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The introduction of quality management Internet of things (QM IoT) can help food supply chain members to enhance real-time visibility, quality, safety and efficiency of products and processes. Current literature indicates three main research gaps, including a lack of studies in QM IoT in the food supply chain, the vagueness of integrative adoption of new technology framework and deficient research covering both adoption attitude and intention in the same model. This study aims to propose an analysis model based on the technological–organizational–environmental (TOE) framework and reinforced by the collaborative structure to capture the importance of the supply chain network.

The partial least square-structural equation modeling (PLS-SEM) was applied to test the impacts of the adoption factors on QM IoT adoption attitude and intention among 197 respondents in food manufacturing in Thailand.

The results indicated that compatibility, trialability, adaptive capacity, innovative capability, executive support, value chain partner pressure, presence of service provider and information sharing significantly impacted the attitude toward QM IoT adoption, while adaptive capability, innovative capability and information sharing directly influenced the QM IoT adoption intention. Furthermore, the attitude toward QM IoT adoption positively impacted the QM IoT adoption intention.

This study contributed to academicians by proposing a more solid adoption framework for QM IoT area. In addition, the business practitioners could actively prepare themselves for the QM IoT adoption, whereas the service providers could provide better and suitable service.

This research contributes to the building of a more solid framework and indicates significant factors that impact the attitude toward QM IoT adoption and adoption intention.

The e-freight program launched by the International Air Transport Association (IATA) has gradually become a standard specification for international air freight operations. This study examined critical factors affecting air freight forwarders’ decision to adopt the IATA e-freight using a technology-organization-environment model with air freight forwarders in Taiwan as the base. Our findings show that ‘information technology (IT) competence’, ‘trading partner pressure’, ‘government policy’ and ‘competitive pressure’ all have significant positive effects on air freight forwarders’ decision to adopt the e-freight and the top three factors among these are ‘government funding’, ‘government’s active promotion’ and ‘government’s requirement of electronic air waybill (e-AWB)’. Finally, this study proposes strategies that can encourage air freight forwarders to decide on e-freight adoption for the information of relevant oK regyawniozradtison International Air Transport Association (IATA); IATA e-freight; Technology organization environment model; Air freight forwarder

Mitigating the energy trilemma (ET) is of great importance for dealing with climate change and realizing carbon neutrality. To this end, effectively assessing the level of the ET is essential. The purpose of this study is to evaluate the current situation and the spatio-temporal changes of the ET in the whole of China.

Moreover, based on provincial-level data in China for the period 2002–2017, and by using the dynamic estimation model, we aim to determine the specific marginal impacts of smart transportation (ST) on the ET, and the possible channels through which ST works on the ET.

We thus present the following findings: (1) The performance of both ET and its three pillars is gradually improving in China. Moreover, the situation tends to vary dramatically among various regions and provinces, and the gap between the best performers and the worst is large. (2) ST plays a significant role in inhibiting the ET, a finding that remains robust after a series of tests. And (3) the ET eradication effect of ST is caused mainly by improved innovation, advanced technical efficiency, and the increasing energy scale.

Accordingly, we put forward some policy recommendations to help tackle ET and accelerate ST in China.

The purpose of this paper is to investigate the relationships between environmental consciousness (ECO), green purchase attitude (GPA), green purchase intention (GPI), perceived customer effectiveness (PCE), green behaviour (GRB) and green purchase behaviour (GPB). Based on the statistical analyses, this paper offers some further research directions to advance the extant literature.

The theoretical model is firmly grounded in extant literature. To test the study hypotheses, the authors have developed a survey instrument following a two-stage process. The constructs were first operationalized by the authors and then pre-tested by experts. Dillman’s (2007) guidelines were then followed to gather data. Finally, the theoretical model was tested using multivariate statistical tools.

Results indicate that ECO has an influence on GPA and PCE; GPA has an influence on PCE and GRB; GPI has an influence on PCE; and GRB has an influence on GPB. Environmental benefit still ranks at the sixth position among eight product-selection criteria, as is evident from qualitative in-depth interviews indicating a primarily rationalistic and not an altruistic purchase approach. The gap in translation of ECO into GB and GPB can be attributed to costliness, non-availability with less variety, lack of brand reputation of green products and budget constraints for customers.

The study faces the limitation of generalizability of the results because it was carried out in a particular state in India; it may not be the perception of the country as a whole. The bias owing to social desirability, selective memory and telescoping with the use of self-reported data could also be a limitation for the current empirical study.

This study aimed to extend pro-environmental behaviour studies beyond developed countries and to empirically validate the models built on the theory of ECO leading to GPB, especially for India, a rising market. A novel approach to empirically discuss the situational and market factors will provide a much-needed thrust for research on these lines.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.