(2), 15–16.Google Scholar
Tallman, S., Luo, Y., & Buckley, P. J. (2018). Business models in global competition. Global Strategy Journal, 8(4), 517–535.CrossRefGoogle Scholar
Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49.CrossRefGoogle Scholar
Teo, T. S., & Choo, W. Y. (2001). Assessing the impact of using the internet for competitive intelligence. Information & Management, 39(1), 67–83.CrossRefGoogle Scholar
Tereso, A., Ribeiro, P., Fernandes, G., Loureiro, I., & Ferreira, M. (2019). Project management practices in private organizations. Project Management Journal, 50(1), 6–22.CrossRefGoogle Scholar
Thomas, S., & Jadeja, A. (2021). Psychological antecedents of consumer trust in CRM campaigns and donation intentions: The moderating role of creativity. Journal of Retailing and Consumer Services, 61, 102589.CrossRefGoogle Scholar
Tidd, J. (2001). Innovation management in context: Environment, organization and performance. International Journal of Management Reviews, 3(3), 169–183.CrossRefGoogle Scholar
Tien, N. H., Diem, P. T., Vu, N. T., Nhan, V. K., Bien, B. X., & Hung, N. T. (2021). The strategy of CRM system development at Mega Market Vietnam. International Journal multidisciplinary Research and Growth Evaluation, 2(4), 802–806.Google Scholar
Tomlin, B., & Wang, Y. (2011). Operational strategies for managing supply chain disruption risk. In Kouvelis, P., Dong, L., Boyabatli, O., & Li, R. (Eds.), The handbook of integrated risk management in global supply chains (pp. 79–101). doi: 10.1002/9781118115800.ch4.CrossRefGoogle Scholar
Turner, R., & Ledwith, A. (2018). Project management in small to medium-sized enterprises: Fitting the practices to the needs of the firm to deliver benefit. Journal of Small Business Management, 56(3), 475–493.CrossRefGoogle Scholar
Uribe, D. F., Ortiz-Marcos, I., & Uruburu, Á (2018). What is going on with stakeholder theory in project management literature? A symbiotic relationship for sustainability. Sustainability, 10(4), 1300.CrossRefGoogle Scholar
Van Der Merwe, A. (2002). Project management and business development: Integrating strategy, structure, processes and projects. International Journal of Project Management, 20(5), 401–411.CrossRefGoogle Scholar
Venkataraman, S. (2019). The distinctive domain of entrepreneurship research. In Katz, J. A., & Corbet, A. C. (Eds.), Seminal ideas for the next twenty-five years of advances (Advances in Entrepreneurship, Firm Emergence and Growth, Vol. 21, pp. 5–20). Bingley: Emerald Publishing Limited. doi: 10.1108/S1074-754020190000021009.CrossRefGoogle Scholar
Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmental modelling & software, 25(11), 1268–1281.CrossRefGoogle Scholar
Walle, A. H. (1999). From marketing research to competitive intelligence: Useful generalization or loss of focus? Management Decision, 37(6), 519–525.CrossRefGoogle Scholar
Wang, Z., Ramamoorthy, R., Xi, X., & Namazi, H. (2022a). Synchronization of the neurons coupled with sequential developing electrical and chemical synapses. Mathematical Biosciences and Engineering, 19(2), 1877–1890.CrossRefGoogle ScholarPubMed
Wang, Z., Ramamoorthy, R., Xi, X., Rajagopal, K., Zhang, P., & Jafari, S. (2022b). The effects of extreme multistability on the collective dynamics of coupled memristive neurons. The European Physical Journal Special Topics, 21(2), 393–405.Google Scholar
Wang, J., Yang, Y., Wang, T., Sherratt, R. S., & Zhang, J. (2020). Big data service architecture: A survey. Journal of Internet Technology, 21(2), 393–405.Google Scholar
Wannakrairoj, W., & Velu, C. (2021). Productivity growth and business model innovation. Economics Letters, 199, 109679.CrossRefGoogle Scholar
Watson, G. F. IV, Weaven, S., Perkins, H., Sardana, D., & Palmatier, R. W. (2018). International market entry strategies: Relational, digital, and hybrid approaches. Journal of International Marketing, 26(1), 30–60.CrossRefGoogle Scholar
Weiss, A. (2002). A brief guide to competitive intelligence: How to gather and use information on competitors. Business Information Review, 19(2), 39–47.CrossRefGoogle Scholar
White, D., & Fortune, J. (2002). Current practice in project management – An empirical study. International Journal of Project Management, 20(1), 1–11.CrossRefGoogle Scholar
Wikström, K., Artto, K., Kujala, J., & Söderlund, J. (2010). Business models in project business. International Journal of Project Management, 28(8), 832–841.CrossRefGoogle Scholar
Wilcox, P. A., & Gurău, C. (2003). Business modelling with UML: The implementation of CRM systems for online retailing. Journal of Retailing and Consumer Services, 10(3), 181–191.CrossRefGoogle Scholar
Woltjer, G., van Galen, M., & Logatcheva, K. (2021). Industrial innovation, labour productivity, sales and employment. International Journal of the Economics of Business, 28(1), 89–113.CrossRefGoogle Scholar
Wu, Y., & Zhu, W. (2021). The role of CSR engagement in customer-company identification and behavioral intention during the COVID-19 pandemic. Frontiers in Psychology, 12, 3171.Google ScholarPubMed
Xing, Y., Grant, D. B., McKinnon, A. C., & Fernie, J. (2010). Physical distribution service quality in online retailing. International Journal of Physical Distribution & Logistics Management, 40(5), 415–432.CrossRefGoogle Scholar
Yan, R. (2007). Market information strategies for online retailers. Journal of Revenue and Pricing Management, 6(3), 200–211.CrossRefGoogle Scholar
Yeniyurt, S., Cavusgil, S. T., & Hult, G. T. M. (2005). A global market advantage framework: The role of global market knowledge competencies. International Business Review, 14(1), 1–19.CrossRefGoogle Scholar
Yuldashevich, R. K. (2022). Entrepreneurship management mechanism. EPRA International Journal of Economics, Business and Management Studies (EBMS), 9(2), 15–18.Google Scholar
Zagzebski, L. (2017). What is knowledge? The Blackwell Guide to Epistemology, 92–116. doi: 10.1002/9781405164863.ch3.CrossRefGoogle Scholar
Zahoor, N., & Al-Tabbaa, O. (2021). Post-entry internationalization speed of SMEs: The role of relational mechanisms and foreign market knowledge. International Business Review, 30(1), 101761.CrossRefGoogle Scholar
Zareie, B., & Navimipour, N. J. (2016). The impact of electronic environmental knowledge on the environmental behaviors of people. Computers in Human Behavior, 59, 1–8.CrossRefGoogle Scholar
Zerbino, P., Aloini, D., Dulmin, R., & Mininno, V. (2018). Big data-enabled customer relationship management: A holistic approach. Information Processing & Management, 54(5), 818–846.CrossRefGoogle Scholar
Zhang, H., Chen, G., & Li, X. (2019). Resource management in cloud computing with optimal pricing policies. Computer Systems Science and Engineering, 34(4), 249–254.CrossRefGoogle Scholar
Zhang, J., Zhong, S., Wang, T., Chao, H.-C., & Wang, J. (2020). Blockchain-based systems and applications: A survey. Journal of Internet Technology, 21(1), 1–14.Google Scholar
Zheng, Y., Han, W., & Yang, R. (2021). Does government behaviour or enterprise investment improve regional innovation performance?-evidence from China. International Journal of Technology Management, 85(2–4), 274–296.CrossRefGoogle Scholar
Zins, A. H. (2014). Reflections on destination positioning analyses and identifying competitors Tourists’ perceptions and assessments (Advances in Culture, Tourism and Hospitality Research, Vol. 8, pp. 157–165). Bingley: Emerald Group Publishing Limited. doi: 10.1108/S1871-317320140000008010.CrossRefGoogle Scholar
Figure 0

Figure 1. A general processing architecture of CRM.

Figure 1

Table 1. Summarization and comparison of the mentioned related works in the field of online retailing and CRM.

Figure 2

Figure 2. The effective factors on efficient CRM systems for online retailing.

Figure 3

Table 2. Sources of sub-factors

Figure 4

Table 3. The frequency of age

Figure 5

Table 4. The frequency of work experience in Digikala

Figure 6

Table 5. The convergent validity and reliability of measurement for the measurement model

Figure 7

Table 6. The discriminant validity of the measurement model

Figure 8

Table 7. T-value and path analysis of the structural model

Figure 9

Figure 3. Path model of ß and r R2.

Figure 10

Figure 4. Path model of T- Value.

Figure 11

Table A1. Table of mathematical symbols used

Figure 12

Table A2. The Morgan table

Figure 13

Table A3. Questionnaire related to the efficient CRM systems for online retailing