Abstract
Purpose
The purpose of this paper was to assess and determine the impact of the five core technologies of Industry 4.0 (3D Printing, Big Data Analytics, Cloud Computing, Internet of Things (IoT) and Robotics) on the organizational performance of the retail industry in the context of Pakistan.
Design/methodology/approach
Pakistan's retail industry was chosen as the target sector, and the target population was composed of senior-level employees, including managers from first-level positions to top-level positions, as well as subordinate employees working under the supervision of first-level managers, possessing the technological know-how of Industry 4.0. The data were collected through a matrix-based survey questionnaire that was based on a five-point Likert scale, ranging from “strongly agree” to “strongly disagree.” The process of data analysis was conducted using IBM SPSS Statistics.
Findings
The findings obtained by this research work showed a significant relationship among the five core pillars of Industry 4.0 and the organizational performance of Pakistan's retail industry. Besides, the obtained findings provided preliminary evidence that Industry 4.0's disruptive technologies, particularly, 3D printing, big data analytics, cloud computing, IoT and robotics, could help Pakistan's retail industry solve various problems and challenges, such as meager revenues, increased expenses and unorganized systems.
Originality/value
The present study extended the theoretical body of knowledge through studying and examining Industry 4.0's five crucial factors that significantly contribute to the service sector, particularly, the retail industry, of the big emerging markets (BEM) economies, including Pakistan.
Keywords
Citation
Ali, S. and Xie, Y. (2021), "The impact of Industry 4.0 on organizational performance: the case of Pakistan's retail industry", European Journal of Management Studies , Vol. 26 No. 2/3, pp. 63-86. https://doi.org/10.1108/EJMS-01-2021-0009
Publisher
:Emerald Publishing Limited
Copyright © 2021, Shahbaz Ali and Yongping Xie
License
Published in European Journal of Management Studies. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Industrial revolutions have entirely changed handicraft and agriculture-based economies into economies based on factories, mechanized manufacturing and large-scale industries (Rogers et al., 1978). As a result, the new power sources and machines and the innovative ways of managing work significantly enhanced the existing industries' productivity and efficiency (Evans et al., 2007). The review of the literature suggests that there are four phases of the industrial revolution (Kagermann et al., 2013). The first industrial revolution took place at the end of the 18th century, and was focused on mechanization, with both water and steam being used to power the factories. The second industrial revolution took place around the beginning of the 20th century and focused on mass production and electricity. Electronics, IT systems, and automation were the focus of the third industrial revolution, which refers to the period of the 1970s. The fourth and current industrial revolution (also known as Industry 4.0 – a term originally coined by the German government in 2011) took place at the beginning of the 21st and focuses on cyber-physical systems (CPS). Industry 4.0 is an umbrella term, which utilizes various pioneering technologies, ranging from cyber-physical systems (CPS) and big data analytics through to IoT, 3D printing, and cloud computing. Under the umbrella of Industry 4.0, these cutting-edge technologies result in the unique transformation of the value chain (Zhou et al., 2015). Industry 4.0 refers to the evolution of automation, and the acquisition of data and technology, which, when combined, transforms multiple value chain activities, ranging from design to production and from marketing to distribution (Vaidya et al., 2018). The advancement of technology was the primary focus of the first three industrial revolutions, whereas Industry 4.0 emphasizes how everyday lives are being impacted by the evolution and the advancement of technology. Figure 1 shows the four phases of the industrial revolution.
The last few years have seen an increased interest in Industry 4.0, due to its far-reaching influence (Lasi et al., 2014; Lu, 2017). A number of studies have shown that the disruptive technologies of Industry 4.0 can blend breakthroughs in the industrial sector. Over the years, an enormous amount of research has been carried out to investigate the role of Industry 4.0 in the production sector, particularly the manufacturing industry (Frank et al., 2019; Qi and Tao, 2018). However, the role of Industry 4.0 in the service sector, in particular the retail industry, is still poorly understood. Furthermore, no attempts have been made to explore the impact of Industry 4.0 on the organizational performance of the retail industry in developing countries, such as Pakistan, which has made an enormous contribution to Pakistan's total economic development. Together with the wholesale industry, the retail industry consists of more than one-third of Pakistan's service sector, which itself accounts for over 53% of Pakistan's GDP (Imran, 2018).
Therefore, for the first time, this study took the unprecedented step to assess and determine the impact of the various disruptive technologies of Industry 4.0, such as 3D printing, big data analytics, cloud computing, the Internet of things (IoT) and robotics, on the organizational performance of Pakistan's retail industry, which is one of the world's fastest-growing retail markets.
This research work was carried-out in two ways. First, a thorough literature review was conducted on Industry 4.0, organizational performance and the retail industry of the BEM economies, such as Pakistan. This literature review served the purpose of: developing the problem statement, stating research questions, defining the variables relevant to the problem being investigated by this research work, proposing hypotheses for this research work, and developing a comprehensive research model for the study to be carried-out. For this purpose, a broad range of sources was examined, such as abstracting and indexing services, annual reviews, government documents, handbooks and encyclopedias, major public search engines, and review articles. Second, after completing the literature review, a quantitative approach was used to conduct the process of data analysis, which included the use of a matrix-based survey questionnaire to gather the participants' responses and verify the proposed hypotheses. IBM SPSS Statistics was used for this analysis. The participants of this research work included senior-level employees from Pakistan's retail industry, including first-level to top-level managers, as well as subordinate employees working under the supervision of first-level managers. Industry 4.0 was taken as the independent variable, and five core pillars of industry 4.0 were considered, which included 3D printing, big data analytics, cloud computing, IoT, and robotics. The organizational performance of the retail industry was taken as the dependent variable.
The findings obtained showed a significant relationship among the five core pillars of Industry 4.0 and the performance of Pakistan's retail industry's organizational, that is to say, the implementation of Industry 4.0 and its related cutting-edge technologies within the retail industry of Pakistan appear to help improve the overall organizational performance of the retail industry. The findings obtained by this study could be utilized by a wide range of audiences, such as business providers, Industry 4.0 experts, and academics and researchers related to the various fields of social sciences, to gain a comprehensive insight into the link between the five core Industry 4.0 technologies and the overall organizational performance of the retail industry. In addition, the study carried-out focused primarily on the managerial perspective of Industry 4.0, which could assist managers and policymakers from the service sector, in particular the retail industry, in making better and informed decisions, which would, in the long run, help not only improve the overall organizational performance, but also achieve a sustainable competitive advantage in the context of Industry 4.0.
1.1 Research questions
The research work carried-out intends to seek answers to the following research questions:
Does Industry 4.0 have implications for the service sector, in particular the retail industry of Pakistan?
To what extent does Industry 4.0 impact the organizational performance of the retail industry of Pakistan?
1.2 Research objectives
The objective of this research work is to assess and determine the impact of Industry 4.0 and its five core technologies, i.e. 3D printing, big data analytics, cloud computing, IoT and robotics, on the organizational performance of the retail industry in the context of Pakistan. Industry 4.0's five innovative technologies of 3D printing, big data analytics, cloud computing, IoT and robotics are taken as independent variables, and the organizational performance of the retail industry is taken as the dependent variable.
2. Literature review
2.1 Industry 4.0
Industry 4.0 has significantly transformed the way businesses and organizations work in the 21st century (Iqbal et al., 2020). It is an umbrella term, which utilizes various pioneering technologies, ranging from cyber-physical systems (CPS) and big data analytics through to IoT, 3D printing and cloud computing. Under the umbrella of Industry 4.0, these cutting-edge technologies result in the unique transformation of this value chain (Zhou et al., 2015). Figure 2 shows several technology trends and design principles of Industry 4.0.
Numerous scholars have offered explanations for Industry 4.0 (Safar et al., 2020). The study carried out by Xu et al. (2018) revealed that Industry 4.0 helps enhance the manufacturing efficiency and competency by adopting advanced information and communication technologies (ICT) in the manufacturing sector. There seems to be a general agreement in the literature on Industry 4.0 that Industry 4.0 will improve customer experience, efficiency and productivity (Chen et al., 2017; Imran, 2018). Furthermore, Industry 4.0 will provide better agility and flexibility and as a result, profitability will increase to a large extent.
Industry 4.0 enables both businesses and organizations to establish worldwide linkages that integrate their systems of warehousing, production facilities and CPS-based machinery (Singhal, 2020). As a result, several industrial processes involved in engineering, life cycle management, manufacturing, material usage and supply chain are significantly improved. Furthermore, with Industry 4.0, customers will be free to customize their products whenever required and this will help increase profits and reduce manufacturing waste. Furthermore, Industry 4.0 would allow engineering processes and businesses to make necessary changes at the eleventh hour and achieve end-to-end transparency over the whole manufacturing process – aiding in making optimized decisions (Hofmann and Rüsch, 2017). In addition, Industry 4.0 will help small businesses and startups develop and deliver downstream services. In sum, Industry 4.0 will eventually encourage novel ways of creating business models and values (Islam et al., 2018).
Industry 4.0 also has vast potential to address pressing contemporary societal challenges, such as the efficient utilization of energy and other resources, population change, and urban production. With Industry 4.0, efficiency gains and nonstop productivity of the resources can be delivered across the complete value network. Additionally, working conditions will be uniquely transformed, resulting in more consideration of social factors and population change. Machines will perform routine tasks, whereas humans will be encouraged to perform novel value-added activities. Furthermore, the flexible nature of businesses and organizations will help their employees achieve a better personal and professional life balance.
While most of the research on Industry 4.0 has focused on how Industry 4.0 impacts the manufacturing sector, the influence of Industry 4.0 is far-reaching (Vaidya et al., 2018; Zhong et al., 2017). Industry 4.0 can blend breakthroughs in business as a whole (De Marchi and Di Maria, 2020; Maresova et al., 2018; Shamim et al., 2016). Although Industry 4.0 encompasses several enabling technologies, this research work focuses on the five core pillars of Industry 4.0 from the retail industry's perspective, which are: (1) 3D printing, (2) big data analytics, (3) cloud computing, (4) IoT and (5) robotics. According to the literature available on Industry 4.0, these five core pillars, play a vital role in the retail industry (Agnihotram et al., 2017; Aktas and Meng, 2017; Caro and Sadr, 2019; Sun and Zhao, 2017; Zhao and Xu, 2017). Figure 3 shows the five core pillars of Industry 4.0.
These core pillars are briefly explained as follows:
2.1.1 3D printing
In the era of Industry 4.0, suppliers, digital enterprises, and customers all fall and work under the umbrella of industrial digital ecosystems. The existing Industry 4.0 related literature shows that 3D printing is one of the core pillars of Industry 4.0, and that it can blend breakthroughs in the industrial sector, particularly manufacturing (Almada-Lobo, 2015). Empirical evidence confirms that 3D printing can significantly disrupt the entire value chain. Furthermore, by using 3D printing, the customers is free to fully customize the products – thus enabling the enterprises to move from traditional bulk production to complete customization (Yin et al., 2018). In addition, production will turn into distributed production instead of traditional centralized production. Indeed, 3D printing will completely reshape the way different products are manufactured, delivered and maintained.
Recent research shows that apart from the manufacturing industry, 3D printing will also positively impact the healthcare, defence equipment manufacturing and retail industry. The existing 3D printing-related literature shows that the retail industry can be disrupted by incorporating 3D printing in business models and different parts of the supply chain (Tjahjono et al., 2017). 3D printing can help meet customer requirements and demands better, resulting in improved customer experience.
The current literature on the utilization of 3D printing in Industry 4.0 suggests that 3D printing plays a fundamental part in digitally transforming industries due to its quality, speed, reduced costs, safety, and reliability. It is generally agreed that the improved quality and the reduced costs of 3D printing will eventually take 3D printing up to the level of mass production. Furthermore, the range of the products produced will also increase with the further development of 3D printing (Horst et al., 2018).
2.1.2 Big data analytics
Big data refers to an enormous amount of data that is generated by machines through blogs, comments, commercial transactions, documents, messages, photos, web forms, and weblogs (Weber, 2009). Based on the data generation sources, big data can be categorized into professional big data, personal big data and social big data (Li et al., 2018). The current literature on Industry 4.0 suggests that big data analytics is one of the essential pillars of Industry 4.0, and that it helps effectively handling in smart factories (Wang et al., 2016; Witkowski, 2017). To effectively manage smart factories, the latest tools and technologies are crucial for processing big data and to convert it into a form that can provide valuable insights to businesses and organizations. In Industry 4.0, the big data generated by industries is named as the industrial big data, whereby the industries generate it through utilizing cyber-based data, cloud-based data, data, which is obtained from IoT devices and surveillance cameras, human-computer interfaces, mobiles and smart sensors. This generated data can then be sent to the cloud for advanced level processing and analysis, or it can be directly stored in the industry's database for future utilization (Yan et al., 2017). Accordingly, big data and Industry 4.0 can reshape the industrial processes in terms of resource consumption, process optimization and automation, and thus ultimately achieve sustainable development (Bettencourt, 2014; Oliveira, 2019; Wu et al., 2016; Zhang et al., 2020).
2.1.3 Cloud computing
The role of the cloud is groundbreaking in the convergence of various innovations in different fields (Almarabeh et al., 2016; Schmidt et al., 2015). It has helped dramatically evolve the market infrastructure and technological systems, transforming the correct range of services into an adaptive and flexible collection of applicants that have been continuously adapted to meet the needs of both companies and consumers (Foster et al., 2008). In a continually shifting and highly competitive environment, the corporate world has to deliver superior versatility and efficiency to gain organizational performance in the context of consumer demand fulfilment (Sether, 2016). The incapacity to provide services at the pace needed can result in business failure. Such performance failures at the market level can be attributed to the absence of flexible technologies in the host systems, resulting in output reduction and customer loss (Putnik et al., 2013). An incapable system will eventually fail, and the consumer base should predict similar outcomes if they do not adjust and prepare themselves with the requisite upgrades. A failure to fulfil this criterion will create problems for the whole industry. Cloud computing, however, can help fulfil the existing marketing objectives and meet emerging business needs. Furthermore, it can help both businesses and organizations effectively deal with operational and organizational challenges. The existing literature on cloud computing shows that cloud computing is now a critical component of contemporary society's technological development (Xue and Xin, 2016). Numerous businesses and organizations depend on it to undertake significant tasks (Aljabre, 2012; Saini et al., 2019). In addition, with the expansion of digitalization and technology elevation throughout the entire globe, several industries have integrated the computerized and digital methods of job management and rely heavily on the latest technologies to maximize their productivity (Wu et al., 2012; Zhang et al., 2014).
2.1.4 Internet of things (IoT)
IoT has diverse uses, roles and facilities in day-to-day life and across various realms; it links the real world to the digital world and enables computers and humans to be linked ubiquitously (Vermesan et al., 2011). In addition, IoT – specifically the industrial Internet of things (IIoT) – is the real ignition force behind Industry 4.0. Industry 4.0 blends conventional production and business processes with revolutionary innovations, such as CPS, machine-to-machine (M2M) communication and IoT (Zhou et al., 2015). In turn, it transforms conventional businesses into smart enterprises by utilizing self-customization, self-management, self-optimization, and self-cognition into the industrial sector (Dlamini and Johnston, 2016). Furthermore, IoT can provide industries with a wide variety of solutions and numerous traditional and modern technologies and services that will improve the quality of life and contribute to the intimate, technical, and economic prospects and benefits (Li et al., 2011; Nagy et al., 2018).
IoT uses independent, reliable and stable communication, as well as the sharing of data between devices and technologies in the modern world, that thus leads to the accomplishment of M2M collaboration and interconnectedness and the incorporation of information into applications (Chowdhury and Raut, 2019). Accordingly, computers can process knowledge and data and render human-like independent instantaneous judgments, just like rational judgments, but without any human involvement. IoT will thus build a safer environment for people, where the things surrounding them know what they want, need, and like, and behave consequently without needing specific human orders (Kanagachidambaresan et al., 2020).
2.1.5 Robotics
Competitiveness is on the increase every day in the current market climate, and is vital for making better decisions at the right moment, such that more intelligent systems can make intelligent decisions (Galin and Meshcheryakov, 2019). Machines in the shape of decades-old robots are used to execute designated roles in production processes (Albers et al., 2016). Simultaneously, humans are allocated predetermined roles in partnership, such as checking commodity consistency and discarding those that have deficiencies. Robotics plays an essential function throughout modern manufacturing and can intelligently execute their assignments, emphasizing health, efficiency and teamwork (Bahrin et al., 2016). The artificial intelligence and robotics industries are the primary innovations and they provide the technology to boost both demand and economy (Acemoglu and Restrepo, 2019).
The review of the literature shows that robotics is the cornerstone of Industry 4.0. Indeed, smart factories which incorporate robots will be wholly programmed and autonomous (Bayram and İnce, 2018; Dhanabalan and Sathish, 2018; Klincewicz, 2018). These factories will become a highly productive manufacturing environment, with computers being used at various production stages and transportation networks that share knowledge continuously without much human involvement (Bartodziej, 2017). Furthermore, adaptable automation solutions enable custom-made output, which, in turn, helps meet the increasingly complex needs of customers and consumers (Goel and Gupta, 2020).
2.2 Organizational performance
For both profit and non-profit purposes, organizations' performance is the most critical topic for any corporation. Understanding which factors influence organizational performance is crucial for managers. Nevertheless, it is not a particularly onerous job to describe, conceptualize, and evaluate performance. Therefore, organizational performance is defined as the extent to which a company can satisfy shareholders' needs and survival requirements. Consequently, performance is not reasonably associated with a certain amount of turnover, a significant market share, or providing the best goods. For it can also be achieved simultaneously through a performance description. Organizational performance can be affected by various factors combined in unusual forms which enhance efficiency or hinder output (Aluko, 2003; Ramayah et al., 2011). To succeed in the digital era, companies can use performance assessment mechanisms extracted from their skills and approaches (Mushref and Ahmad, 2011).
According to Aluko (2003), organizational performance refers to the organization's ability to satisfy the needs of customers, employees and owners alike. Researchers usually focus on relative performance measurements, because these rely on quantitative measurements and are what are used to identify opponents (Uncles, 2011). Clarifying how a company interacts with its business peers when evaluating operational efficiency is essential. Therefore, it is necessary to use a comparative industry method when creating firm performance assessments tools for companies that are sampled from a broad range of industries (Allen and Helms, 2006). According to Narver and Slater (1990), organizational performance can be measured in subjective and objective ways. The subjective measures are focused on opinions or estimates provided by respondents who are typically asked to assess their organizational performance. However, the objective measures focus on information and can be tested objectively, either by requesting respondents to disclose absolute values, or by reviewing secondary sources (Gosselin, 2005). Accordingly, performance evaluation is the essential determining factor of performance improvement, and it ultimately impacts the organizational performance.
2.3 Pakistan's retail industry
Retailing includes a transparent interaction with clients and the end-to-end management of company operations (Ghani, 2005). In recent years, Pakistan has seen a shift from tiny supermarket clusters to huge wholesalers and shopping malls. It has witnessed a major retail transformation and has been host to various multinational brands and renowned foreign wholesale chains (Khan et al., 2014).
Pakistan's retail sector is exceptional and has become the third-largest sector after agriculture and mining, however, it is still unorganized. During the last decade, the percentage of GDP of wholesale and retail trade has been around 17.5%, and that of utilities about 34%. Retail is also the second-largest employer in the world, recruiting around 16% of the world's total workforce. Since wholesale and retail trade occupies more than one-third of Pakistan's service market, representing more than 53% of Pakistan's GDP, there is a clear link between the three growths. Pakistan's wholesale and retail trade directs the country's overall economic development by having a significant impact on service contraction and expansion and the increase in GDP (Ahmed and Ahsan, 2011).
The retail market is one of Pakistan's primary sectors. However, it has faced numerous development-based challenges. Due to a decline in technical advancement, the retail industry is not producing optimum efficiency. In addition, Pakistan's online business industry is very competitive and volatile, and the retail sector is struggling to cope with numerous problems. Furthermore, relative to other neighbouring nations, including India, China and Malaysia, Pakistan's retail industry is lacking significantly. Therefore, in order to achieve a sustainable competitive advantage and improved overall organizational performance, the retail industry of Pakistan could follow the current technology trends by harnessing the true potential of Industry 4.0 and its various innovative technologies, such as 3D printing, big data analytics, cloud computing, IoT and robotics. The incorporation of these innovation technologies within the retail industry of Pakistan could help solve various organizational issues and challenges and thus result in an improvement in overall organizational performance.
3. Hypotheses development and research model
3.1 Hypotheses development
3.1.1 3D printing and organizational performance
Numerous scholars have carried out empirical studies on 3D printing to examine its potential benefits for businesses and organizations. According to Cohen (2014), 3D printing offers numerous compelling benefits, such as reduced time-to-market, increased geometrical complexity and reduced assembly and tooling costs. The work of Schniederjans (2017) demonstrates that 3D printing helps improve the performance of supply chain management and optimize operations. Furthermore, innovative designs can be significantly improved by implementing 3D printing in industries. Vanderploeg et al. (2017) claim that 3D printing helps businesses and organizations effectively develop prototypes and helps companies create customized products according to consumers' needs. In addition, 3D printing helps improve the quality of products and reduces overall lead time – leading to improved overall organizational efficiency and effectiveness.
Based on the findings of the literature, as mentioned above, this study proposes the following hypothesis:
3D Printing has a significant relationship with Organizational Performance.
3.1.2 Big data analytics and organizational performance
The existing literature on big data analytics shows that big data analytics helps businesses and organizations improve their overall organizational performance. In their study of big data analytics and organizational performance, Bogdan and Borza (2019) found that big data analytics plays a crucial part in the overall organizational performance. In addition, they claim that big data analytics increases the effectiveness of the decision-making process. Furthermore, big data analytics helps improve customer satisfaction due to better customer relationship management and the adoption of big data analytics results in increased sales and higher market share. Big data analytics helps achieve firm agility, resulting in the improvement of the capability of an organization to predict and respond to developments. Wamba et al. (2017) argue that firms' adoption of big data analytics can significantly help them achieve a sustainable competitive advantage over those that do not utilize the benefits of big data analytics. In their study of big data analytics and business performance, Popovič et al. (2018) found that big data analytics promotes cost savings, increases efficiency, encourages improved case management, and boosts customer satisfaction.
Based on the findings of the literature, as mentioned above, this study proposes the following hypothesis:
Big Data Analytics has a significant relationship with Organizational Performance.
3.1.3 Cloud computing and organizational performance
Numerous scholars have sought to understand cloud computing. The study carried out by Gangwar (2017) reveals that the adoption of cloud computing by businesses and organizations can significantly help them achieve profitability and improve their overall organizational performance. The work of Ratten (2016) demonstrates that cloud computing provides companies with a more robust mechanism of knowledge management, enabling further correlation between systems of information and organizational standards. In their discussion of cloud computing and firm performance, Son et al. (2011) claim that cloud computing helps businesses and organizations increase their overall market value. Schniederjans and Hales (2016) support the notion that cloud computing can help businesses sustain good cooperation in the supply chain and align environmental and economic performance. Ooi et al. (2018) carried out an extensive study on cloud computing and found that the adoption of cloud computing by industries improves the organization's innovativeness, productivity, and performance.
Based on the findings of the literature, as mentioned above, this study proposes the following hypothesis:
Cloud Computing has a significant relationship with Organizational Performance.
3.1.4 Internet of things (IoT) and organizational performance
Numerous scholars have examined the role of IoT in improving overall organizational performance. The study carried out by Li and Li (2017) reveals that IoT can help businesses and organizations sustain their strategic edge in the complex business ecosystem. In addition, the environments of modern businesses and technologies, aided by IoT, allow supply chain managers to address current and future issues dynamically and create more tactical and strategic procedures to improve supply chain growth, whilst finding ways to improve supply chain efficiency through the implementation of revolutionary supply chain management practices and techniques. In their discussion of IoT and firm performance, Tang et al. (2018) argue that IoT implementation supports businesses and organizations and significantly influences financial performance, profitability, and consumer demand. The work of Collymore (2017) demonstrates that IoT can help organizations boost their corporate strategic edge and their overall performance, and that IoT assists companies to compile, track, and evaluate the performance of their organizational processes. As a result, the improvement in a company's operational performance can have an enormous effect on the organizations' net revenues.
Based on the findings of the literature, as mentioned above, this study proposes the following hypothesis:
Internet of Things (IoT) has a significant relationship with Organizational Performance.
3.1.5 Robotics and organizational performance
The review of the literature shows that robotics plays a crucial role in improving overall organizational performance. The work of Graetz and Michaels (2018) demonstrates that robotics helps businesses and organizations by not only increasing overall throughput but also by reducing output costs. In addition, they found that robotics substantially impacts and enhances overall labour productivity. The study carried out by Lichtenthaler (2019) reveals that artificial intelligence and robotics can help firms achieve a sustainable competitive advantage over those firms that do not benefit from these disruptive technologies. Fragapane et al. (2020) claimed that robotics boosts the flexibility of the production industry, which, in turn, increases the organization's capacity to respond to customers' requirements in a reasonable time frame and maximize the profitability of the production chain, without incurring unnecessary costs and or the need to commit additional resources. The work of Morikawa (2016) demonstrates that artificial intelligence and robotics significantly contribute to the improvement of the overall productivity performance of the manufacturing and the service industries.
Based on the findings of the literature, as mentioned above, this study proposes the following hypothesis:
Robotics has a significant relationship with Organizational Performance.
3.2 Research model
Based on the formal systematic literature review and findings and interpretations of the literature, a comprehensive research model was proposed in this study, which is shown in Figure 4.
4. Materials and methods
4.1 Sampling and data collection
Pakistan's retail industry was chosen as the target sector, with the target population being composed of senior-level employees from Pakistan's retail industry, including managers ranging from first-level positions to top-level positions, as well as subordinate employees working under the supervision of first-level managers who possess the technological know-how of Industry 4.0. In order ensure whether the participants possessed the technological know-how of Industry 4.0, short informal interviews related to Industry 4.0 and its related technologies were carried out prior to the distribution of the survey questionnaire, which helped shortlist the most suitable participants for the subsequent research work, by excluding those who were not aware of Industry 4.0 and its related technologies. The data were collected through a matrix-based survey questionnaire. The work by Comrey and Lee (2013) demonstrates that the acceptable size of a sample should be more than 200 respondents. Drawing on their work, Haque et al. (2017) highlight that any sample size of more than 200 is adequate in social science research to reach a reasonable inference. Accordingly, 630 survey questionnaires were sent out both online and offline to the respondents, with adequate instructions on how to fill out the survey questionnaires properly, as well as the purpose of the research, which proved to be efficient. The online methods of questionnaire distribution included Google forms, e-mail and social media platforms, whereby the offline methods of questionnaire distribution included visiting the participants in person and mailing the hard copies of the survey questionnaires to those who were not available for an in-person meeting, due to personal reasons. After sending out the survey questionnaires both online and offline, 477 responses were collected, with a 75.71% response rate. Out of these 477 responses, 8 responses were found invalid. 469 survey questionnaires were thus used finally, which is an acceptable sample size for drawing conclusions. The sampling method used for the subsequent research work was representative sampling, in order to ensure that only those participants are targeted who truly represent the larger population, which, as a result, helps improve the overall accuracy of the research work to be carried out.
4.2 Research instruments
A quantitative approach was employed to gather the participants' responses, involving the use of a matrix-based survey questionnaire – based on a five-point Likert scale, ranging from “strongly agree” to “strongly disagree”. The survey questionnaire was designed and then divided into two main segmentations: (1) the demographic segmentation and (2) the attitudinal and behavioural segmentation. The demographic segmentation part of the survey questionnaire was based on respondents' demographic profiles, comprising four items: (1) gender, (2) age, (3) level of education and (4) work experience. Table 1 shows respondents' demographic profiles in terms of their gender, age, level of education, and work experience.
The attitudinal and behavioural segmentation part of the survey questionnaire was based on the attitudinal and behavioural research items – based on the key independent and dependent variables of this study, i.e. 3D printing, big data analytics, cloud computing, IoT, robotics and organizational performance.
The 3D printing section was comprised of five items, which were taken from the study by Schniederjans (2017). The participants were asked to answer a variety of questions on 3D printing, such as “3D printing increases our organization's productivity.”
The big data analytics section contained eight items, from which six items were taken from the study of NewVantage Partners (2012), whereas two items were newly developed. The participants were asked to answer a variety of questions on big data analytics, such as “using big data analytics enables our organization to make better, fact-based decisions.”
The cloud computing section was comprised of seven items, which were newly developed. The participants were asked to answer various questions on cloud computing, such as “cloud computing helps our organization achieve better collaboration across teams.”
The IoT section contained seven items, out of which three items were taken from the study of Imran (2018), whereas four items were newly developed. The participants were asked to answer various questions on IoT, such as “using IoT helps our organization provide better communication between employees.”
The robotics section was comprised of six items, which were all newly developed. The participants were asked to answer various questions on robotics, such as “using robotics enables our organization to reduce risks.”
The organizational performance section contained five items, which were taken from the study of Powell (1995). The participants were asked to answer a variety of questions on the organizational performance, such as “over the past three years, our financial performance has been outstanding.”
5. Results and discussion
5.1 Results
5.1.1 Reliability and validity analysis
IBM SPSS Statistics was used for the data analysis. The reliability of the research instrument was tested using Cronbach's alpha, whereas the KMO measure of sampling adequacy and Bartlett's test of sphericity were used to test the research instrument's validity. Table 2 shows the results of the reliability and validity analysis.
The existing literature on Cronbach's alpha shows that to pass the reliability test, each variable of the research instrument needs to have a Cronbach's alpha value greater than or equal to 0.7. Table 2 shows that each variable of the research instrument had a Cronbach's alpha value greater than 0.7. Therefore, the reliability test was successfully passed by the research instrument. For passing the validity test, it is generally agreed that each variable of the research instrument should have a KMO value of greater than or equal to 0.5. Furthermore, each variable of the research instrument should also have a p-value (Sig.) of less than 0.05. Table 2 shows that each variable of the research instrument had a KMO value of greater than 0.5. Additionally, each variable of the research instrument had a p-value of 0.000. The validity test was accordingly also successfully passed by the research instrument. It can therefore be concluded that the research instrument was proved to be both reliable and valid.
5.1.2 Correlation and regression analysis
A correlation analysis was carried out to evaluate the co-relationship among all the variables of the research instrument. Table 3 shows the results of the correlation analysis for all the variables of the research instrument, and Table 4 shows the correlation analysis results between Industry 4.0 (a single variable computed by adding the five variables of Industry 4.0) and organizational performance.
Table 3 shows that positive and statistically significant correlations were obtained for 3D printing, big data analytics, cloud computing, IoT, robotics, and organizational performance. Furthermore, when Industry 4.0 was taken as a single variable, a correlation between Industry 4.0 and organizational performance was obtained, which was also positive and statistically significant (as shown in Table 4). Figure 5 shows the research model with the obtained correlations.
All variables of the research instrument were positive and significantly correlated with each other. Therefore, the obtained findings were found suitable for carrying out the regression analysis.
The regression analysis was carried out in two ways: (1) a regression analysis among each variable of Industry 4.0 and organizational performance, and (2) a regression analysis between Industry 4.0 (a single variable computed by adding the five variables of Industry 4.0) and organizational performance. For carrying out a regression analysis for all the variables of Industry 4.0 and organizational performance, 3D printing, big data analytics, cloud computing, IoT and robotics were taken as the independent variables, and organizational performance was taken as the dependent variable. Table 5 shows the results of the regression analysis for the five variables of Industry 4.0 and organizational performance.
It is generally agreed that a hypothesis with a t-value of greater than or equal to 1.96 is considered as an accepted hypothesis, whereas a hypothesis with a t-value of less than 1.96 is considered as a rejected hypothesis. Table 5 shows that the results yielded a statistically significant relationship between 3D printing and organizational performance. Hypothesis 1 was therefore accepted. Similarly, a statistically significant relationship was obtained between big data analytics and organizational performance and thus Hypothesis 2 was not rejected. Additionally, the relationship between cloud computing and organizational performance was statistically significant. Thus, Hypothesis 3 was accepted. Furthermore, the results yielded a statistically significant relationship between IoT and organizational performance and therefore Hypothesis 4 was not rejected. It was also found that the relationship between robotics and organizational performance was statistically significant and Hypothesis 5 was thus accepted. In addition, the β-values, shown in Table 5, indicate that all of the obtained relationships were positive. The hypotheses along with their statistical indicators of relevance are shown in Table 6.
In addition, we also carried out the regression analysis between Industry 4.0 (a single variable computed by adding the five variables of Industry 4.0) and organizational performance, where Industry 4.0 was taken as the independent variable, and organizational performance was taken as the dependent variable. Table 7 shows the results of the regression analysis between Industry 4.0 and organizational performance.
Table 7 shows that the results also yielded a statistically significant relationship between Industry 4.0 and organizational performance. Furthermore, the β-value, shown in Table 7, indicates that the relationship obtained was positive.
5.2 Discussions
This study was carried out to assess and determine the impact of the five disruptive technologies of Industry 4.0 on the organizational performance of Pakistan's retail industry, which is among the world's fastest-growing retail markets. The findings obtained provided convincing evidence of a strong association among the five core pillars of Industry 4.0 and Pakistan's retail industry's organizational performance, that is to say, the implementation of Industry 4.0 and its related cutting-edge technologies in the retail industry of Pakistan could help improve the overall organizational performance of retail.
The findings obtained demonstrate that there was a statistically significant relationship between 3D printing and organizational performance. A t-value of 10.040 and a β-value of 0.421 indicate that 3D printing had a positive and direct relationship with organizational performance. The obtained results provided preliminary evidence that 3D printing can help accomplish tasks and activities more quickly and that 3D printing can increase organizational productivity. Thus, the implementation of 3D printing leads to an increase in the overall organizational performance of Pakistan's retail industry. Our findings are consistent with the results obtained by Cohen (2014) and Schniederjans (2017).
The results yielded a statistically significant relationship between big data analytics and organizational performance. A t-value of 10.375 and a β-value of 0.433 indicate a positive and direct relationship between big data analytics and organizational performance. The findings obtained provided preliminary evidence that big data analytics can improve customer experience and lead to the making of better, fact-based decisions, and achieve more efficient organizational operations. In addition, big data analytics can enable an increase in sales and reduce risks. Accordingly, the implementation of big data analytics would increase the overall organizational performance of Pakistan's retail industry. Furthermore, our findings are in line with the studies carried out by Bogdan and Borza (2019), Wamba et al. (2017), and Popovič et al. (2018).
The findings obtained also show that there was a statistically significant relationship between cloud computing and organizational performance. A t-value of 10.527 and a β-value of 0.438 indicate that cloud computing had a positive and direct relationship with organizational performance. The results obtained provided preliminary evidence that cloud computing can help increase organizational agility and that cloud computing also helps achieve better collaboration across teams. Thus, the implementation of cloud computing would increase the overall organizational performance of Pakistan's retail industry. In addition, our findings are consistent with the results obtained by Gangwar (2017), Son et al. (2011), and Ooi et al. (2018).
The findings obtained yielded a statistically significant relationship between IoT and organizational performance. A t-value of 10.194 and a β-value of 0.427 indicate that the relationship between IoT and organizational performance was positive and direct. The results obtained provided preliminary evidence that IoT could provide better communication between employees. Additionally, IoT can increase organizational efficiency and customer satisfaction. Therefore, the implementation of IoT would increase the overall organizational performance of Pakistan's retail industry. Our findings are in line with the studies carried out by Collymore (2017) and Tang et al. (2018).
The results obtained demonstrate that there was a statistically significant relationship between robotics and organizational performance. A t-value of 12.112 and a β-value of 0.489 indicate that robotics had a positive and direct relationship with organizational performance. The findings obtained provided preliminary evidence that robotics can help reduce risks. Furthermore, robotics can increase customer experience and customer satisfaction. Accordingly, the implementation of robotics would increase the overall organizational performance of Pakistan's retail industry. Furthermore, our findings are consistent with the results obtained by Fragapane et al. (2020) and Morikawa (2016).
The findings obtained show that there was also a statistically significant relationship between Industry 4.0 (a single variable computed by adding the five variables of Industry 4.0) and organizational performance. A t-value of 13.877 and a β-value of 0.540 indicate a positive and direct relationship between Industry 4.0 and organizational performance; this means that the implementation of Industry 4.0 would increase the overall organizational performance of Pakistan's retail industry.
6. Conclusions and recommendations for further research
6.1 Conclusions
This study took the unprecedented step to assess and determine the impact of the five key technologies of Industry 4.0, i.e. 3D printing, big data analytics, cloud computing, IoT, and robotics, on the organizational performance of Pakistan's retail industry. The target population was composed of senior-level employees from Pakistan's retail industry, including managers ranging from first-level positions to top-level positions, as well as subordinate employees working under the supervision of first-level managers, who possess the technological know-how of Industry 4.0. The data were collected through a matrix-based survey questionnaire. The findings obtained yielded some interesting findings and provided convincing evidence of a strong association among the five core pillars of Industry 4.0 and the organizational performance of Pakistan's retail industry. In addition, the results obtained provide preliminary evidence that the disruptive technologies of Industry 4.0, in particular, 3D printing, big data analytics, cloud computing, IoT and robotics, could help Pakistan's retail industry in solving various problems and challenges, such as meager revenues, increased expenses and unorganized systems. It can therefore be concluded that the implementation of Industry 4.0 in Pakistan's retail industry would increase the overall organizational performance of Pakistan's retail industry. Accordingly, Pakistan's retail industry should introduce Industry 4.0 through a comprehensive strategy, covering the aforementioned five disruptive technologies of Industry 4.0.
6.2 Research limitations and recommendations for further research
The findings obtained are not generalizable to the entire service sector. Future research should therefore explore the impact of the various disruptive technologies of Industry 4.0 on the organizational performance of the other industries of the service sector. Furthermore, our findings are not generalizable beyond the population studied and thus future studies should take other countries into account. Future scholars and researchers from both developed and developing countries are encouraged to investigate and clarify the relationship between Industry 4.0 and the organizational performance of the production and service sectors of their respective countries, as this would help further the understanding of Industry 4.0 in the context of the production and service sectors of both developed and developing countries. In addition, future research should also compare and contrast organizations' overall performance, both before and after the implementation of the various disruptive technologies of Industry 4.0, as this would help assess the extent to which the implementation of Industry 4.0 impacts the overall performance of businesses and organizations.
Figures
Figure 1
Four phases of the industrial revolution
Figure 2
Technology trends and design principles of industry 4.0
Figure 3
Five core pillars of industry 4.0
Figure 4
Research model
Figure 5
Research model with obtained correlations
The demographic profiles of the respondents (N = 469)
| Variables | Frequencies | Percentages (%) | |
|---|---|---|---|
| Gender | Male | 316 | 67.4 |
| Female | 153 | 32.6 | |
| Total | 469 | 100 | |
| Age | 18–25 Years | 128 | 27.3 |
| 26–35 Years | 191 | 40.7 | |
| 36–45 Years | 107 | 22.8 | |
| 46–55 Years | 43 | 9.2 | |
| 56 Years or above | 0 | 0 | |
| Total | 469 | 100 | |
| Level of education | SSC/HSSC/Diploma | 7 | 1.5 |
| BA/BS/BE | 290 | 61.8 | |
| MA/MS/ME | 145 | 30.9 | |
| MPhil | 27 | 5.8 | |
| PhD | 0 | 0 | |
| Total | 469 | 100 | |
| Work experience | 1–2 Years | 128 | 27.3 |
| 3–5 Years | 85 | 18.1 | |
| 6–8 Years | 58 | 12.4 | |
| 9–12 Years | 71 | 15.1 | |
| 13–16 Years | 60 | 12.8 | |
| 17–20 Years | 32 | 6.8 | |
| 21 Years or above | 35 | 7.5 | |
| Total | 469 | 100 | |
Reliability and validity analysis
| Variable | No. of items | Cronbach's alpha (α) | KMO measure of sampling adequacy | Bartlett's test of sphericity | ||
|---|---|---|---|---|---|---|
| Approx. Chi-square | df | Sig | ||||
| 3D printing | 5 | 0.930 | 0.883 | 1888.292 | 10 | 0.000 |
| Big data analytics | 8 | 0.951 | 0.947 | 3598.760 | 28 | 0.000 |
| Cloud computing | 7 | 0.943 | 0.931 | 3187.108 | 21 | 0.000 |
| Internet of Things (IoT) | 7 | 0.950 | 0.945 | 3475.500262 | 21 | 0.000 |
| Robotics | 6 | 0.935 | 0.922 | 2529.185 | 15 | 0.000 |
| Organizational performance | 5 | 0.953 | 0.899 | 2806.951 | 10 | 0.000 |
Correlations among all variables of the research instrument
| Correlations | ||||||
|---|---|---|---|---|---|---|
| 3D printing | Big data analytics | Cloud computing | Internet of Things (IoT) | Robotics | Organizational performance | |
| 3D printing | 1 | |||||
| Big data analytics | 0.642** | 1 | ||||
| Cloud computing | 0.529** | 0.669** | 1 | |||
| Internet of Things (IoT) | 0.472** | 0.567** | 0.708** | 1 | ||
| Robotics | 0.527** | 0.471** | 0.518** | 0.611** | 1 | |
| Organizational performance | 0.421** | 0.433** | 0.438** | 0.427** | 0.489** | 1 |
Note(s): **Correlation is significant at the 0.01 level (2-tailed)
Correlations between industry 4.0 and organizational performance
| Correlations | ||
|---|---|---|
| Industry 4.0 | Organizational performance | |
| Industry 4.0 | 1 | |
| Organizational performance | 0.540** | 1 |
Note(s): **Correlation is significant at the 0.01 level (2-tailed)
Regressions among five variables of Industry 4.0 and Organizational Performance
| Regressions (organizational performance) | |||
|---|---|---|---|
| β | t | Sig | |
| 3D printing | 0.421 | 10.040 | 0.000 |
| Big data analytics | 0.433 | 10.375 | 0.000 |
| Cloud computing | 0.438 | 10.527 | 0.000 |
| Internet of Things (IoT) | 0.427 | 10.194 | 0.000 |
| Robotics | 0.489 | 12.112 | 0.000 |
Hypotheses along with their statistical indicators of relevance
| Hypotheses | Statistical indicators of relevance | |||
|---|---|---|---|---|
| t | Correlations | Sig | End result | |
| Hypothesis 1 | 10.040 | 0.421 | 0.000 | Accepted |
| Hypothesis 2 | 10.375 | 0.433 | 0.000 | Accepted |
| Hypothesis 3 | 10.527 | 0.438 | 0.000 | Accepted |
| Hypothesis 4 | 10.194 | 0.427 | 0.000 | Accepted |
| Hypothesis 5 | 12.112 | 0.489 | 0.000 | Accepted |
Regressions between industry 4.0 and organizational performance
| Regressions (organizational performance) | |||
|---|---|---|---|
| β | t | Sig | |
| Industry 4.0 | 0.540 | 13.877 | 0.000 |
References
Acemoglu, D. and Restrepo, P. (2019), Artificial Intelligence, Automation, and Work, University of Chicago Press.
Agnihotram, G., Vepakomma, N., Trivedi, S., Laha, S., Isaacs, N., Khatravath, S., Naik, P. and Kumar, R. (2017), “Combination of advanced robotics and computer vision for shelf analytics in a retail store”, International Conference on Information Technology, IEEE, pp. 119-124.
Ahmed, A. and Ahsan, H. (2011), “Contribution of services sector in the economy of Pakistan”, Working Papers and Research Reports.
Aktas, E. and Meng, Y. (2017), “An exploration of big data practices in retail sector”, Logistics, Vol. 1 No. 2, p. 12.
Albers, A., Gladysz, B., Pinner, T., Butenko, V. and Stürmlinger, T. (2016), “Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems”, Procedia Cirp, Vol. 52 No. 1, pp. 262-267.
Aljabre, A. (2012), “Cloud computing for increased business value”, International Journal of Business and Social Science, Vol. 3 No. 1, pp. 234-239.
Allen, R.S. and Helms, M.M. (2006), “Linking strategic practices and organizational performance to Porter's generic strategies”, Business Process Management Journal, Vol. 12, pp. 433-454.
Almada-Lobo, F. (2015), “The Industry 4.0 revolution and the future of manufacturing execution systems (MES)”, Journal of Innovation Management, Vol. 3 No. 4, pp. 16-21.
Almarabeh, T., Majdalawi, Y.K. and Mohammad, H. (2016), Cloud Computing of E-Government, Scientific Research Publishing.
Aluko, M.A.O. (2003), “The impact of culture on organizational performance in selected textile firms in Nigeria”, Nordic Journal of African Studies, Vol. 12 No. 2, pp. 164-179.
Bahrin, M.A.K., Othman, M.F., Azli, N.H.N. and Talib, M.F. (2016), “Industry 4.0: a review on industrial automation and robotic”, Jurnal Teknologi, Vol. 78, pp. 6-13.
Bartodziej, C.J. (2017), “Technologies and functions of the concept Industry 4.0”, The Concept Industry 4.0, pp. 51-78.
Bayram, B. and İnce, G. (2018), “Advances in robotics in the era of industry 4.0”, Industry 4.0: Managing the Digital Transformation, Springer, Cham.
Bettencourt, L.M. (2014), “The uses of big data in cities”, Big Data, Vol. 2 No. 1, pp. 12-22.
Bogdan, M. and Borza, A. (2019), “Big data analytics and organizational performance: a Meta-analysis study”, Management and Economics Review, Vol. 4 No. 2, pp. 1-13.
Caro, F. and Sadr, R. (2019), “The internet of things (IoT) in retail: bridging supply and demand”, Business Horizons, Vol. 62 No. 1, pp. 47-54.
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M. and Yin, B. (2017), “Smart factory of industry 4.0: key technologies, application case, and challenges”, IEEE Access, Vol. 6, pp. 6505-6519.
Chowdhury, A. and Raut, S.A. (2019), “Benefits, challenges, and opportunities in adoption of industrial IoT”, International Journal of Computational Intelligence and IoT, Vol. 2 No. 4, pp. 822-828.
Cohen, D.L. (2014), “Fostering mainstream adoption of industrial 3D printing: understanding the benefits and promoting organizational readiness”, 3D Printing and Additive Manufacturing, Vol. 1 No. 2, pp. 62-69.
Collymore, A. (2017), “IOT and IIOT: innovation, competitive advantage and firm performance of diverse environments”, International Journal of Information Research and Review, Vol. 4 No. 12, pp. 4793-4800.
Comrey, A.L. and Lee, H.B. (2013), A First Course in Factor Analysis, Psychology Press.
De Marchi, V. and Di Maria, E. (2020), “Achieving circular economy via the adoption of industry 4.0 technologies: a knowledge management perspective”, Knowledge Management and Industry 4.0, Springer, Cham, pp. 163-178.
Dhanabalan, T. and Sathish, A. (2018), “Transforming Indian industries through artificial intelligence and robotics in industry 4.0”, International Journal of Mechanical Engineering and Technology, Vol. 9 No. 10, pp. 835-845.
Dlamini, N.N. and Johnston, K. (2016), “The use, benefits and challenges of using the Internet of Things (IoT) in retail businesses: a literature review”, International Conference on Advances in Computing and Communication Engineering, IEEE, pp. 430-436.
Evans, S., Partidário, P.J. and Lambert, J. (2007), “Industrialization as a key element of sustainable product-service solutions”, International Journal of Production Research, Vol. 45 Nos 18-19, pp. 4225-4226.
Foster, I., Zhao, Y., Raicu, I. and Lu, S. (2008), “Cloud computing and grid computing 360-degree compared”, IEEE Access, pp. 1-10.
Fragapane, G., Ivanov, D., Peron, M., Sgarbossa, F. and Strandhagen, J.O. (2020), “Increasing flexibility and productivity in industry 4.0 production networks with autonomous mobile robots and smart intralogistics”, Annals of Operations Research, pp. 1-19.
Frank, A.G., Dalenogare, L.S. and Ayala, N.F. (2019), “Industry 4.0 technologies: implementation patterns in manufacturing companies”, International Journal of Production Economics, Vol. 210, pp. 15-26.
Galin, R. and Meshcheryakov, R. (2019), “Automation and robotics in the context of Industry 4.0: the shift to collaborative robots”, IOP Conference Series: Materials Science and Engineering, IOP Publishing, Vol. 537, pp. 032073-032077.
Gangwar, H. (2017), “Cloud computing usage and its effect on organizational performance”, Human Systems Management, Vol. 36 No. 1, pp. 13-26.
Ghani, J.A. (2005), “Consolidation in Pakistan's retail sector”, Asian Journal of Management Cases, Vol. 2 No. 2, pp. 137-161.
Ghobakhloo, M. (2018), “The future of manufacturing industry: a strategic roadmap toward Industry 4.0”, Journal of Manufacturing Technology Management, Vol. 29, pp. 910-936.
Goel, R. and Gupta, P. (2020), “Robotics and industry 4.0”, A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development, Springer, Cham, pp. 157-169.
Gosselin, M. (2005), “An empirical study of performance measurement in manufacturing firms”, International Journal of Productivity and Performance Management, Vol. 54 Nos 5-6, pp. 419-437.
Graetz, G. and Michaels, G. (2018), “Robots at work”, Review of Economics and Statistics, Vol. 100 No. 5, pp. 753-768.
Haque, A.U., Faizan, R. and Cockrill, A. (2017), “The relationship between female representation at strategic level and firm's competitiveness: evidences from cargo logistic firms of Pakistan and Canada”, Polish Journal of Management Studies, Vol. 15, pp. 69-81.
Hofmann, E. and Rüsch, M. (2017), “Industry 4.0 and the current status as well as future prospects on logistics”, Computers in Industry, Vol. 89, pp. 23-34.
Horst, D.J., Duvoisin, C.A. and de Almeida Vieira, R. (2018), “Additive manufacturing at Industry 4.0: a review”, International Journal of Engineering and Technical Research, Vol. 8 No. 8, pp. 3-8.
Imran, M. (2018), “Influence of industry 4.0 on the production and service sectors in Pakistan: evidence from textile and logistics industries”, Social Sciences, Vol. 7 No. 12, p. 246.
Iqbal, M.S., Rahim, Z.A. and Hussain, S.A. (2020), “Industry 4.0 revolution and challenges in developing countries: a case study on Pakistan”, Journal of Advanced Research in Business and Management Studies, Vol. 21 No. 1, pp. 40-52.
Islam, M.A., Jantan, A.H., Hashim, H., Chong, C.W., Abdullah, M.M. and Abdul Hamid, A.B. (2018), “Fourth industrial revolution in developing countries: a case on Bangladesh”, Journal of Management Information and Decision Sciences, Vol. 21 No. 1, pp. 1-9.
Kagermann, H., Helbig, J., Hellinger, A. and Wahlster, W. (2013), Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry; Final Report of the Industrie 4.0 Working Group, Acatech, Forschungsunion.
Kanagachidambaresan, G.R., Anand, R., Balasubramanian, E. and Mahima, V. (2020), Internet of Things for Industry 4.0, Springer, Cham.
Khan, S.A., Liang, Y. and Shahzad, S. (2014), “Adoption of electronic supply chain management and e-commerce by small and medium enterprises and their performance: a survey of SMEs in Pakistan”, American Journal of Industrial and Business Management, Vol. 4, pp. 433-444.
Klincewicz, K. (2018), “Robotics in the context of industry 4.0: patenting activities in Poland and their comparison with global developments”, Problemy Zarządzania, Vol. 17, Nos 2(82), pp. 53-95.
Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M. (2014), “Industry 4.0”, Business and Information Systems Engineering, Vol. 6 No. 4, pp. 239-242.
Li, B. and Li, Y. (2017), “Internet of things drives supply chain innovation: a research framework”, International Journal of Organizational Innovation, Vol. 9 No. 3, pp. 71-92.
Li, J., Huang, Z. and Wang, X. (2011), “Notice of retraction: countermeasure research about developing internet of things economy: a case of Hangzhou city”, International Conference on E-Business and E-Government, IEEE, pp. 1-5.
Li, J., Xu, L., Tang, L., Wang, S. and Li, L. (2018), “Big data in tourism research: a literature review”, Tourism Management, Vol. 68, pp. 301-323.
Lichtenthaler, U. (2019), “An intelligence-based view of firm performance: profiting from artificial intelligence”, Journal of Innovation Management, Vol. 7 No. 1, pp. 7-20.
Lu, Y. (2017), “Industry 4.0: a survey on technologies, applications and open research issues”, Journal of Industrial Information Integration, Vol. 6, pp. 1-10.
Maresova, P., Soukal, I., Svobodova, L., Hedvicakova, M., Javanmardi, E., Selamat, A. and Krejcar, O. (2018), “Consequences of industry 4.0 in business and economics”, Economies, Vol. 6 No. 3, p. 46.
Morikawa, M. (2016), The Effects of Artificial Intelligence and Robotics on Business and Employment: Evidence from a Survey on Japanese Firms, The Research Institute of Economy, Trade and Industry.
Mushref, A.M. and Ahmad, S.B. (2011), “The relationship between knowledge management and business performance: an empirical study in Iraqi industry”, World Review of Business Research, Vol. 1 No. 2, pp. 35-50.
Nagy, J., Oláh, J., Erdei, E., Máté, D. and Popp, J. (2018), “The role and impact of Industry 4.0 and the internet of things on the business strategy of the value chain—the case of Hungary”, Sustainability, Vol. 10 No. 10, p. 3491.
Narver, J.C. and Slater, S.F. (1990), “The effect of a market orientation on business profitability”, Journal of Marketing, Vol. 54 No. 4, pp. 20-35.
NewVantage Partners, L.L.C. (2012), Big Data Executive Survey 2012, NewVantage Partners, LLC.
Oliveira, A.L. (2019), “Biotechnology, big data and artificial intelligence”, Biotechnology Journal, Vol. 14 No. 8, p. 1800613.
Ooi, K.B., Lee, V.H., Tan, G.W.H., Hew, T.S. and Hew, J.J. (2018), “Cloud computing in manufacturing: the next industrial revolution in Malaysia?”, Expert Systems with Applications, Vol. 93, pp. 376-394.
Popovič, A., Hackney, R., Tassabehji, R. and Castelli, M. (2018), “The impact of big data analytics on firms' high value business performance”, Information Systems Frontiers, Vol. 20 No. 2, pp. 209-222.
Powell, T.C. (1995), “Total quality management as competitive advantage: a review and empirical study”, Strategic Management Journal, Vol. 16 No. 1, pp. 15-37.
Putnik, G., Sluga, A., ElMaraghy, H., Teti, R., Koren, Y., Tolio, T. and Hon, B. (2013), “Scalability in manufacturing systems design and operation: state-of-the-art and future developments roadmap”, CIRP Annals, Vol. 62 No. 2, pp. 751-774.
Qi, Q. and Tao, F. (2018), “Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison”, IEEE Access, Vol. 6, pp. 3585-3593.
Ramayah, T., Samat, N. and Lo, M.C. (2011), “Market orientation, service quality and organizational performance in service organizations in Malaysia”, Asia-Pacific Journal of Business Administration, Vol. 3, pp. 8-27.
Ratten, V. (2016), “Continuance use intention of cloud computing: innovativeness and creativity perspectives”, Journal of Business Research, Vol. 69 No. 5, pp. 1737-1740.
Rogers, D.L., Pendleton, B.F., Goudy, W.J. and Richards, R.O. (1978), “Industrialization, income benefits, and the rural community”, Rural Sociology, Vol. 43 No. 2, p. 250.
Safar, L., Sopko, J., Dancakova, D. and Woschank, M. (2020), “Industry 4.0—awareness in South India”, Sustainability, Vol. 12 No. 8, p. 3207.
Saini, H., Upadhyaya, A. and Khandelwal, M.K. (2019), “Benefits of cloud computing for business enterprises: a review”, Proceedings of International Conference on Advancements in Computing and Management.
Schmidt, R., Möhring, M., Härting, R.C., Reichstein, C., Neumaier, P. and Jozinović, P. (2015), “Industry 4.0-potentials for creating smart products: empirical research results”, International Conference on Business Information Systems, Springer, Cham, pp. 16-27.
Schniederjans, D.G. (2017), “Adoption of 3D-printing technologies in manufacturing: a survey analysis”, International Journal of Production Economics, Vol. 183, pp. 287-298.
Schniederjans, D.G. and Hales, D.N. (2016), “Cloud computing and its impact on economic and environmental performance: a transaction cost economics perspective”, Decision Support Systems, Vol. 86, pp. 73-82.
Sether, A. (2016), “Cloud computing benefits”, available at: SSRN 2781593.
Shamim, S., Cang, S., Yu, H. and Li, Y. (2016), “Management approaches for Industry 4.0: a human resource management perspective”, IEEE Congress on Evolutionary Computation, IEEE, pp. 5309-5316.
Singhal, N. (2020), “An empirical investigation of industry 4.0 preparedness in India”, Vision, Vol. 25, pp. 300-311, 0972262920950066.
Son, I., Lee, D., Lee, J.N. and Chang, Y.B. (2011), Understanding the Impact of IT Service Innovation on Firm Performance: the Case of Cloud Computing, PACIS, Association for Information Systems, p. 180.
Sun, L. and Zhao, L. (2017), “Envisioning the era of 3D printing: a conceptual model for the fashion industry”, Fashion and Textiles, Vol. 4 No. 1, pp. 1-16.
Tang, C.P., Huang, T.C.K. and Wang, S.T. (2018), “The impact of Internet of things implementation on firm performance”, Telematics and Informatics, Vol. 35 No. 7, pp. 2038-2053.
Tjahjono, B., Esplugues, C., Ares, E. and Pelaez, G. (2017), “What does industry 4.0 mean to supply chain?”, Procedia Manufacturing, Vol. 13, pp. 1175-1182.
Uncles, M.D. (2011), “Researching market orientation and business performance”, Australasian Marketing Journal, Vol. 19 No. 3, pp. 161-164.
Vaidya, S., Ambad, P. and Bhosle, S. (2018), “Industry 4.0–a glimpse”, Procedia Manufacturing, Vol. 20, pp. 233-238.
Vanderploeg, A., Lee, S.E. and Mamp, M. (2017), “The application of 3D printing technology in the fashion industry”, International Journal of Fashion Design, Technology and Education, Vol. 10 No. 2, pp. 170-179.
Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., Jubert, I.S., Mazura, M., Harrison, M., Eisenhauer, M. and Doody, P. (2011), “Internet of things strategic research roadmap”, Internet of Things-Global Technological and Societal Trends, pp. 9-52.
Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R. and Childe, S.J. (2017), “Big data analytics and firm performance: effects of dynamic capabilities”, Journal of Business Research, Vol. 70, pp. 356-365.
Wang, S., Wan, J., Zhang, D., Li, D. and Zhang, C. (2016), “Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination”, Computer Networks, Vol. 101, pp. 158-168.
Weber, L. (2009), How Digital Customer Communities Build Your Business, John Wiley & Sons.
Witkowski, K. (2017), “Internet of things, big data, industry 4.0–innovative solutions in logistics and supply chains management”, Procedia Engineering, Vol. 182, pp. 763-769.
Wu, D., Thames, J.L., Rosen, D.W. and Schaefer, D. (2012), “Towards a cloud-based design and manufacturing paradigm: looking backward, looking forward”, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers (ASME), pp. 315-328.
Wu, J., Guo, S., Li, J. and Zeng, D. (2016), “Big data meet green challenges: big data toward green applications”, IEEE Systems Journal, Vol. 10 No. 3, pp. 888-900.
Xu, L.D., Xu, E.L. and Li, L. (2018), “Industry 4.0: state of the art and future trends”, International Journal of Production Research, Vol. 56 No. 8, pp. 2941-2962.
Xue, C.T.S. and Xin, F.T.W. (2016), “Benefits and challenges of the adoption of cloud computing in business”, International Journal on Cloud Computing: Services and Architecture, Vol. 6 No. 6, pp. 1-15.
Yan, J., Meng, Y., Lu, L. and Li, L. (2017), “Industrial big data in an industry 4.0 environment: challenges, schemes, and applications for predictive maintenance”, IEEE Access, Vol. 5, pp. 23484-23491.
Yin, Y., Stecke, K.E. and Li, D. (2018), “The evolution of production systems from Industry 2.0 through Industry 4.0”, International Journal of Production Research, Vol. 56 Nos 1-2, pp. 848-861.
Zhang, L., Luo, Y., Tao, F., Li, B.H., Ren, L., Zhang, X., Guo, H., Cheng, Y., Hu, A. and Liu, Y. (2014), “Cloud manufacturing: a new manufacturing paradigm”, Enterprise Information Systems, Vol. 8 No. 2, pp. 167-187.
Zhang, H., Song, M. and He, H. (2020), “Achieving the success of sustainability development projects through big data analytics and artificial intelligence capability”, Sustainability, Vol. 12 No. 3, p. 949.
Zhao, S.M. and Xu, X.H. (2017), “The meaning pattern and development path of ‘new retail’”, China Business and Market, Vol. 31 No. 5, pp. 12-20.
Zhong, R.Y., Xu, X., Klotz, E. and Newman, S.T. (2017), “Intelligent manufacturing in the context of industry 4.0: a review”, Engineering, Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company, Vol. 3 No. 5, pp. 616-630.
Zhou, K., Liu, T. and Zhou, L. (2015), “Industry 4.0: towards future industrial opportunities and challenges”, 12th International Conference on Fuzzy Systems and Knowledge Discovery, IEEE, pp. 2147-2152.
Further reading
Abubakar, A.M., Elrehail, H., Alatailat, M.A. and Elçi, A. (2019), “Knowledge management, decision-making style and organizational performance”, Journal of Innovation and Knowledge, Vol. 4 No. 2, pp. 104-114.
Ahmed, N. (2013), “Wholesale and retail trade sector in Pakistan”, FBR Quarterly Review, Vol. 12, pp. 1-21.
Ahuett-Garza, H. and Kurfess, T. (2018), “A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing”, Manufacturing Letters, Vol. 15, pp. 60-63.
Batista, N.C., Melício, R. and Mendes, V.M.F. (2017), “Services enabler architecture for smart grid and smart living services providers under industry 4.0”, Energy and Buildings, Vol. 141, pp. 16-27.
Davenport, T.H., Barth, P. and Bean, R. (2012), “How ‘big data’ is different”, MIT Sloan Management Review, Vol. 54, pp. 21-25.
Dilberoglu, U.M., Gharehpapagh, B., Yaman, U. and Dolen, M. (2017), “The role of additive manufacturing in the era of industry 4.0”, Procedia Manufacturing, Vol. 11, pp. 545-554.
Fatorachian, H. and Kazemi, H. (2018), “A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework”, Production Planning and Control, Vol. 29 No. 8, pp. 633-644.
Lee, J., Ardakani, H.D., Yang, S. and Bagheri, B. (2015a), “Industrial big data analytics and cyber-physical systems for future maintenance and service innovation”, Procedia Cirp, Vol. 38, pp. 3-7.
Lee, J., Bagheri, B. and Kao, H.A. (2015b), “A cyber-physical systems architecture for industry 4.0-based manufacturing systems”, Manufacturing Letters, Vol. 3, pp. 18-23.
Okano, M.T. (2017), “IOT and industry 4.0: the industrial new revolution”, International Conference on Management and Information Systems, p. 26.
Perera, C., Zaslavsky, A., Christen, P. and Georgakopoulos, D. (2013), “Context aware computing for the internet of things: a survey”, IEEE Communications Surveys and Tutorials, Vol. 16 No. 1, pp. 414-454.
Ur Rehman, H.I. and Ishaq, Z. (2017), “The impact of brand image on purchase intention: Moderating role of store image in Pakistan's retail sector”, IUP Journal of Brand Management, Vol. 14 No. 3, pp. 54-66.
Acknowledgements
Funding: This research received funding from National Natural Science Foundation: Research on Core Enterprise Leadership and Mechanism of Its Role in Strategic Alliance of Technological Innovation (71472144), Research on the Influence Mechanism of Core Enterprise Leadership on the Performance of Technology Innovation Strategy Alliance (14XJA630004) of the Western and Frontier Regions Project of the Humanities Society of the Ministry of Education, Research on Xi’an Civil-Military Integration Industry Cluster Development Based on the Core Enterprise Leading the Xi’an Comprehensive Innovation and Reform Experiment of Xi’an Soft Science Project of Xi’an Science and Technology Plan [2017110SF/RK004-(6)]. The project Research on Development Path of Shaanxi Civil-Military Integration Industrial Cluster (2019KRM006) is co-funded by the Soft Science Research Program of Shaanxi Province.