An Internet of Things (IoT) Acceptance Model. Assessing Consumer’s Behavior toward IoT Products and Applications

Abstract

A common managerial and theoretical concern is to know how individuals perceive Internet of Things (IoT) products and applications and how to accelerate adoption of them. The purpose of the current study is to answer, “What are the factors that define behavioral intention to adopt IoT products and applications among individuals?” An IoT adoption model was developed and tested, incorporating pull factors from two different information impact sources: technical and psychological. This study employs statistical structural equation modeling (SEM) in order to examine the conceptual IoT acceptance model. It is demonstrated that facilitated appropriation, perceived usefulness and perceived ease of use, as mediators, significantly influence consumers’ attitude and behavioral intention towards IoT products and applications. User character, cyber resilience, cognitive instrumentals, social influence and trust, all with different significance rates, exhibited an indirect effect, through the three mediators. The IoT acceptance model (IoTAM) upgrades current knowledge on consumers’ behavioral intention and equips practitioners with the knowledge needed to create successful integrated marketing tactics and communication strategies. It provides a solid base for examining multirooted models for the acceptance of newly formed technologies, as it bridges the discontinuity in migrating from information and communication technologies (ICTs) to IoT adoption studies, causing distortions to societies’ abilities to make informed decisions about IoT adoption and use.

Assessing Antecedents of Behavioral Intention to Use Mobile Technologies in E-Commerce

“…In empirical research, most TAMbased studies use the following antecedent variables for the two variables (PEU and PU): confidence, innovation, mobility, enjoyment, and social influence [30][31][32][66][67][68][69][70][71]. Some studies used customer satisfaction as a variable in TAM to determine the influence on consumer adoption of m-commerce [29][30][31][35][36][37][38][39][40][41][42][43][44][45][72][73][74][75][76][77][78]; exploring consumer behavioral intent and customer satisfaction. Our study used a simplified TAM model, in which consumer satisfaction influences behavioral intention.…”

“…Depending on the domain in which the TAM model is applied, these two main variables are influenced by domain-specific external variables. Our research used the external variables (antecedents) of trust, convenience, customization, innovativeness, rapidity, accessibility, mobility, and enjoyment; variables identified and used in multiple other studies [10,11,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][66][67][68][69][70][71][72][73][74][75][76][77][78]. Our theoretical model defines PEU and PU as latent variables influenced by external variables.…”

“…Based on a series of studies that used customer satisfaction as a variable in the TAM [29][30][31][35][36][37][38][39][40][41][42][43][44][45][71][72][73][74][75][76][77], we included in our research an exogenous variable, consumer satisfaction, measured in parallel with the extent of use of m-commerce. Stated satisfaction and extent of use are exogenous observable variables that characterize endogenous latent variables: consumer satisfaction and actual usage.…”

“…Conceptual structural model. Source: own construction based on[10,11,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][66][67][68][69][70][71][72][73][74][75][76][77][78].…”

Limiting Factors in Widespread Adoption of Active Queue Management in the Philippines’ Consumer Electronics Space

“…On the other, factors such as social influence do play a part though to a lesser extent against the factors (Tsourela & Nerantzaki, 2020). [15]…”