A Consumer-Centric Paradigm Shift in Business Environment with the Evolution of the Internet of Things: A Literature Review

Introduction

Internet of Things (IoT) is the technology that integrates the physical devices, such as sensors and actuators, gives them digital identities and uses an integrated communication network through the internet, so that information can be exchanged across the devices in real-time to make the system perform in an intelligent way without human interference by incorporating the digital intelligence to the system. Kevin Ashton (2009) fashioned the term, Internet of Things in his presentation at P&G in 1999. Even though there are many contributions to the evolution of IoT, some significant milestones are shown in Figure 1.

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Figure 1.

Source: Created by authors.

The application of IoT is not limited to any particular industry or product, or services. This study reviews the studies on the application of IoT in different industries to capture and analyse consumer behaviour and enhance customer satisfaction. The literature search is done using the keywords, Internet of Things, IoT, adoption, consumer behaviour, customer experience and customer satisfaction with appropriate Boolean operations to get the studies that focus on the application of IoT to product or service or process for capturing consumer behaviour, enhancing customer experience and satisfaction and transforming the business environment. The search is done with Web of Science, Scopus Index and Google Scholar. After removing the duplicates, the documents are selected based on their relevance to the study.

IoT Architecture

The architecture of the IoT varies based on the purpose it serves. Various approaches and systems have been proposed and are being improvized every day. The IoT is constructed based on several technologies and systems. However, radio frequency identification (RFID) is one of the critical technologies that IoT depends on. Tagging the objects with RFID enables automatic wireless identification. Condea et al. (2010) studied the uses of RFID and the information generated through it and proposed a model on economic benefits, especially for time-sensitive products in the reverse logistics process. It was highlighted that maximization of benefits could be drawn by an early product-disposition decision.

The major challenge in implementing the IoT with numerous connected sensors, actuators and devices is the wastage of resources in terms of power consumption for information exchange, computational power and bandwidth. Forsström and Kanter (2014) studied the wastage of resources such as computational power, bandwidth and battery power due to the continuous flow of information across the different components of the IoT devices. They proposed a four-layer approach to run the IoT devices by moderating information-flow, reducing resource wastage. Huang et al. (2014) proposed a framework in three levels to achieve an energy-efficient network model by converting the wireless sensor networked IoT into a green networked IoT. The three levels are the hierarchical framework, the optimized framework and the energy consumption algorithm, which are meant for deployment, projected system-based implementation and flexible and efficient energy consumption.

The critical aspect of IoT is the use of real-time data and a communication grid. Li et al. (2014) proposed the use of IoT in an emergency-based system, where the experts across different domains could interact and use real-time data to analyse and make decisions. It paves the way for companies to have a real-time problem-solving mechanism. Irrespective of their industry, all the companies strive to have a competitive advantage over the others. Gerpott and May (2016) explored the role of different IoT components that a company could use to achieve competitive advantage and objectives and identified the components, novelty, servitization and innovation or advancement in exiting products using IoT. Parry et al. (2016) highlighted the need for accurate and timely data related to the consumption pattern to improve the reverse supply chain. They demonstrated the operationalization of IoT in capturing the data in terms of consumption, experience, visibility, interaction and depletion.

Song et al. (2019) studied the critical challenges in designing the smart e-commerce systems (SESs) and identified the key factors that could be fuelled by IoT. While the key challenges are complexity, data quality, interoperability, robustness, security and privacy, the key factors are scaling, context awareness and reconfigurability. They provided an idea to address these challenges and issues by depicting the SESs qualitatively and quantitatively.

IoT Adoption

The adoption of IoT technologies gives space for new actors and value chains in the newly transformed business ecosystem. Considering the current solutions in application fields and market sectors, the solutions based on IoT can provide a competitive advantage. The role of IoT is not limited to any particular product or services but across all avenues, including healthcare, environment, business management, inventory and product management, smart cities, smart homes and security and surveillance (Miorandi et al., 2012).

Gao and Bai (2014) extended the technology acceptance model (TAM) and developed an integrated model for the behavioural intentions of the customers towards IoT acceptance, with the constructs, perceived usefulness, perceived ease of use and trust, social influence, behavioural enjoyment and perceived behavioural control. All the factors except trust were found to be strongly supporting for acceptance of IoT, and trust was found to be insignificant in predicting the intentions. However, trust and perceived ease of use had an influence over the perceived usefulness of IoT. The integrated model explained the behavioural intention better than TAM.

Wuenderlich et al. (2015) hinted that the future of connected devices would lead to an increased level of smart services, and their adoption would be based on individual decisions, prominence, environment and embeddedness in the objects. Zhou and Piramuthu (2015) suggested that the privacy configuration of IoT devices could be customized for individuals or groups by differentiation based on context, time and space so that there could be a mutual benefit for both customers and business. Businesses are more interested in retaining their status quo, as a result of which the economic and cultural forces at a sector-based level make them resist the acceptance of IoT (Trequattrini et al., 2016). Implementing IoT and leveraging its traits (simple installation, regularity, solidity, design and servicing) would support the business in terms of value creation in the future (Del Giudice, 2016). Nolin and Olson (2016) studied the impact of IoT evolution on notions of convenience, using Constructive Technology Assessment and the seven value drivers proposed by Fleisch (2010) and found that the extreme form of convenience has shifted from conventional human agency to technology, and the convenience of internet any-everything connectivity has become an omnipresent feature of the future society.

In the service sectors, IoT is evolving in term of risk assessment and customer interactions. The three IoT technologies, embedded technologies, machine intelligence and connected ecosystems are expected to have a more profound impact on consumer behaviour than ever before. Shin and Jin Park (2017) carried out a socio-technical analysis of the IoT ecosystem in terms of consumer experience, policy and impact of IoT to understand the prospects of IoT and its sustainability. They found that cognitive motivation and user values are critical influential factors. However, the challenge is to design the different components to make the IoT sustainable.

Yu et al. (2016) suggested that the IoT, as a standalone implementation, could enhance the product innovation but the process innovation. The tangible business innovation and development could be achieved by improving the association of IoT capability with the value-chain alliance. In the new ventures, when the IoT capability is built, the alliances with the value-chain partners could also be developed, which could help in formulating novel offerings.

In the automobile industry, the scope of IoT is enormous in terms of the development of new products, services and business models. Dominici et al. (2016) studied consumers acceptance of IoT-enabled driving, considering their experience in driving, confidence in the IoT and their exposure to the social and cultural influences, and found that the consumers want IoT offerings in terms of alleviating the most tedious tasks such as dealing with traffic jams, finding a parking lot etc. and in terms of privacy and security. However, they wanted to overrule the IoT-aided decisions on the actual driving task.

Valmohammadi (2016) studied the benefits of IoT and the barriers in its realization by literature review and examining the perceptions of directors and specialists in Iran. While the significant benefits could be the enhanced consumer experience and supply chain optimization, the most significant challenges could be the integration and finding the right supplier. To transform the conventional companies into a big data-driven company model, Cheah and Wang (2017) proposed and validated a mechanism using deductive reasoning and case analysis. Inspecting the role of IoT applications from a sustainability point of view could help the manufacturers and service providers to have a deep understanding of their objectives for their new offerings. Sustainability could have a significant contribution towards user acceptance and standardization of IoT framework in designing the products or services. Alptekin (2017) proposed a sustainability-oriented design framework build on quality function deployment (QFD) for IoT by considering energy efficiency, resources usage and customers’ expectations. The deployment of QFD enables systematic traceable analysis of customers’ needs and objectives and transforms them into measurable product attributes.

Verma and Bhattacharyya (2017) studied the hindrances for adopting big data analytics through exploratory research and semi-structured interviews for the key personnel of 22 companies. They found the significant hindrances were inclined to technology, organization and environment, and the organizations did not realize its strategic value. However, Attaran (2017) identified the potential strategic benefits of using IoT could be productivity, quality information and information access, and the limitations of IoT could be the lack of privacy, over-hyped anticipations and technology complexity. The usage of IoT could be the potential differentiation factor among the competitors. Chepurna and Rialp Criado (2018) studied barriers to value co-creation faced by online marketing companies using exploratory qualitative research method and identified nine factors in terms of users perspectives and marketing professionals perspectives.

Warner and Waeger (2019) explored how traditional industries built their capabilities for digital transformation and proposed a model explaining the factors triggering, enabling and hindering the capability building process. Despite the potential of IoT in terms of cost reduction, efficiency, quality and predictive maintenance, most of the factories with limited resources could not revamp their existing production lines. Aheleroff et al. (2020) demonstrated the process of converting the conventional home appliances into IoT supported smart appliances and integrating them into smart home using Industry 4.0 technologies.

The behavioural intentions of consumers towards IoT and the factors influencing these intentions have been studied a lot. Leong et al. (2017) studied the factors of behavioural intention towards IoT adoption in smart cities in Malaysia, using the extension of the UTAUT2 by including additional factors, smart perceived security risk and smart perceived trust from the mobile TAM. They found that performance expectancy, effort expectancy, hedonic motivation, price–value and perceived trust were influencing behavioural intention towards IoT adoption.

Smart homes are becoming the prime offering under IoT. Yang et al. (2017) developed a framework for behavioural intentions and IoT adoption in smart home services and found that mobility, security/privacy risk and trust were influencing the IoT adoption. Shin et al. (2018) found that compatibility, ease of use and usefulness were influencing the decision on IoT adoption in smart homes. The elders were more interested in having smart homes than the younger ones.

The public relation (PR) professionals carry the responsibility of making the reputation of their clients reach their target audience. Amodu et al. (2019) explored the perception of PR professionals on IoT adoption for their functions and found that independent PR firms were more interested towards IoT adoption than in-house departments. For performing the advanced operations, the PR professionals were recommended to explore the benefits of IoT actively.

Mącik (2018) explored the factors influencing the IoT adoption by young consumers in Poland and found that performance expectancy, habit, personal innovativeness, gender (for some devices) had a significant influence on IoT adoption, and income or lack of fund was insignificant. The perceived benefits of IoT usage were more noticeable compared to concerns and fears. Tsourela (2020) studied the factors that influence the behavioural intentions of customers in adopting IoT products and services and proposed IoT adoption model by applying technology acceptance model (TAM) on IoT, including both psychological and technical aspects.

Perceived Risk

When a new technology is introduced, the first thing that comes into the minds of the consumers is the risk with which the technology comes with in terms of privacy and security, besides the perceived benefits. The deployment of IoT in products and services generate big data, which helps the manufacturers and service providers to understand consumer behaviour and to provide a personalized experience. Even in business-to-business transactions, the data collected through IoT can be used for the automation of the process. Thus, the huge data collected also pose a privacy and security concern. The privacy and security concerns are the reluctances that keep the consumers from using the products or availing the services that use IoT.

Maras (2015) studied the risks associated with IoT and revealed that the producers of IoT devices and the developers of IoT applications were not foreseeing the risks, and users were having restricted control over the information collected by IoT devices. It implied the need for urgent action on the legal framework for IoT and the factors related to the risks associated with IoT should be addressed. Despite the privacy issues associated with IoT, a large cross-section of consumers prefers the services that use IoT. Bailey (2015) studied the reason for this adoption and found three behavioural and economic reasons. Consumers may be unaware of the privacy issues associated with IoT. Some consumers perceive the benefits of IoT more than the privacy they sacrifice. The hyperbolic discounting priorities of the consumers change over time. Harwood and Garry (2017) studied the dimensionality of trust within potential IoT applications and suggested that it might fluctuate with the type of service offered through the IoT.

The implementation drive of IoT could be faster and efficient if the price of sensors and increased security are addressed. An efficient data encryption algorithm could be used to address the security. Consumers are inclined towards privacy before making a purchase decision. With the existing approaches, the IoT is considered a single system when assessing the risks associated with it, and this makes the assessment of risks limited. The IoT should be considered as an interdependent system, and the necessary technology should be used to ensure the automatic and continuous risk assessment (Nurse et al., 2017).

AlHogail (2018) studied the factors influencing consumer trust and IoT adoption and proposed a conceptual framework with the crucial factors, categorizing them based on their relatedness to product, social influence and security. The security-related factors were having a strong influence on consumer trust. Among the product-related factors, the perceived usefulness has the most decisive influence. Jayashankar et al. (2018) studied the trust, perceived value and risk with IoT adoption by farmers in the United States and found that the perceived value was positively associated with the trust and positively influencing the IoT adoption, while the perceived risk was negatively associated with the trust and negatively influencing the IoT adoption.

Bhatnagar and Kumar (2020) found the motivating factors encouraging the consumers to share the data generated by IoT devices being personal innovation, joy of helping, rewards, moral obligations and articulating negative states of mind. The manufacturers and service providers could make use of these factors to motivate the consumers to share their IoT data to develop customer-oriented growth strategies.

Consumer Behaviour and Customer Satisfaction

The impact of using IoT would be significantly beneficial in terms of capturing and analysing the consumer behaviour, personalization of the products or services according to the consumers’ needs, real-time analysis of customer-related aspects, predictive analysis using social media, maintaining the customer intimacy and creating a connected marketing environment. The ability of IoT system to track or monitor the goods when they are with the end-users is the significant reason for the increased growth of IoT. The data collected through IoT can be used to develop an enhanced post-sales service mechanism and a predictive maintenance mechanism. It can also be used to understand the shortfalls in the existing product, and the new product could be modified in accordance with the needs of the consumers to enhance their satisfaction. IoT can also be used to detect the problems early, check the availability of parts and estimate the time spent on-site, so that customer satisfaction can also be improved by solving the problems immediately. It would reduce the loss of time and other resources.

Yu et al. (2017) studied the factors influencing customer satisfaction on e-retail websites in terms of site-related variables and consumer-related variables and found that positive mindset, stickiness and propensity were associated with satisfaction, which was the prime factor of e-loyalty. Thomas (2017) explored hybrid services, an intersection of technology innovations and customer service, and suggested a multivariate hybrid approach for streamlining and delivering exceptional customer service, thereby enhancing customer retention and gaining competitive advantage. Dong et al. (2017) studied the factors related to the psychological perception and perceived usefulness of IoT systems and found that the psychological factors were having a significant influence on the usage pattern.

The data collected through IoT can be used in product innovation and in creating differentiation from competitors. The significant features of the IoT, such as interconnectivity and real-time data exchange, make it efficient in the personalization of a product or service in a consumer-dominant market. By identifying and recognising the emotional experience of the consumers throughout their purchase journey, the customer experience programmes can be developed focusing on customer attraction, customer acquisition and customer retention (Batra, 2018). It could also help the manufacturers and service providers to establish an interactive and everlasting customer relationship (Aunkofer, 2018).

Osmonbekov and Johnston (2018) discussed the impact of IoT in buying behaviour, organizational communication, structure of buying centre and processes of buying and privacy and security issues. The nature of organizational communication has started to shift towards machine communication, and buying centres might be smaller with fewer hierarchy, efficient coordination and fewer conflicts. Verhoef et al. (2017) discussed the interconnections of people, objects and the world using IoT and the massive amount of data generated through it and examined the economic value of the ubiquitous, multifaceted and multidimensional connectivity in terms of the conventional perspective of active engagement network of consumers, firms and objects and the emerging perspective of passive engagement network using IoT with sensors to gather information about the consumers and other related environments. This dual perspective gives way for more avenues for research and development.

Even though e-commerce has found a significant transformation with the help of IoT, it is still in the development phase, and it creates a vast opportunity for making the customers get into the last point of transaction. It is believed that in future, the customer service, recommender system and understanding consumer behaviour will become smarter that retailers could tailor the offerings according to their preferences, needs and mind set as a day-to-day operation and make the journey of customers pleasant and tangle-free (Kaczorowska-Spychalska, 2017).

In manufacturing firms, mass customization, delivery optimization, flexibility and quality are the critical factors for sustainable operation. The sustainable operation can be achieved by a smart factory with IoT-enabled production scheduling, as the results of production scheduling impact the quantity, quality and customer satisfaction. To minimize the total tardiness of orders, Shim et al. (2017) proposed a heuristic algorithm with fewer dispatching rules and a lot size model.

Yerpude and Singhal (2018) carried out an extensive literature review to study the impact of IoT on customer relationship management and to assess its benefits. Since the world evolving into a hyper-connected platform, in order to stay in the market, the firms should focus on customers and should be agile to the transformations taking place in the market. With the help of real-time data from IoT, the firms could foster a one-to-one relationship with the customers so that detailed customization and personalization could be possible, which could result in enhanced customer satisfaction and retention.

Kaczorowska-Spychalska (2018) studied the influence of interactive communication, including social media, mobile marketing and IoT solutions and on customer behaviour in terms of the fashion market and found that even though social media is the crucial factor, it does not negate the effects of other forms of information and IoT. The rational application of these communication platforms intensifies the experience and emotions of the customers and influences the brand value and its significance with customers.

Firms consider intelligent agents a tool to maximize their efforts in marketing their products, and this perspective reflects in the vision of IoT-enabled products. The interaction that the consumers have with their smart objects is considered a relationship metaphor. Novak and Hoffman (2019) proposed a consumer–object relationship model based on the circumplex model of interpersonal complementarity, involving assembly theory and object-oriented ontology. Both consumer and the objects play expressive roles, which lead to the explanation of consumer–object relationship in two dimensions, agency behaviour and communion behaviour. They suggested the marketers to consider both the consumers and the objects equally as parts of their assemblages, each with their capabilities.

Lin and Hu (2018) proposed a framework for load scheduling based on constrained particle swarm optimization by accommodating users’ comfort satisfaction and demand response strategy. The demand response actively engages the customers to alter their energy consumption according to the price signals. Additionally, the implementation of edge computing along with IoT and cloud computing would be more efficient with bandwidth-intensive elements and latency-sensitive applications.

Lin et al. (2019) explored improving the energy efficiency of home energy management systems by evaluating customers’ comfort satisfaction quantitatively and formulated an optimal energy-saving model. They classified the utility functions of electronic appliances as time sensitive and temperature sensitive and constructed a general utility function for determining energy-saving costs by incorporating the prospect theory from behavioural economics. The proposed model yielded a total energy saving rate of 65.5%.

Yan et al. (2020) studied the influence of IoT on customer choice behaviours for cyber intelligence and proposed the customer intelligence decision model. The IoT, big data and rational fusion technologies, an effective and customer-centric network of customers and service providers could be established, and the customer experience can be enhanced using data mining. Sima et al. (2020) explored the influence of Industry 4.0 on human capital development and consumer behaviour and identified 12 factors that were found to be influencing consumer behaviour. Taylor et al. (2020) proposed a framework using IoT for marketing activities to enhance customer relationship management, implementing pattern analysis, device monitoring and business intelligence. The usage pattern analysis would also support optimizing the design of new products. Shoukry and Aldeek (2020) studied different classification algorithms using IoT to predict the attributes of hotels based on the customer reviews and found the CNN-DL algorithm to be having better classification accuracy.

Yu et al. (2015) proposed a framework with a network of e-retailers, logistics service providers and customers using IoT to enhance the synergy, thereby customer satisfaction. They found both hard and soft infrastructures of the logistics service providers having a positive influence on flexibility between the e-retailers and logistics service providers. The flexibility would strengthen their relationship, which will earn a competitive advantage and improve customer satisfaction. Further, the flexibility was found to be fully mediating the relationship between the infrastructure and customer satisfaction. The study by Hu et al. (2016) on the influence of customized logistic services on the satisfaction level of online shoppers with the data from tmall.com by applying expectation confirmation theory revealed that the customized logistic service was having a significant influence on the satisfaction level of the online shoppers.

For enhancing the operational effectiveness of cargo handling and storage, Tsang et al. (2017) proposed an IoT-based cargo monitoring system through which customers could monitor their cargo and its storage conditions. Further, any feedback given by the customers on exiting operation would be used by the warehouse to revise their storage operations and guidance for future cargo and to modify the standard operating procedure to a real-life scenario so that customer satisfaction could be enhanced continuously. Tsang et al. (2018) proposed a multi-temperature packing model with a real-time transportation monitoring framework and optimized routing solution using the IoT-based route planning system. This proposed model could be helpful in monitoring the entire process involved in the multi-temperature food distribution process, which could reduce the food spoilage and the time required for route planning and increase customer satisfaction.

Wang et al. (2020) proposed an IoT-based logistics system with a network of customers, order-picking robots and cloud technology to ensure dynamic coordination. It was designed with three layouts which are an IoT-based intelligent dispatching platform, an optimization model for dynamic coordination and a two-level algorithm for intelligent operation. Yusianto et al. (2020) reviewed the smart logistics systems in food horticulture industrial products. In these products, the mishandling would decrease the product quality or damage the product and proposed a new framework for smart logistics systems using RFID and Global Positioning System (GPS) navigation systems to estimate harvest time, decide the suitable warehouse and distribution centre and choose the optimal transportation route so that the post-harvest mishandling could be minimized.

The studies which have been discussed so far gives a glimpse of monitoring and analysing customer behaviour and enhancing customer satisfaction with the use of the IoT. The word-cloud of frequently used terms in the studies on customer satisfaction and consumer behaviour with the IoT is represented in Figure 2.

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Figure 2.

Source: Created by authors using NVivo.

The themes of the selected studies are identified during the review process using NVivo. The studies are clustered based on the similarity of themes. The result of this clustering is represented in the form of a dendrogram in Figure 3.

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Figure 3.

Source: Created by authors using NVivo.

Some Significant Business Transformations with IoT

As mentioned earlier, IoT can be implemented across different sectors. The potential application areas of IoT and some examples of the products or services under each area are represented in Figure 4.

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Figure 4.

Source: Created by authors from literature review.

Conclusion

Customer satisfaction, the prime factor of loyalty, could be enhanced by capturing and analysing consumer behaviour, thereby customising the products or services. With the literature review, this study confirms that the application of IoT into the product, services and end-to-end process from business to consumers could remarkably improve the ways of monitoring consumer behaviour and enhancing customer satisfaction. The IoT enables the real-time capture of the usage pattern and the consumer behaviour, which results in tailoring the offerings according to preferences, needs and mindset of the consumers. Thus, sustainable operation with mass customization, flexibility and delivery optimization is achievable.

Businesses have begun to understand the potential benefits of the IoT and are also aware that the future that it holds in terms of all the levels of communication between the business and customers, differentiation and competitive advantage because of the increasing necessity for customer centricity to have long-term relevance to the market. Businesses that are more interested in retaining their status quo have not realized the strategic value of IoT implementation. As a result, the economic and cultural forces at a sector-based level keep them away from IoT adoption. It is also found that even though most businesses have begun to realize the potential of IoT in terms of cost reduction, efficiency, quality and predictive maintenance, they are not able to revamp their existing production lines. Also, there are other firm-specific hindrances related to technology, organization and environment. An efficient network of the manufacturers, customers and delivery partners is also possible with an IoT-based logistics system. The interactive communication between the business and the consumers, which is considered a relationship metaphor, is also made realistic using IoT. The interactive communication, data mining and identification and recognition of the consumers’ emotional experience help in product development, focusing on customer attraction, acquisition and retention and influencing customers’ decision-making.

This study could be helpful for the researchers in terms of understanding the different frameworks for capturing and analysing consumer behaviour and improving customer satisfaction using IoT. For future research, this study encourages exploration of the applicability of these IoT-based frameworks in different industries and functional areas and the development of a potential new comprehensive framework.