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The purpose of this paper is to describe and analyze the impacts that the recent proliferation of private and overlapping standards is having in the trade of agricultural products from developing countries.

In a first stage industry experts in the Chilean fresh fruit trading industry were interviewed to understand the perceived impact that private standards are imposing in the industry. These interviews allowed to identify the market case study, table grapes, the landscape of private standards and their prevalence in different countries. In a second stage, a gravity trade model for trade in table grapes was estimated, with a focus on the more stringent countries identified by experts in the first stage.

We show evidence that the proliferation of private standards required by large European retailers has diverted trade away from more stringent countries that require more certifications (and into less stringent European markets). We also show that the costs of these additional certifications have been shared by trading partners, via an increase in direct sales, as opposed to consignment (the traditional marketing mode), which is associated with higher prices.

The impacts of the recent proliferation of private and overlapping standards in international trade needs to be better understood both by the legal and economic literature. While the use of private standards has been growing since the 1990s, there is a recent trend of large European retailers imposing their own and overlapping standards that needs to be better understood to inform policy.

While there is a thin literature on the impact of private standards on trade, most of this has studied the effects of the now de facto mandatory GlobalGAP certification. However, there is a recent trend by large European retailers of demanding their own private certifications, together with other already existing overlapping private standards. This study describes and analyzes the impacts of this rather new trend.

The purpose of this paper is to analyse the influence of economic growth, digitalisation, eco-innovation, energy consumption and patents on environmental technologies on the volume of greenhouse gas emissions (GHG) recorded in European countries for a period of nine years (2010–2018).

Two empirical methods were integrated into the theoretical approach developed based on the analysis of the current scientific framework. Multiple linear regression, an extended version of the OLS model, and a non-causal analysis as a robustness method, Dumitrescu–Hurlin, were used to achieve the proposed research objective.

Digitalisation described by the number of individual Internet users and patents on environmental technologies determines the amount of GHG in Europe, and economic growth continues to have a significant effect on the amount of emissions, as well as the consumption of renewable energy. European countries are not framed in well-established patterns, but the economic growth, digitalisation, eco-innovation and renewable energy have an impact on the amount of GHG in one way or another. In many European countries, the amount of GHGs is decreasing as a result of economic growth, changes in the energy field and digitalisation. The positive influence of economic growth on climate neutrality depends on its degree of sustainability, while patents have the same conditional effect of their translation into environmentally efficient technologies.

This study has a number of limitations which derive, first of all, from the lack of digitalisation indicators. The missing data restricted the inclusion in the analysis of variables relevant to the description of the European digitalisation process, also obtaining conclusive results on the effects of digitalisation on GHG emissions.

A similar analysis of the relationship among the amount of greenhouse gas emissions and economic growth, digitalisation, eco-innovation and renewable energy is less common in the literature. Also, the results can be inspirational in the sphere of macroeconomic policy.

This study aims to explore the relationship between innovation and strategic management in contemporary enterprises, emphasizing the navigation of organizational change for sustainable competitive advantage. This study addresses the challenge of adapting to dynamic environments and the critical role of leadership, organizational culture and collaboration in successful innovation management.

The authors used the typology research design and comparative analysis to explore the principles and strategies underlying different innovation approaches. This study examines their impact on organizational structures, resource allocation and the integration of technological advancements with managerial practices.

The authors developed a typology of two innovation management models. The sequential approach emphasizes phased and incremental innovation, while the simultaneous approach advocates for dynamic and comprehensive integration of innovation across the organization. Each model presents distinct advantages and challenges, underscoring the need for a tailored approach based on the enterprise’s context and objectives. Mature companies may benefit from the sequential approach to gradually evolve their innovation, while new and high-tech-intensive companies can leverage the simultaneous approach for dynamic and continuous innovation.

Future research should examine local bodies and trade unions’ perception on the energy crisis’ impact toward rural entrepreneurship.

The findings are useful to Greek and European policymakers and rural micro-entrepreneurs as the experience of dealing with several previous crises can be a useful tool when dealing with current and future crises.

This study enhances understanding of the complex interplay between organizational innovation and strategy. The authors recommend further exploration of emerging technologies, cultural values, collaboration, sustainable practices and changing customer behavior to boost innovation capabilities and achieve success.

The purpose of this study is to investigate the influence of design for lean six sigma (DFLSS) on operational functions of the innovation management model by appraising an innovation management assessment framework.

An empirical approach for evaluating causal relationships among various constructs in the model phases that identify optimum pathways in achieving commercial success was adopted. A quantitative analysis of survey data were collected from large, medium and small organiations, including incubators in ANZ (Australia, New Zealand) and TMSV (Thailand, Malaysia, Sri Lanka and Vietnam).

The structural equation modelling recursive path analysis results of the model provide empirical evidence and pathways through the various constructs considered in the model. All these pathways lead to delivering optimum commercialization success (CS). Furthermore, DFLSS is confirmed as an enabler and has direct one-to-one and indirect influence on all the operational function constructs of the model including commercial success.

This study had a relatively small sample size of completed responses obtained from the population and a constrained ability to compare commercialization success (CS) between the two regions in the dataset. Future studies could be conducted on a global scale to increase responses.

The research findings enabled the development of important and practical guidelines for managers and innovation practitioners engaged in planning and management of innovation.

This research offers a holistic approach for integrating DFLSS with stage gate phases of innovation management assessment framework, supported by empirical evidence, to aid organizations in effectively managing the innovation process and achieving greater success in commercialization.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Using perspectives from the technology affordance and social capital theories, this study aims to unpack the process through which platform-enabled co-development unfolds in supply chain contexts. Specifically, it explores how innovation outcomes can be fostered through platform affordances and supply chain relationship (SCR) capital.

The paper integrates literature on digital platforms, SCRs and co-development to produce an integrative framework, developing propositions on the relationships among digital platforms, SCR capital and innovation outcomes.

The authors identify affordances for distinctive strategic use of platforms: value co-creation, relationship building and strategic learning. The authors discuss ways in which each affordance contributes to the advances in SCR capital, thus altogether enabling focal firms to orchestrate and integrate internal and external resources to attain incremental and radical innovation.

Based on the proposed research framework, further empirical studies can use quantitative data to measure the relationship between affordances and SCR capital and use longitudinal case studies to explore how affordances and SCR capital evolve to provide more fine-grained and contextualised information in different research settings.

This paper sheds light on how the relation between the adoption of digital platforms and SCR capital shapes digitally enabled service co-development. The authors provide an alternative explanation of resource integration in platform-mediated supply chain contexts and enrich the related literature on how digital platforms can maximise value from introducing ambidextrous innovation by leveraging internal and external resources.

Social commerce (s-commerce) offers community-based platforms that facilitate customer-to-customer interactions and the development of customers' social shopping-based experience. While prior research has addressed the role of customer engagement (CE) in boosting s-commerce-based sales and performance, insight into the effect of s-commerce attributes on CE remains tenuous. Addressing this gap, this study examines the role of specific s-commerce attributes (i.e. community, collaboration, interactivity and social dynamics) on CE, which is, in turn, proposed to impact customers' repurchase- and electronic word of mouth (eWOM) intention.

A web-based survey was deployed to target users of a popular s-commerce platform, Etsy.com. Partial least squares structural equation modeling (PLS-SEM) was, then, used to analyze the survey data collected from 390 users.

The results reveal that the four examined attributes positively affect CE. The findings also demonstrate CE's positive effect on customers' repurchase- and eWOM intention.

Though CE has been identified as a key s-commerce performance indicator, little remains known about the role of specific s-commerce attributes in driving CE, as, therefore, explored in this research. Specifically, the authors examine the role of s-commerce-based community, collaboration, interactivity and social dynamics on CE. Their analyses also corroborate that CE, in turn, drives customers' post-purchase (i.e. repurchase/eWOM) intention. Managerially, our findings can be used to develop more engaging s-commerce platforms.

Despite considerable research on the subject, there is still some misunderstanding about what characterizes successful project delivery in construction projects. Evaluating project delivery success is crucial for organizations since it enables them to prepare for future growth through more effective project management mechanisms and rank the organization's projects for continuous improvement. There is considerable disagreement over a set of success criteria that can be applied to all kinds of projects when evaluating project delivery success, making it a complicated procedure for practitioners and scholars. This research seeks to alleviate the problem by validating and testing a systematic project delivery success model (3D integration model) in the Australian construction industry. The aim is to establish a dependable approach built upon prior research and reliable in evaluating delivery success for any project type.

Based on a novel project delivery success model, this research applies a case study methodology to analyse 40 construction projects undertaken by a single Australian project management consultancy. The research utilizes a mixed-method research approach and triangulates three sets of data. First, the project delivery success (PDS) scores of the projects are calculated by the model. Second, a qualitative analysis targeting the performance of the same projects using a different system called the performance assessment review (PAR) scores was obtained. These culminate in two sets of ranking. The third step seeks validation of results from the head of the partnering organization that has undertaken the projects.

The findings of this study indicate that the 3D integration model is accurate and reliable in measuring the success of project delivery in construction projects of various sizes, locations and durations. While the model uses six key performance indicators (KPIs) to measure delivery success, it is evident that three of these may significantly improve the likelihood of PDS: value, speed and impact. Project managers should focus on these priority aspects of performance to generate better results.

Restrictions inherent to the case study approach are identified for this mixed-method multiple-case study research. There is a limitation on the sample size in this study. Despite the researcher's best efforts, no other firm was willing to share such essential data; therefore, only 40 case studies could be analysed. Nonetheless, the number of case studies met the literature's requirements for adequate units for multiple-case research. This research only looked at Australian construction projects. Thus, the conclusions may not seem applicable to other countries or industries. The authors investigated testing the PDS in the construction sector. It can assist in improving efficiency and resource optimization in this area. Nonetheless, the same technique may be used to analyse and rank the success of non-construction projects.

Despite the research conducted previously on the PDS of construction projects, there is still confusion among researchers and practitioners about what constitutes a successful project delivery. Although several studies have attempted to address this confusion, no consensus on consistent performance metrics or a practical project success model has been formed. More importantly, (1) the ability to measure success across multiple project types, (2) the use of triple bottom line (TBL) to incorporate sustainability in evaluating delivery success and (3) the use of a complexity measurement tool to adjust delivery success scores set the 3D integration model apart from others.