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Understanding how attributes, consequences and values (A-C-V) influence the predisposition to purchase and buying intention of organic food consumers (OFC) is crucial for its stakeholders. This study aims to (1) investigate whether OFC perceptions of the A-C-V impact their predisposition to purchase and buying intention; (2) examine the mediating effect of predisposition to purchase on the relationship between OFC personal values and their buying intentions and (3) verify whether consumers with distinct levels of organic food-buying intention perceive differently of the A-C-V, predisposition to purchase and consumption frequency.

This quantitative study comprised 307 consumers who filled out a form about their perceptions of organic foods’ A-C-V and their consumption frequency, purchasing predisposition and buying intention. Partial least squares strutural equation modelling (PLS-SEM) tested the hypothetical model that resorted to the means-end chain (MEC) theory (Gutman, 1982). Cluster analysis based on OFC’s buying intentions compared their perceptions of the A-C-V, purchasing predisposition and consumption frequency.

The OFC’s perception of the attributes of these foods impacts the consequences of their consumption and values. Such values positively influence their purchase predisposition and buying intention. Predisposition to purchase measured the relationship between OFC values and purchase intention. Three OFC clusters were identified according to their buying intentions. Such groups perceive the A-C-V singularly and have different purchasing predispositions and consumption frequencies.

OFC values directly influence buying intentions. However, the predisposition to purchase strongly mediates the relationship between values and buying intentions, producing an indirect impact more notable than a direct one. It brings academic and managerial contributions to organic food stakeholders.

The study seeks to explore why despite growing concern for the environment, consumers’ intention to purchase organic foods remains relatively low. In addition, the study also seeks to investigate the role of perceived marketplace influence (PMI) and moral norms in organic food consumption.

Data collected from 330 young consumers chosen with non-probability sampling were analysed using structural equation modelling in Amos 22.0.

The results of the parallel mediation analysis confirmed that environmental concern influences purchase intention indirectly through attitude, subjective norms, perceived behavioural control and perceived marketplace influence. In addition, moral norms were found to moderate the effect of perceived behavioural control on purchase intention. Moreover, the results also indicated that the impact of environmental concern on consumers’ attitude toward organic foods was also moderated by moral norms. Further, the results of moderated mediation showed that the indirect effect of environmental concern on purchase intention (through attitude and perceived behavioural control) was moderated by moral norms.

The study contributes to the existing literature by investigating the inconsistency between environmental concern and purchase intention. In addition, the study also investigate role of perceived marketplace influence and moral norms in stimulating organic food consumption intentions.

The emergence of perceived marketplace influence as an important determinant of organic food consumption shows that every individual needs to realise the importance of their environment friendly actions to promote organic food consumption. In addition, the study also highlights the pivotal role of moral norms in the promotion of organic food consumption. Thus, markets, policy-makers, family, friends, society all should promote and inculcate the spirit of contributing in the cause of safeguarding the environment to the young children specially by promoting consumption of organic foods.

The study examines the role of perceived marketplace influence as predictor of purchase intention towards organic foods which is rarely explored specially in the domain of organic food consumption. In addition, the results also produced some novel insights into the moderating role of moral norms.

The purpose of this paper is to design an output-feedback algorithm based on low-power observer (LPO), robust chattering-free controller and nonlinear disturbance observer (DO) to achieve trajectory tracking of quadrotor in the Cartesian plane.

To achieve trajectory tracking control, firstly the decoupled rotational and translational model of quadrotor are modified by introducing backstepped state-space variables. In the second step, robust integral sliding mode control is designed based on the proportional-integral-derivative (PID) technique. In the third step, a DO is constructed. In next step, the measurable outputs, i.e. rotational and translational state variables, are used to design the LPO. Finally, in the control algorithm all state variables and its rates are replaced with its estimates obtained using the state-observer.

The finding includes output-feedback control (OFC) algorithm designed by using a LPO. A modified backstepping model for rotational and rotational systems is developed prior to the design of integral sliding mode control based on PID technique. Unlike traditional high-gain observers (HGO), this paper used the LPO for state estimation of quadrotor systems to solve the problem of peaking phenomenon in HGO. Furthermore, a nonlinear DO is designed such that it attenuates disturbance with unknown magnitude and frequency. Moreover, a chattering reduction criterion has been introduced to solve the inherited chattering issue of controllers based on sliding mode technique.

This paper presents input and output data-driven model-free control algorithm. That is, only input and output of the quadrotor model are required to achieve the trajectory tracking control. Therefore, for practical implementation, the number of on-board sensor is reduced.

Although extensive research has been done for designing OFC algorithms for quadrotor, LPO has never been implemented for the rotational and translational state estimations of quadrotor. Furthermore, the mathematical model of rotational and translational systems is modified by using backstepped variables followed by the controller designed using PID and integral sliding mode control technique. Moreover, a DO is developed for attenuation of disturbance with unknown bound, magnitude and frequency.

This study examines the importance of income income-shifting arrangements of US multinational corporations (MNCs) on future stock price crash risk.

This study employs a sample of 7,641 corporation-year observations over the 2005–2017 period and uses ordinary least squares regression analysis.

The authors find that the income-shifting arrangements of MNCs are positively and significantly associated with stock price crash risk after controlling for corporate tax avoidance and other known determinants of stock price crash risk in the regression model. This result is robust to alternative measures of stock price crash risk and income-shifting, and several endogeneity tests. The authors also observe that income-shifting arrangements increase stock price crash risk both directly and indirectly through the information opacity channel. Finally, in cross-sectional analyses, the authors find that the positive association between income-shifting and stock price crash risk is more pronounced for MNCs that use tax haven subsidiaries and have weak corporate governance mechanisms.

The authors provide new empirical evidence that MNCs will likely face significant capital market consequences regarding their income-shifting arrangements.

This paper aims to draw lessons for telecommunications (telecom) network deployment in India, from a study of policy and regulatory approaches taken by other federal administrations in streamlining processes for site clearances, grant of rights of way (RoW) and approvals for local infrastructure deployment and sharing. With the urgent need for setting up small cells and rapid fiberisation of networks in the 5G era, the importance of such processes has gained prominence.

The authors adopt qualitative thematic content analysis with three-tier coding and classification to identify themes in archival and current documentary data and information obtained from subject-matter experts in the countries studied.

Formulation and implementation of telecom policy is led by national governments. However, national telecom administrations, in recognition of new needs, have co-opted states and local authorities as partners in development of telecom networks, providing the overall framework, guidance and appropriate incentives where required.

This cooperative model could work well in India, where telecom policy making and regulation is the prerogative of the central government, but administration of RoW and local clearances for cable laying, tower siting and associated infrastructure activities for expanding telecom networks are left to decentralised decision-making in the states and local bodies.

This research attempts to sytematise, thematise and draw cross-country comparisons to inform regulatory and administrative policy for 5G infrastructure rollout in India.

The purpose of this paper is to address the challenging issue of developing a quantitative approach for the representation of cultural significance data in heritage information systems (HIS). The authors propose to provide experts in the field with a dedicated framework to structure and integrate targeted data about historical objects' significance in such environments.

This research seeks the identification of key indicators which allow to better inform decision-makers about cultural significance. Identified concepts are formalized in a data structure through conceptual data modeling, taking advantage on unified modeling language (HIS). The design science research (DSR) method is implemented to facilitate the development of the data model.

This paper proposes a practical solution for the formalization of data related to the significance of objects in HIS. The authors end up with a data model which enables multiple knowledge representations through data analysis and information retrieval.

The framework proposed in this article supports a more sustainable vision of heritage preservation as the framework enhances the involvement of all stakeholders in the conservation and management of historical sites. The data model supports explicit communications of the significance of historical objects and strengthens the synergy between the stakeholders involved in different phases of the conservation process.