Work has begun in earnest to formulate a post-2020 Global Biodiversity Framework which will outline the vision and targets for the next decade of biodiversity conservation and beyond. However, the performance of the 2011-2020 Strategic Plan for Biodiversity suggests that even a meaningful target can fail to deliver if not accompanied by fit-for-purpose indicators. Here we provide a review of how ‘protected area’ effectiveness was addressed in the 2011-2020 plan and based on this, provide recommendations for fit-for-purpose indicators that will measure how such efforts contribute to the conservation of biodiversity. Indicators need to be built on quantitative data from site-level biodiversity monitoring of species and ecosystems combined with measurements of the state of nature in near-time, informed by remote-sensed products and other technologies. Additionally, indicators need to capture whether the essential elements of good management are in place including the identification of ecological values, threats, and objectives, equitable governance, and sufficient management resources and capacity. These fit-for-purpose indicators will require multilateral collaboration to galvanize support for, and resources to develop, the necessary infrastructure to collate and store information from countries.

Supplier Relationship Management (SRM) is a critical aspect of supply chain management, particularly in today's dynamic and interconnected business environment. This qualitative research study explores SRM practices within the context of supply chain dynamics, aiming to understand how organizations manage their supplier relationships to achieve strategic objectives. Through semi-structured interviews, document analysis, and participant observation, key themes emerge, including collaboration, trust, risk management, performance measurement, digital technology integration, sustainability, cultural alignment, supplier development, strategic alignment, emotional intelligence, and marketing perspectives. The findings underscore the importance of collaboration and trust in fostering strategic partnerships with suppliers, mitigating risks, and enhancing supply chain resilience. Effective risk management practices, performance measurement systems, and digital technology integration are essential for improving transparency, efficiency, and responsiveness in SRM. Sustainability considerations highlight the need for aligning SRM practices with corporate social responsibility goals and stakeholder expectations. Cultural alignment, supplier development programs, strategic alignment, emotional intelligence, and marketing perspectives further enrich the understanding of how organizations navigate their supplier relationships. This study contributes to the body of knowledge on SRM by providing empirical insights into the practices, challenges, and opportunities associated with managing supplier relationships in dynamic supply chains. The findings offer valuable guidance for practitioners and policymakers seeking to enhance their SRM practices and achieve competitive advantage in today's complex business landscape.

(1) As the craft beer industry continues to expand in Canada, understanding the regional differences between the Eastern and Western markets becomes pivotal for brewing ingredient companies seeking to penetrate or expand within these regions. This study aims to provide a comprehensive review of these two distinctive markets, focusing on economic, logistical, ingredient, and cultural differences. (2) The study primarily relies on secondary data from government, industry reports, and scholarly publications, providing a holistic view of the Canadian craft beer landscape. It further highlights the need to consider market distinctions between Canada and the United States. (3) Findings reveal that Western Canadian breweries, unlike their Eastern counterparts, operate under constrained storage space, prefer locally-sourced ingredients, and offer a distinct variety of beer styles catering to local tastes. The study further uncovers the regulatory and financial complexities of the Canadian market compared to the United States. (4) Based on these findings, this study provides strategic recommendations for brewing ingredient companies, emphasizing domestic logistics, local sourcing, product adaptation, educational support, and regulatory navigation, fostering success in the Canadian craft beer landscape.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

In data-scarce regions, prudent planning and precise decision-making for sustainable development especially in agriculture remains a challenging task due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use land cover (LULC) classes and crop identification. Applying Remote Sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/ algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resources management. The (this)study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/ classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The MODIS (Moderate Resolution Imaging Spectroradiometer) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with unsupervised classification technique to get LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as a key input for agricultural mechanization planning and resources management. The accuracy of the LULC map was 78%, assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced techniques of processing, which will make it more useful and applicable for regional agriculture and environmental management.

The rapid evolution of technology has profoundly reshaped supply chain operations across various industries, compelling organizations to adapt swiftly to maintain competitive advantage. This qualitative study delves into the adaptive strategies that organizations employ to navigate the intricate landscape of digital transformation within their supply chains. By conducting comprehensive in-depth interviews with key stakeholders, including supply chain managers, technology officers, and operational staff, this research aims to uncover the multifaceted challenges, opportunities, and best practices associated with integrating digital technologies into supply chain operations. The findings of this study reveal that successful digital transformation in supply chains hinges on several critical factors. Leadership plays a pivotal role in steering the organization through the complexities of technological change, emphasizing the need for visionary and adaptive leaders who can drive strategic initiatives. Organizational culture, characterized by a willingness to embrace change and foster innovation, emerges as a significant enabler of digital transformation. Additionally, strategic planning and a clear roadmap are essential for aligning technological initiatives with business objectives and ensuring seamless integration. Employee training and development are identified as crucial elements in overcoming resistance to change and enhancing digital literacy among the workforce. The study highlights the importance of continuous learning and skill development programs to equip employees with the necessary competencies to leverage new technologies effectively. Furthermore, the research underscores the significance of collaboration and communication across different levels of the organization to foster a cohesive approach to digital transformation. Challenges such as data security concerns, substantial financial investments, and the complexity of integrating diverse technologies are also explored. The insights derived from the interviews provide a nuanced understanding of how organizations can address these challenges by adopting robust cybersecurity measures, securing adequate funding, and leveraging modular and scalable technological solutions. This research contributes valuable empirical insights into the digital transformation journey of supply chain operations, offering practical recommendations for practitioners and scholars. The implications of this study are far-reaching, providing a framework for organizations to navigate the digital landscape successfully. Key recommendations include fostering a culture of innovation, investing in leadership development, prioritizing employee training, and adopting a strategic and holistic approach to digital transformation.

This research addresses the strategic issue of why key decision-makers fail to foresee potential extreme ‘black swan’ events. Following a review of the literature, a conceptual framework is developed that identifies two types of organisational blindness that are reflected in Tetlock’s hedgehog cognitive thinking style, being the oversimplification of uncertainty (e.g., inductive biases) and an unquestioned, top-down, reference narrative. This framework is tested using a case study approach and qualitative analysis of secondary data sources available from the Royal Commission of Inquiry and other published reports following the 2010 methane explosion at the Pike River Coal Ltd’s mine (Pike) in New Zealand, that killed 29 miners and caused the loss of all funds invested. The results indicate that the combined effect of both blindness’s meant that Pike’s collective intelligence was limited, and for the three key decision-makers at the Pike River mine, some type of extreme ‘black swan’ event was apparently inevitable. This research provides theoretical and practical contributions to the analysis of business and public policy decision-making under uncertainty.

The increasing social pressure for decarbonization has placed businesses under considerable scrutiny to actively reduce carbon emissions. A critical step towards achieving this objective is to shift conventional production and consumption systems to more sustainable alternatives. Thus, there is the emergent need to understand the patterns and drivers of the transformative business models (BMs) that underpin that shift. This study adopts a mixed-methods approach that integrates different literature streams--including Sustainability Transitions Theory (STT), Strategic Niche Management (SNM), and the Business Models approach--and stakeholders’ interviews to investigate the key elements of business models that lead towards sustainable practices. This research examines the organizational arrangements of European start-ups operating between 2014 and 2020. The transformation towards decarbonized production and consumption is characterized by efficient combination of business strategies that incorporate advanced technologies (ATs), such as artificial intelligence (AI), machine learning (ML) and its algorithms, along with sustainable elements, resulting in transformative business models. By exploring the driving elements behind the transition to low-carbon approaches, this study fills a significant gap in the existing literature on business models. The findings from this research also hold relevance for policymakers to promote decarbonization.

There is a lot of pressures from organizations to continually redesign the business processes and the technology infrastructure to support the organization, but to also help organizations manage competing stakeholder needs, changing objectives and behaviour, and so on. A key part of this is making sure that all their activities in the different components of the business are interconnected and that they are moving in the same direction. Enterprise Architecture (EA) is an effective way of dealing with the challenge of designing business activities and supporting IT. This paper will summarize the organization of EA and look more closely at some major components of EA: governance architecture, business architecture, information architecture and technical architecture and consider the overlap and constructive interaction involved. Enterprise Architecture can be best understood through business process redesign. So, it is useful to briefly review the leading frameworks for business process redesign, including COBIT from ISACA, LEAN from the Toyota Group, TOGAF from the Object-Management Group, and the Capgemini Integrated Architecture Framework (IAF). All these frameworks allow us to more successful transformation, to return to the central idea of this paper.