Understanding the factors affecting the adoption of the Internet of Things

Achieving organizational performance by integrating industrial Internet of things in the SMEs: a developing country perspective

Purpose

This research investigates the adoption of the industrial Internet of things (IIoT) in SMEs to achieve and increase organizational performance. With the latest technology, small and medium-sized enterprises (SMEs) can create a competitive edge in the market and better serve customers.

Design/methodology/approach

Twelve hypotheses are proposed for this study. This study constructed a questionnaire based on technological, organizational, environmental and human perspectives. A survey is conducted on the SMEs of India using the questionnaire.

Findings

Eight hypotheses were accepted, and four hypotheses were not supported. The hypotheses rejected are infrastructure, organizational readiness, internal excellence and prior experience. The findings suggested that adopting IIoT in SMEs will increase organizational performance.

Research limitations/implications

This study will be helpful for the manager, top management and policymakers. This study identified the areas SMEs need to work on to adopt the technologies.

Originality/value

In the literature, no article considered IIoT adoption in SME firms as a human factor. Therefore, this study is unique, including human, technological, organizational and environmental factors.

Robotic process automation adoption: contextual factors from service sectors in an emerging economy

Purpose

The study aimed to examine the robotic process automation (RPA) contextual (center of excellence and scalability) and the multidisciplinary (TOE) determinants of RPA adoption in service industries in the emerging economy.

Design/methodology/approach

Ten factors were identified through literature surveys and popular studies grounded in technology, organization and environment. SPSS AMOS SEM is used for scale measurement and hypotheses testing. A sample of 313 respondents was collected from middle to above middle management executives of service industries from India. The authors tested the hidden layers and non-linear relationships using artificial neural network (ANN) analysis.

Findings

The low complexity, center of excellence (CoE), and industry/business partner pressure were significant to the RPA adoption in service industries in emerging economies. Counterintuitively, the scalability showed a negative influence on the RPA adoption, and the process capability did not show influence. The results of SEM and ANN were consistent.

Research limitations/implications

This research can unfold the RPA adoption scholarly debate to multiple services industries beyond the telecom sector in emerging economies.

Practical implications

RPA is a disruptive technology on the artificial intelligence (AI) continuum. It has the potential to change the ways of working and enable technology-driven transformation. However, despite having thriving service industries that can benefit from RPA, emerging economies lag in adoption compared to the developed nations.

Social implications

The RPA and automation can bring transformation to human society. Large economies such as India and China have large-scale demand for services, and the waiting lines are a common issue struggled by society. RPA can address the scalability issues of several services.

Originality/value

This study is among the first to examine technology-organization-environment (TOE) with RPA, including RPA contextual variables such as the CoE and scalability. Literature reports TOE applications on several emerging technologies of Industry 4.0 such as cloud, blockchain, big data and 3 Dimensional Printing (3DP), but no or little reported studies around RPA in services industries in emerging markets.

Adoption of Green Industrial Internet of Things to Improve Organizational Performance: The Role of Institutional Isomorphism and Green Innovation Practices

Emerging technology developments and functions of the Internet of Things (IoT) in industrial systems are leading the development of the Industrial IoT (IIoT). Greener, i.e., cleaner environmental goals can be achieved by putting green IIoT (GIIoT) into practice. This research aims to explore the reasons for the adoption of GIIoT in organizational decision-making and to explore its impact on organizational performance. The proposed research model was tested by collecting data through a structured questionnaire. The findings suggest that institutional isomorphism has a positive impact on the adoption of GIIoT. Moreover, GIIoT is positively associated with green innovation (GI) practices (e.g., product, process, and management) that lead to organizational performance. The potential impact of various types of institutional isomorphism described in this study can help organizations better comprehend the institutional pressures they enforce and/or appease their stakeholders, especially as they adopt GIIoT, to manage production issues and potential compliance pressures in the process.

A Systematic Review of Internet of Things Adoption in Organizations: Taxonomy, Benefits, Challenges and Critical Factors

Despite the evident growth of the Internet of Things (IoT) applications, IoT deployments in organizations remain in their early stages. This paper aims to systematically review and analyze the existing literature on IoT adoption in organizations. The extant literature was identified using five electronic databases from 2015 to July 2021. Seventy-seven articles have met the eligibility criteria and were analyzed to answer the research questions. This study produced a coherent taxonomy that can serve as a framework for future research on IoT adoption in organizations. This paper presents an overview of the essential features of this emerging technology in terms of IoT adoption benefits and challenges in organizations. Existing theoretical models have been analyzed to identify the factors that influence IoT adoption and to understand the future requirements for widespread IoT adoption in organizations. Six critical factors affecting and playing a key role in IoT adoption in organizations were identified based on the critical review findings: technological, organizational, environmental, human, benefit, and value. Decision-makers and developers can prioritize these critical factors and progressively improve their development to enhance IoT adoption efficiency. This review also includes an in-depth analysis to bridge gaps and provide a comprehensive overview to further understand this research field.

Internet of Things as a Sustainable Energy Management Solution at Tourism Destinations in India

The development of tourist destinations has been determined by sustainable energy management and the advancement of traditional methods. The rate of development must adapt to technological innovations while also considering future generations. The present study aims to determine to what extent tourism personnel are aware of the skills, knowledge, and methods for sustainable energy management, and also to analyze the Internet of Things as a technological solution for sustainable energy management at tourism destinations in India with the help of the Servqual method. It is important to implement modern technologies such as internal things and develop a sustainable attitude toward tourism. Findings suggested that over each attribute of Servqual model, reliability, assurance, tangibles, empathy and responsiveness of IoT as sustainable energy management solutions at tourism destinations in India, tourism stakeholders have higher level of expectations (23.41, 19.86, 18.45, 23.60 and 24.73) and perceptions (18.34, 16.50, 14.97, 18.17 and 19.20) followed by tourists expectations (22.10, 17.36, 16.01, 22.62 and 21.87) and perceptions (19.32, 11.75, 09.46, 15.06 and 17.43) and local residents expectations (20.17, 14.61, 14.87, 19.46 and 18.81) and perceptions (13.48, 08.85, 07.73, 13.54 and 12.94), respectively. Results also showed that older generations and traditional tourism destinations are unable to cope with the modern advanced terminology, tools, and management strategies, which makes the present study the most significant about changing the traditional way of energy management and developing tourism destinations as sustainable and responsible.

Internet of things challenges of sustainable supply chain management in the manufacturing sector using an integrated q-Rung Orthopair Fuzzy-CRITIC-VIKOR method

Purpose

The “Internet of Things (IoT)” is a platform for involving smart devices via the Internet at a worldwide scale. It supports the “supply chain (SC)” and “information and communication technology (ICT)” infrastructure to be well integrated into an organization and externally with customers and suppliers. The “sustainable supply chain (SSC)” is currently unavoidable if a company seeks to satisfy the aggressive change in its customers' requirements. Numerous studies have confirmed that manufacturing firms have to accelerate the shift of their focus toward sustainability and the implementation of novel technologies, such as IoT, to accomplish their organizational goals most effectively. Although the literature consists of many theoretical approaches to IoT and numerous studies that have extremely concentrated upon the IoT technology and its potential applications, it lacks research with a focus on the challenges that arise when applying IoT to the “sustainable supply chain management (SSCM)”.

Design/methodology/approach

The present study proposes an integrated framework using the “Criteria Importance Through Intercriteria Correlation (CRITIC)” and “VlseKriterijumska optimizcija I kaompromisno resenje in Serbian (VIKOR)” models and employs to evaluate the IoT challenges to implement the SSCM. For estimating the criteria weights, the CRITIC tool is utilized. The organization's prioritization is obtained by the VIKOR procedure, which delivers simple mathematical procedures with precise and consistent outcomes.

Findings

To exhibit the practicality of the introduced model, a case study is taken to evaluate the IoT challenges to implement the SSCM within the “q-Rung Orthopair Fuzzy Sets (q-ROFSs)” environment. Moreover, the authors exhibit a sensitivity investigation over given parameter values, examining the stability of developed approach. Finally, the authors draw attention to a comparison between developed q-ROF-CRITIC-VIKOR decision-making approach with an existing q-ROF-TOPSIS method to show its superiority and potency.

Originality/value

The outcome of the study lies in observing the top benefits of individual businesses, and their entire SSCs can be found by implementing IoT. This paper investigates the most important challenges that individual firms and entire SSCs might while applying IoT. It provides a deep insight regarding the effects of IoT upon SSCM and the issues every firm need to contemplate when it is to apply IoT solutions.

Key Factors in the Implementation of the Internet of Things in the Hotel Sector

Many factors can influence decision-making, and if you wish to know which are the most influential factors in a decision, they must be classified by their degrees of influence. This study seeks to determine the most influential factors in the decision of hotels to accept and implement the Internet of Things in their services through a literary review and a causal study carried out on experts in technology and hotels. The methodology involves the use of fuzzy cognitive maps and the FCMappers tool. The results obtained show that the following factors are among the most influential (in order of relevance): the perceived reliability of the technology, the relative advantage it gives, the level of top management support, compatibility, customer pressure, information systems provider support, security, business partner pressure, characteristics of the leader or manager, government pressure or incentives, pressure from competitors, technological organizational readiness, complexity, size of the company, and the perceived cost.

The internet of things in the food supply chain: adoption challenges

Purpose

The food supply chain (FSC) challenges coupled with global disruptions, such as the recent coronavirus disease 2019 (COVID-19) pandemic outbreak, exacerbate its vulnerability. The Internet of things (IoT) is one of the disruptive technologies being adopted in food supply chain management (FSCM). This study aims to address the challenges of IoT adoption in the FSC by systematically analyzing the prior pertinent literature.

Design/methodology/approach

A structured literature review was used to collate a list of peer-reviewed and relevant publications. A total of 72 out of 210 articles were selected for the final evaluation.

Findings

The literature review findings suggest five themes: technical, financial, social, operational, educational and governmental related challenges. A total of 15 challenges were devised from the review related literature of IoT adoption. The study concludes with future research recommendations for scholars and practical implications for practitioners.

Research limitations/implications

While this study focuses on the overall FSC, further research should address other domains in the FSC such as cold supply chain, agriculture and perishable food to gain a better contextual understanding of the specific case.

Originality/value

The topic of IoT adoption in the FSCM is still considered emerging. Therefore, the present work contributes to the limited studies and documentation on the level of IoT implementation in the FSCM. This study should help organizations to assimilate how to adopt and manage the IoT application by addressing the factors and challenges presented in this research.

Applying the Multi-Criteria Decision-Making Approach for Lawyer Selection from Law Firms’ Perspective in China

This study proposes a novel evaluation model for lawyer selection incorporating the lawyer’s backbone leadership attitude employing the hybrid multi-criteria decision-making (MCDM) approach. In the proposed approach, the lawyer’s backbone leadership attitude is employed as an evaluation factor in the evaluation model for lawyer selection from law firms’ perspective. In this paper, a hybrid approach based on the Delphi technique and analytic hierarchy process (AHP) is proposed to manage qualitative and quantitative criteria for selecting the best alternative lawyer for law firms in China. Finally, a law firm in China is carried out to verify the feasibility of the proposed approach. Based on the result, the backbone leadership does provide valuable information in the evaluation model for lawyer selection. The results also revealed that the proposed approach would help law firms and human resource managers to understand and develop strategies to hire a lawyer.

Trust in the smart home: Findings from a nationally representative survey in the UK

Businesses in the smart home sector are actively promoting the benefits of smart home technologies for consumers, such as convenience, economy and home security. To better understand meanings of and trust in the smart home, we carried out a nationally representative survey of UK consumers designed to measure adoption and acceptability, focusing on awareness, ownership, experience, trust, satisfaction and intention to use. We analysed the results using theories of meanings and acceptability of technologies including semiotics, social construction of technology (SCOT) and sociotechnical affordance. Our findings suggest that the meaning and value proposition of the smart home have not yet achieved closure for consumers, but is already foregrounding risks to privacy and security amongst the other meaning-making possibilities it could afford. Anxiety about the likelihood of a security incident emerges as a prominent factor influencing adoption of smart home technology. This factor negatively impacts adoption. These findings underline how businesses and policymakers will need to work together to act on the sociotechnical affordances of smart home technology in order to increase consumers’ trust. This intervention is necessary if barriers to adoption and acceptability of the smart home are to be addressed now and in the future.

Adoption of internet of things (IoT) in the agriculture industry deploying the BRT framework

Purpose

The purpose of this paper is to investigate the adoption of Internet of Things (IoT) in the agriculture industry by the farmers' in India using the theoretical lens of the behavioral reasoning theory (BRT).

Design/methodology/approach

A survey on farmers was conducted to examine the adoption of IoT in agriculture industry (IoT-A) using BRT. The data analysis of the primary survey was done by applying the structural equation modelling (SEM) technique.

Findings

The ‘reasons for’ adoption of IoT-A were as follows: Relative advantage, social influence, perceived convenience, and perceived usefulness. The ‘reasons against’ adoption were as follows: Image barrier, technological anxiety, perceived price and perceived risk. The BRT theory provides the platform to discuss the psychological processing of acceptance of IoT in agriculture industry by the farmers.

Practical implications

This research has unique implications as it studies the rural consumers’ behavior of innovation adoption namely IoT in agriculture. It provides the specific reasons ‘for’ and ‘against’ IoT adoption in agriculture, which will give directions to the marketers of IoT technology to develop suitable marketing strategies to improve the adoption in rural areas.

Originality/value

This research takes the first step in the direction toward deliberation of the adoption of IoT-A by farmers in an emerging Indian economy using the BRT theory, which discusses the ‘reasons for’ and ‘reasons against’ adoption in a proposed model.

Understanding the Determinants of Big Data Analytics Adoption

This article aims to empirically investigate the factors that affects the adoption of big data analytics by firms (adopters and non-adopters). The current study is based on three feature that influence BDA adoption: technological context (relative advantage, complexity, compatibility), organizational context (top management support, technology readiness, organizational data environment), and environmental context (competitive pressure, and trading partner pressure). A structured questionnaire-based survey method was used to collect data from 231 firm managers. Relevant hypotheses were derived and tested by partial least squares. The results indicated that technology, organization and environment contexts impact firms' adoption of big data analytics. The findings also revealed that relative advantage, complexity, compatibility, top management support, technology readiness, organizational data environment and competitive pressure have a significant influence on the adopters of big data analytics, whereas relative advantage, complexity and competitive pressure have a significant influence on the non-adopters of big data analytics.

Adoption of Industrial IoT (IIoT) in Auto-Component Manufacturing SMEs in India

This article investigates the adoption of Industrial Internet-of-Things (IIoT) in Auto-Component Manufacturing SMEs (ACM SMEs) in the context of a developing country like India by using the Technology-Organization-Environment (TOE) framework. This research surveyed Information & Communication Technology (ICT) officers, managers and owners of 320 ACM SMEs in India using a structured questionnaire to understand the adoption of IIoT. The primary data was analyzed using the PLS-SEM technique. It was found that IIoT expertise, IIoT infrastructure, relative advantage, compatibility, cost, security, organizational readiness, top management support, competitive pressure, and support from technology vendors are factors that affect the adoption of IIoT. This article considered organization size as the control variable. The results show that it does not have a significant effect on the adoption of IIoT. ACM SMEs are one of the important sectors adopting IIoT. This article provides valuable insights to their managers and IIoT vendors. It also suggests key inputs to the government officials involved in the ‘Make in India' initiative of Government of India (GoI) and Ministry of Micro, Small and Medium Enterprises (MSME).

A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM)

In this paper, a new multi-criteria problem solving method—the Full Consistency Method (FUCOM)—is proposed. The model implies the definition of two groups of constraints that need to satisfy the optimal values of weight coefficients. The first group of constraints is the condition that the relations of the weight coefficients of criteria should be equal to the comparative priorities of the criteria. The second group of constraints is defined on the basis of the conditions of mathematical transitivity. After defining the constraints and solving the model, in addition to optimal weight values, a deviation from full consistency (DFC) is obtained. The degree of DFC is the deviation value of the obtained weight coefficients from the estimated comparative priorities of the criteria. In addition, DFC is also the reliability confirmation of the obtained weights of criteria. In order to illustrate the proposed model and evaluate its performance, FUCOM was tested on several numerical examples from the literature. The model validation was performed by comparing it with the other subjective models (the Best Worst Method (BWM) and Analytic Hierarchy Process (AHP)), based on the pairwise comparisons of the criteria and the validation of the results by using DFC. The results show that FUCOM provides better results than the BWM and AHP methods, when the relation between consistency and the required number of the comparisons of the criteria are taken into consideration. The main advantages of FUCOM in relation to the existing multi-criteria decision-making (MCDM) methods are as follows: (1) a significantly smaller number of pairwise comparisons (only n − 1), (2) a consistent pairwise comparison of criteria, and (3) the calculation of the reliable values of criteria weight coefficients, which contribute to rational judgment.