This study addresses the ongoing challenge of concurrently integrating Lean and Industry 4.0, a critical need for companies striving to enhance operational capabilities in increasingly digital environments. While past research focused on sequential approaches or high-level conceptualizations, this research delves into the level of 'how', applying Dynamic Capabilities theory through a deductive, four-stage mixed-method design, focused on large German manufacturing firms. First, expert interviews underwent thematic analysis to identify strategies and courses of action for integration. Next, an exploratory survey refined these results statistically. Third, the findings were validated and triangulated via a Delphi study, from which a structured integration framework emerged. Finally, confirmatory composite analysis of 236 managerial responses confirmed the reliability and validity of the framework. The study presents 44 validated actions grouped into six dimensions: Initiating, Sensing, Seizing, Transforming, Resources and Capabilities. Notably, it introduces the new 'Initiating' dimension, expanding the Dynamic Capabilities theory. While the study's focus on large German manufacturers is a limitation, the resulting framework offers practical pathways for companies unable to pursue sequential integration due to time pressures. Ultimately, this research provides both theoretical advancement and practical tools for successfully managing the simultaneous transformation towards Lean and Industry 4.0.
This study aims to derive, define and identify drivers of a circular economy in tourism (CET) to support strategic and operational transitions toward sustainable tourism systems. For this, it utilises a modified total interpretive structural modeling (m-TISM), cross-impact matrix multiplication applied to classification (MICMAC) analysis and the novel Influencer-Facilitator-Initiative (IFI) framework. Theoretically, it is based on a three-staged systematic literature review process, alongside expert consultation from industry and academia experts working in middle/senior positions. While the interpretive analysis reveals the hierarchical interactions and interdependences between the drivers of a CET, the IFI framework translates them into actionable strategies for addressing sustainable development goals (SDGs). In terms of novelty, it is among the first to synthesise interpretive logic with argumentative analysis, providing an emerging methodological synthesis in sustainability research. The theoretical contribution of the study lies in its advancement of a systems perspective towards a CET. Through its in-depth analysis of the drivers of a CET, it unpacks the complex relationships between tourists, hosts, stakeholders and the interactions that bind them. The findings illustrate how collaborative efforts can orchestrate, localise and expedite a CET. The study is relevant for policymakers, sustainability practitioners and researchers seeking to implement and operationalise circularity in tourism and sustainability in general.
Aim European grasslands rank among the most species-rich ecosystems at small spatial scales, yet their biodiversity and functioning face significant threats from climate change and land-use intensification. Functional traits more effectively explain ecosystem functions (EFs) than species identity or diversity. This study examines how future climate and land cover changes will shape grassland functional composition, addressing gaps in trait-environment relationships and large-scale functional predictions.Location Europe.Time Period 1971-2000 and 2081-2100.Major Taxa Studied 4406 distinct grassland plant species.Methods We used Boosted Regression Trees to model trait-environment relationships based on vegetation plot data from sPlotOpen, GrassPlot, and the Nordic-Baltic Grassland Vegetation Database (NBGVD). We mapped the 17 trait community-weighted means (CWMs) and three functional richness (FRic) metrics under historical conditions and two future climate scenarios to assess temporal and spatial changes in grassland functional composition.Results The trait-environment relationships are highly trait-dependent: structural and size-related traits such as plant height, leaf area and seed number were consistently well-predicted, whereas other traits were less well predicted. Mean annual temperature emerged as the strongest predictor of grassland functional composition. Climate and land cover change were predicted to drive significant spatial shifts in trait CWMs and FRic. Specifically, leaf area was predicted to decline in the Baltic Sea region and Pannonian Basin, while plant height was expected to increase across Europe. Seed number was predicted to rise at higher latitudes and in mountainous regions. Moreover, FRic was expected to decrease in temperate grasslands but increase at high latitudes and mountainous regions.Main Conclusions Our findings reveal distinct spatial patterns in functional shifts, reflecting plant adaptation to future environmental conditions. The increase in FRic at high latitudes and mountainous regions also signals ecosystem transitions that may pose additional threats to further complicate grassland conservation efforts.
In post-mining landscapes of Amazonia, evaluating faunal responses during early successional stages is essential for understanding habitat restoration trajectories and for identifying sensitive bioindicators capable of tracking ecosystem recovery. Herbivorous beetles constitute a highly host-dependent group whose diversity, specialization, and spatial turnover can reveal how resource heterogeneity and vegetation structure shape community reassembly in regenerating ecosystems. The objective of this study was to assess whether a five-year period of natural regeneration provides sufficient structural and resource heterogeneity to support a weevil community (Coleoptera: Curculionidae) converging toward that of adjacent forest remnants. We sampled leaf-dwelling Curculionidae across seven natural regeneration sites and seven altered primary forest remnants using standardized arboreal arthropod beating methods. We recorded 482 individuals across 114 morphotypes, with forest remnants harboring greater richness, higher effective diversity, and distinct dominance–evenness patterns compared to natural regeneration sites. Tree richness was the main predictor of weevil abundance, species richness, and diversity of common taxa. Both habitats exhibited high species turnover among sampling units, yet multivariate analyses revealed clear compositional differences between forest and regenerating areas. Our findings indicate that five years of natural regeneration is insufficient for re-establishing a Curculionidae community structurally or compositionally comparable to forest remnants. These results demonstrate that the recovery of weevil assemblages remains strongly limited by reduced host-plant heterogeneity and suggest that enrichment planting of key tree species may accelerate restoration trajectories.
PurposeExponential population growth coupled with dwindling resources has invariably led to the adoption of smart and sustainable agriculture methods. Data-driven predictive analytics are force multipliers in mitigating these challenges; however, critical challenges exist in their adoption in developing economies. This study provides insights into the challenges of adopting advanced analytics in the agricultural operations of developing economies.Design/methodology/approachThe preliminary challenges were systematically identified through an extensive literature review and refined with expert validation using the fuzzy-Delphi Method (FDM). The opinions of nine experts were then applied in the Neutrosophic DEMATEL method to justify and construct the contextual interrelationships among the challenges, ensuring both rigor and methodological novelty. This integrated methodology led to precision in selecting the challenges and novelty in the causal mapping of their interplay.FindingsThe findings reveal that network outages and a lack of supporting infrastructure are significant challenges impeding the implementation of analytics solutions in farming operations. The considerable effect group challenges were data heterogeneity and lack of user expertise and skill, reflecting their dependency on other challenges.Research limitations/implicationsThe research limitation lies in the small sample of experts and methodological dependency; however, it contributes to the literature by providing frameworks for understanding adoption challenges.Originality/valueThe originality of this work lies in its twofold contribution: first, systematically identifying and validating the critical challenges hindering the integration of analytics into farming operations in developing economies; second, offering actionable insights that support managers and policymakers in advancing data-driven and sustainable agricultural practices.
