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Reducing carbon emissions in agriculture is vital for fostering sustainable agricultural growth and promoting ecological well-being in rural areas. The adoption of Low-Carbon Agriculture (LCA) by farmers holds great potential to accomplish substantial reductions in carbon emissions. The purpose of this study is to explore the farmers' preference and willingness to engage in LCA.

This study employs the Choice Experiment (CE) method to examine farmers' preferences and willingness to adopt LCA, using field survey data of 544 rural farmers in the Weihe River Basin between June and July 2023. We further investigate differences in willingness to pay (WTP) and personal characteristics among different farmer categories.

The empirical results reveal that farmers prioritize government-led initiatives providing pertinent technical training as a key aspect of the LCA program. Farmers' decisions to participate in LCA are influenced by factors including age, gender, education and the proportion of farm income in household income, with their evaluations further shaped by subjective attitudes and habits. Notably, we discovered that nearly half of the farmers exhibit indifference towards LCA attributes.

To the best of the authors' knowledge, this study is the first to investigate farmers' attitudes toward LCA from their own perspectives and to analyze the factors influencing them from both subjective and objective standpoints. This study presents a fresh perspective for advocating LCA, bolstering rural ecology and nurturing sustainable development in developing nations.

There are two main objectives in this study. First, we aim to develop a set of constructs for health task management support (HTMS) features to evaluate which health-related tasks are supported by mobile health application (mHealth app) functions. Second, drawing on innovation resistance theory (IRT), we examine the impacts of the newly developed HTMS dimensions on perceived usefulness, alongside other barrier factors contributing to technology anxiety.

Using a mixed-method research design, this research seeks to develop new measurement scales that reflect how mHealth apps support older adults’ health-related needs based on interviews. Subsequently, data were collected from older adults and exploratory factor analysis was used to confirm the validity of the new scales. Partial least squares structural equation modeling (PLS-SEM) was used to analyze survey data from 602 older adults.

The PLS-SEM results indicated that medical management task support, dietary task support, and exercise task support were positively associated with perceived usefulness, while perceived complexity and dispositional resistance to change were identified as antecedents of technology anxiety. Perceived usefulness and technology anxiety were found to positively and negatively influence adoption intention, respectively.

This study enriches the information systems literature by developing a multidimensional construct that delineates how older adults’ health-related needs can be supported by features of mHealth apps. Drawing on IRT, we complement the existing literature on resistance to innovation by systematically examining the impact of five types of barriers on technology anxiety.

This study investigates barriers to consumers’ organic food purchasing. It identifies the factors and the extent to which they influence their purchase behaviours and future purchase intentions (i.e. continuance purchase intentions).

It combines qualitative and quantitative methods across two phases. Longitudinal research was carried out in two phases. It involved a thematic analysis and a covariance-based structural equation modelling approach. During Phase-1 and Phase-2, responses were collected from 376 and 351 respondents, respectively.

Phase 1 found the value barrier was significantly affecting the consumers’ purchase intention, while Phase 2 identified the impacts from both image and value barriers on purchase intentions. Notably, purchase intention affected continuance intention in both phases, while ethnocentrism showed no influence.

Using the innovation resistance theory, this study sheds light on the factors that prevent purchase intention. It offers valuable insights for policymakers and for the marketers of organic foods. This contribution implies that value and usage barriers were affecting the consumers’ purchase intentions in the short as well as in the long term. In sum, it suggests that consumers were not purchasing organic food as they felt it was either overpriced, not available in the market or because they were sceptical about its organic labelling.

The main purpose of this study is to determine the accounting analytics technology (AAT) adoption among manufacturing small and medium-sized enterprises (SMEs) based on the extended technology acceptance model (TAM).

The quantitative research approach with online surveys was used to collect data from 219 accounting managers among manufacturing SMEs in Jordan. To test the suggested research model, partial least squares structural equation modeling was used.

The findings indicated that all direct paths were found to be significant in the hypothesized directions. Ultimately, the results also revealed that perceived usefulness has mediated the relationship between perceived ease of use and intention to use AAT, and hence all direct and indirect hypotheses were accepted.

This research has successfully extended the TAM model in the context of AAT adoption among Jordanian manufacturing SMEs by including new factors along with the original factors of the TAM model, particularly in the postpandemic era.

This study introduces the Digital Maturity Assessment Model (DMAM), a model tailored to assess the digital maturity of SMEs, tracing its development from addressing business challenges to establishing a comparative analysis framework grounded in Resource Dependence Theory (RDT).

DMAM is based on positivist philosophy and objectivist epistemology, supported by Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). The methodology involves iterative development, from problem identification to creating a practical solution for assessing SMEs' digital maturity and guiding digitalization efforts.

DMAM offers a clear and specific methodology, distinguishing itself by addressing the unique needs of SMEs, particularly resource-dependent ones. The model’s development fills critical gaps in existing literature and provides a practical artifact for SMEs' digitalization.

DMAM is original in its focus on the specific needs of resource-dependent SMEs, offering actionable recommendations and addressing shortcomings in existing models. It serves as a foundational framework for SMEs' digital transformation, making a significant contribution to the digital maturity assessment literature.

This study aims to elaborate on the antecedents of muzakki (zakat payers) paying zakat (Islamic philanthropy) through institutions with social media arrangements, while the drivers of social media engagement are used in the conceptual model with trust and intention.

Overall, the final sample of 230 respondents was obtained through the database of official zakat management institutions. Regarding analytical tools, this study combines the partial least square structural equation modelling and necessary condition analysis approaches to explore research findings.

The findings show that firm-generated information and trust play an important role directly and indirectly. At the same time, other constructions, such as social factors and user-based factors, provide variations in necessary conditions to increase the muzakki’s intention to channel their zakat through institutions.

Zakat institutions must focus on improving social media-based services by integrating important information, such as credibility and transparency, with muzakki’s preferences. Additionally, zakat information on social media must be attractively packaged and contain facilities that muzakki can use in communicating, such as; their opinions, suggestions and input. The findings, in general, underscore the attachment between muzakki and zakat institutions through social media, which can significantly impact the positive environment of zakat institutions.

To the author’s knowledge, this study is pioneering in conceptualizing and testing a theoretical model linking drivers of social media engagement, trust and intention to pay zakat through the institution, particularly in the levels of necessity.

The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.

This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.

The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.

Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

As a high-reward strategy to differentiate social platforms, value co-creation is increasingly becoming a tool to enhance customers' social attachment. However, there is still a lack of academic understanding of the value co-creation that enables users to build social attachment with social platforms. To address this challenge, we develop and then examine a theoretical model grounded in value co-creation theory considering the relationship between value co-creation and social attachment, and also explore the mediating effect of user experience and the moderating effect of self-disclosure.

This study takes representative social platform users as the research object, chooses Questionnaire Star as the platform for questionnaire distribution and collection and collects 531 eligible data through the snowball sampling questionnaire method. And then, MPLUS7.4 is used to analyze the data and thus examine our proposed theoretical model.

The results of structural equation modeling analysis suggest that two dimensions of value co-creation (i.e. initiated value co-creation and spontaneous value co-creation) affect social attachment not only directly but also indirectly (i.e. the mediating role of user experience) and that self-disclosure moderates the impact of value co-creation affecting social attachment.

This study verifies the impact of different dimensions of value co-creation toward social platforms on social attachment, showing that value co-creation plays an important role in developing users' social attachment and provides practical implications for promoting the sustainable development of social platforms and building users' psychological well-being.