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Description MBA 580 Case Question and Analysis Case: AI Wars Student Learning Outcomes Analyze options presented in the case Write case analysis that succinctly argues for a point of view and is supported by evidence Understand the interaction between innovation and competition Case Questions: Which of the proposed end-user technologies demonstrates the most promise as a business for Google? Note: You can say “none of the above” and suggest that Google focus on behind the scenes products. Would you recommend an open or closed license for developers? Which firm is best positioned in AI? When answering these questions, be sure to consider concepts from class (and the book) including disruptive innovation, core competencies, and protections to innovation. You should approach the case as if you are consulting for Google. Therefore, the paper should be substantially your own ideas, rather than a summary of the case. No more than two (2) pages in length plus any appendix(ices) and reference page. Write your recommendation to the company in the third person (i.e. refrain from using “I”, “me”, “you” – instead say “Google should”, “he/she should”). Paper must be typed, double-spaced, 12-point font and margins should be set at one-inch on all four sides. Attach your “References” page (APA format) after the “Appendix.” This is not required, but only necessary if you went outside of the case for info.I have attached the case file. UNFORMATTED ATTACHMENT PREVIEW 9-723-434 REV: FEBRUARY 12, 2024 ANDY WU MATT HIGGINS MIAOMIAO ZHANG HANG JIANG AI Wars Although ChatGPT still has plenty of room for improvement, its release led Google’s management to declare a “code red.” For Google, this was ak in to pulling the fire alarm. Some fear the company may be approaching a moment that the biggest Silicon Valley outfits dread — the arrival of an enormous technological change that could upend the business. — Nico Grant and Cade Metz in The New York Times, December 21, 2022 In February 2024, the world was looking to Google to see what the search giant and long-time putative technical leader in artificial intelligence (AI) would do to compete in the massively hyped technology of generative AI. Over a year ago, OpenAI released ChatGPT, a text-generating chatbot that captured widespread attention. OpenAI would offer a range of new generative AI products as both user-facing applications and developer-facing application programing interfaces (APIs). In January 2023, Microsoft and OpenAI signed a $10 billion deal extending their exclusive partnership. Microsoft would continue to supply OpenAI with seemingly unlimited computing power from its Azure cloud, and Microsoft hoped that OpenAI’s technology and brand would keep Microsoft at the center of the new generative AI boom. Microsoft announced that it would soon begin deploying OpenAI’s technologies throughout its suite of products, from its Microsoft 365 productivity apps to its search engine Bing. 1 Google needed to decide how to respond to the threat posed by OpenAI and Microsoft. Google had a decade of experience developing and deploying AI and machine learning (ML) technologies in its products, but much of their AI work happened in-house and behind the scenes. Google researchers had invented the transformer architecture that made the generative breakthroughs demonstrated by GPT possible. Breakthroughs in AI had been quietly supercharging Google products like Search and Ads for years, but most of the product work was internal and little of it had penetrated the public consciousness. Until 2022, Google leadership had been deliberately cautious about revealing the extent of their AI progress and opening Google’s experimental AI tools to the public. Was generative AI really ready for user-facing applications? Was the public, not to mention the Google PR department, ready for the changes and controversies that more visible and active AI might unleash? What did Google have to gain, or lose, in this opening salvo of the AI wars? Most pressingly, how should Google respond to moves from Microsoft, Meta, Amazon, and many others to commercialize generative AI in what was becoming the biggest big tech narrative of 2024? HBS Professor Andy Wu, Research Associate Matt Higgins, HBS Doctoral Student Miaomiao Zhang, and Doctoral Student Hang Jiang (MIT) prepared this case. This case was developed from published sources. Funding for the development of this case was provided by Harvard Business School and not by the company. HBS cases are developed solely as the basis for class discussion. Cases are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. Copyright © 2023, 2024 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-5457685, write Harvard Business School Publishing, Boston, MA 02163, or go to www.hbsp.harvard.edu. This publication may not be digitized, photocopied, or otherwise reproduced, posted, or transmitted, without the permission of Harvard Business School. This document is authorized for use only by Jada Edgren (jada.edgren@snhu.edu). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. 723-434 AI Wars Google Google’s homegrown AI project, Google Brain, started in 2011 as an exploratory collaboration involving Stanford computer science professor Andrew Ng, 2 initially focusing on the development of neural networks as a general-purpose AI technology. a Google Brain developed DistBelief, a proprietary internal machine learning system for efficiently training deep learning neural networks. 3 Google internally refined DistBelief over time until finally releasing it to the public as an open-source developer platform, TensorFlow, in 2015. 4 TensorFlow was instrumental in the development of deep learning neural networks, both inside and outside Google. For years, TensorFlow was the most popular tool for artificial intelligence (AI) and machine learning (ML) applications in the world. Since its inception, Google Brain’s research and approach to AI had never been far from Google’s products. A list of which Google products made use of Google Brain’s AI and ML breakthroughs, or any details about how they were implemented, was not public knowledge, but public research papers and blog posts documented the use of machine learning in products such as Google Translate 5 and Google Maps, 6 among others. Google Brain was not Google’s only AI interest. Since 2011, Google acquired a number of AI companies, some rolled into existing teams at Google and others operated as subsidiaries. In March 2013, Google acquired DNNresearch, a deep neural networks startup founded by University of Toronto professor Geoffrey Hinton, one of the pioneering academics of the deep learning approach. 7 A decade later in May 2023, Hinton resigned from Google to speak out about the dangers of the technology. In an interview, Hinton said, “I don’t think they should scale this up more until they have understood whether they can control it.” In April 2013, Google acquired Wavii, an iPhone app that used natural language processing and machine learning to convert content from the web into structured semantic knowledge by topic, after a reported bidding war with Apple. 8 Google continued to acquire startups with AI and ML expertise over the years, more than 30 since 2009, with AI-related acquisitions totaling over $3.8 billion in 2020. 9 In January 2014, Google acquired UK-based AI lab DeepMind for over $500 million. 10 DeepMind, known for using games to test and train its AIs, made headlines when its AlphaGo program beat a human world champion at Go—a complex strategy board game sometimes likened to chess—in 2016. DeepMind operated as an independent Alphabet company, organizationally distinct from the division that housed Brain, until they were combined in April 2023 under the Google Research umbrella. 11 In addition to creating the TensorFlow framework, Google made a number of important advances in the area of natural language processing (NLP), large language models (LLM), and pre-training tools that laid the groundwork for Generative Pre-trained Transformers (GPTs). In 2017, Google introduced a new network architecture, the Transformer, that relied on a new attention mechanism to train neural networks, “dispensing with [computationally expensive] recurrence and convolutions entirely.” 12 In 2018, Google open-sourced BERT (Bidirectional Encoder Representations from Transformers), a technique for NLP pre-training that has been widely adopted by subsequent LLMs. 13 Transformers proved to be a crucial step in the emergence of high-quality LLM-powered chatbots such as GPT-3. 14 Following OpenAI’s 2020 announcement that GPT-3 would be licensed exclusively to Microsoft, a This collaboration also involved Google fellow Jeff Dean and Google researcher Greg Corrado. 2 This document is authorized for use only by Jada Edgren (jada.edgren@snhu.edu). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. AI Wars 723-434 Google ramped up its public work on LLMs. 15, b In April 2022, Google researchers published the 540billion parameter Pathways Language Model (PaLM), trained using a Google Brain system. 16 In January 2023, reports indicated that Google would introduce a suite of generative AI products over the coming months. 17 Google added a mission statement to its AI website that summarized its view that the technology should be used conservatively and in open collaboration with others (see Exhibit 1). In March 2023, Google launched its new chatbot, Bard (see Exhibit 2). 18 Anthropic In late 2022, Google invested $300 million for 10% of the AI startup Anthropic and secured Anthropic’s commitment to use Google Cloud as a preferred cloud provider. 19 In February 2023, Anthropic announced that Google Cloud would be its preferred cloud provider. 20 In late 2023, after Amazon also invested in Anthropic, Google agreed to invest up to $2 billion more in Anthropic, with $500 million of that upfront. Anthropic also signed a multiyear deal with Google Cloud worth more than $3 billion. 21 Siblings Daniela and Dario Amodei, previously OpenAI’s VP of safety and OpenAI’s VP of research, respectively, founded Anthropic in 2021 as a for-profit, public benefit corporation with the goal of developing “large-scale AI systems that are steerable, interpretable, and robust.” 22 The founding members left OpenAI in 2019 and 2020, reportedly due to concerns about its shift to a for-profit model and the first Microsoft investment. Of the group that broke away from OpenAI to form Anthropic, Dario said, “We had this view about language models and scaling, which to be fair, I think the organization [OpenAI] supported. But we also had this view about, we need to make these models safe, in a certain way, and the need to do them within an organization where we can really believe that these principles are incorporated top to bottom.” 23 Daniela said that the founding team was attracted to “the opportunity to make a focused research bet with a small set of people who were highly aligned around a very coherent vision of AI research and AI safety.” 24 In early 2023, Anthropic began publicizing its approach to “constitutional AI” in AI safety and research circles and released its Claude model in limited beta. In March 2023, Anthropic released its Claude 2 model in limited beta, with wider public access rolling out in July to largely positive reviews. By October 2023, Claude 2 was praised as “the 2nd best publicly accessible model after OpenAI’s GPT4.” 25 In addition to the publicly accessible Claude chatbot, which was free to use in its public beta stage, Anthropic licensed Claude and its underlying constitutional AI—proprietary technologies and techniques for aligning AI systems—to other companies building AI products and services. 26 Unlike some companies that focused solely on foundation models, Anthropic was willing to create custom constitutional AI systems for partners and clients. Competitors OpenAI Founded as a non-profit research institute in 2015, San Francisco-based OpenAI set out to advance artificial general intelligence (AGI) “in the way that is most likely to benefit humanity as a whole, b In October 2021, Google researchers published GLaM (Generalist Language Model) with 1.2 trillion parameters, approximately seven times larger than GPT-3. In January 2022, Google researchers published LaMDA (Language Models for Dialog Applications), a family of Transformer-based neural language models specialized for dialog with up to 137 billion parameters pre-trained on 1.56 trillion words of public dialog data and web text. 3 This document is authorized for use only by Jada Edgren (jada.edgren@snhu.edu). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. 723-434 AI Wars unconstrained by a need to generate financial return.” OpenAI’s founding research director was machine learning expert Ilya Sutskever, formerly of Google, and the group’s founding co-chairs were Sam Altman and Elon Musk. At its founding, OpenAI received $1 billion in commitments from a group of prominent Silicon Valley investors and companies, including Altman, Musk, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services, Infosys, and YC Research. 27 In March 2019, OpenAI announced that it would restructure into two organizations: OpenAI Nonprofit would remain a 501(c)(3) operating under its original charter, and OpenAI LP, a “cappedprofit” partnership, would be overseen by the nonprofit. The new structure intended to attract new investors without compromising its mission: profits for investors in the LP would be capped at 10 percent of investment. 28 In July 2019, Microsoft announced it would invest $1 billion in OpenAI and become the exclusive cloud provider for OpenAI, collaborating on a hardware and software platform to incorporate AGI within Microsoft Azure. 29 In June 2020, the GPT-3 c API was opened (in private beta) to select researchers. 30 Within weeks of its release, GPT-3 established itself as the most powerful and useful among large language models (LLM) and the first one to offer a glimpse of mainstream usability through a public-facing API. 31 OpenAI used public internet data and large-scale human feedback to train GPT-3, hiring contractors in Kenya, Uganda, and India to annotate data. 32 The release of GPT-3 in the summer of 2020 marked a turning point in the development of language-based artificial intelligence. The term “generative AI” began to appear regularly in the media. In September 2020, Microsoft announced that it would exclusively license OpenAI’s GPT-3 model. The terms of the exclusivity were such that OpenAI could continue to offer third-party developers input and output through its public-facing API, but only Microsoft would have access to the back end and be able to use GPT-3’s data model and underlying code in its products. 33 To the community of AI researchers and observers who had hoped OpenAI would choose to open-source the model, the Microsoft deal was both a disappointment and a confirmation of their worst fears about the change in OpenAI’s non-profit status. One analyst headlined his post on the deal as, “How OpenAI Sold Its Soul for $1 Billion.” 34 A reporter wrote: “It’s not clear exactly how or if OpenAI’s ‘capped profit’ structure will change things on a day-to-day level for researchers at the entity. But generally, we’ve never been able to rely on venture capitalists to better humanity.” 35 OpenAI launched ChatGPT, a chatbot interface built on GPT-3.5 d that anyone could use, in November 2022. OpenAI reportedly spent more than $540 million developing ChatGPT in 2022 alone, a figure that reflected the high costs of training models and helped explain OpenAI’s continued reliance on Microsoft for computing power and cash.36 In January 2023, OpenAI and Microsoft announced a “multiyear, multibillion dollar” extension to their partnership with a new investment from Microsoft. 37 Terms of the deal were not disclosed, but Microsoft’s investment was widely reported to be worth $10 billion, and rumors circulated that Microsoft would receive 75 percent of OpenAI’s profits until it secured its investment return and a 49 percent stake in the company. 38 Microsoft would also become the exclusive cloud partner for OpenAI c OpenAI’s major releases were numbered GPT-n: GPT-2 (February 2019), GPT-3 (June 2020), and GPT-4 (March 2023). In February 2019, OpenAI released Generative Pre-trained Transformer 2 (GPT-2), a language model that could learn new tasks (such as composing “original” text in a particular style) through the use of self-attention, building on the experimental and neverpublicly-released GPT-1. Despite being a breakthrough in language modeling, few took notice outside of AI research circles. d GPT-3.5 was an informal designation, not an official release, reflecting the improvements made to the model between 2020 and late 2022. 4 This document is authorized for use only by Jada Edgren (jada.edgren@snhu.edu). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. AI Wars 723-434 going forward and would begin deploying OpenAI’s models in its enterprise products immediately. “In this next phase of our partnership, developers and organizations across industries will have access to the best AI infrastructure, models, and toolchain with Azure to build and run their applications,” said Microsoft Chairman and CEO Satya Nadella. 39 Within months, Microsoft had deployed some of OpenAI’s technology in its Bing search engine and announced plans to roll out more AI features across its portfolio of products. “The expectation from Satya is that we’re pushing the envelope in A.I., and we’re going to do that across our products,” said Eric Boyd, the executive responsible for Microsoft’s AI platform team, in an early 2023 interview. 40 As OpenAI deepened its ties with Microsoft and pushed forward on the commercialization of generative AI, its transformation from non-profit to for-profit status rankled critics, including at least one of its original donors. In March 2023, Elon Musk tweeted: “I’m still confused as to how a non-profit to which I donated ~$100M somehow became a $30B market cap for-profit. If this is legal, why doesn’t everyone do it?” 41 OpenAI offered a number of its technologies to third-party developers through its API service. As of early 2023 these included Access GPT-3, DALL-E 2 (prompt-based image generation), and Codex (a set of tools for converting natural language to code). Several third-party applications had already begun building on OpenAI’s platforms: GitHub Copilot (owned by Microsoft) drew on the Codex platform to create a powerful predictive autocomplete tool for programmers, and Duolingo used GPT3.5 to interpret user input and provide French grammar corrections in its language instruction app. 42 In March 2023, OpenAI released GPT-4 (see Exhibit 3). This next-generation model demonstrated improved conversational abilities, responsiveness to user steering, potential for image-based inputs, and safety precautions to prevent harmful advice or inappropriate content. With the release of GPT-4, OpenAI published benchmarks on the relative performance of GPT-3.5 and GPT-4 on a range of standardized tests (the Uniform Bar Exam, LSAT, GRE, and topic-specific AP tests from Chemistry to English Literature). In most tests, GPT-4 outperformed all other models, often significantly, achieving what OpenAI called “human-level performance on various professional and academic benchmarks.” 43 Behind the scenes, GPT-4 was reported to be more computationally efficient and cost-effective than its predecessor, gains that OpenAI had presumably achieved through advances in training techniques and model architecture. Researchers reading through the technical documentation of GPT-4 in search of information on how those gains were achieved — a common practice with the release of a new model in the pre-commercial days of AI research labs — found only the following passage: “Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.” 44 Sutskever underscored OpenAI’s shift toward closed models, proprietary training methods, and the non-disclosure of training data in an interview: “We were wrong. Flat out, we were wrong. If you believe, as we do, that at some point, AI – AGI – is going to be extremely, unbelievably potent, then it just does not make sense to open-source. It is a bad idea…I fully expect that in a few years it’s going to be completely obvious to everyone that open-sourcing AI is just not wise.” 45 While it continued to remain unclear how copyright law would apply to the use of copyrighted (but publicly available) data and content, OpenAI drew much of the attention around the issue. In July 2023, OpenAI reached an agreement to license content from Associated Press under a most favored nations clause entitling the publisher to revise the agreement if another publisher got a better deal. 46 In December 2023, the New York Times sued OpenAI and Microsoft for allegedly infringing on its content, joining several other ongoing lawsuits from book authors. 47 5 This document is authorized for use only by Jada Edgren (jada.edgren@snhu.edu). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. 723-434 AI Wars Microsoft Microsoft had been working on the natural language component of artificial intelligence since the founding of Microsoft Research in 1990. The internal research division made an immediate splash by hiring away three of the top computational linguists of the era from rival IBM to start its NLP research group. Within a few years, Microsoft had become a world leader in the development of grammar detection, spell check, and automatic translation tools. 48 Advances in machine learning picked up when the cloud era got underway in the early 2010s. Satya Nadella was promoted to president of the Server and Tools Division in 2011, the division where Microsoft’s then-nascent cloud initiative, Azure, was housed. In February 2014, Nadella took over the CEO role from his predecessor Steve Ballmer. That summer, Microsoft announced Azure ML, one of the first cloud services to offer a machine learning platform. 49 In the post-transformer deep-learning era (since 2017), Microsoft conducted advanced AI, ML, and LLM research primarily through its Turing program, a collaboration with academic researchers from around the world. 50 The Turing Natural Language Generation model (Turing-NLG), published in 2020, contained 17 billion parameters and outperformed other state-of-the-art models at the time. 51 In October 2021, Nvidia and Microsoft Research’s Turing program combined their LLM efforts to publish Megatron-Turing NLG, the world’s largest generative language model with 530 billion parameters. 52 When Microsoft invested its first $1 billion in OpenAI in 2019, the headline was that Azure would become OpenAI’s exclusive cloud provider. 53 One analyst noted, “Beyond the financial risks and rewards for Microsoft, the bigger prize is that it gets to work alongside OpenAI in developing the technology on Microsoft Cloud, which instantly puts Microsoft at the forefront of what could be the most important consumer technology over the next decade.” 54 According to reporting from The Information, executives inside Microsoft were skeptical in 2019 that OpenAI would live up to the hype. Peter Lee, head of Microsoft Research, found it hard to believe that OpenAI could have accomplished in a few years what Microsoft researchers had been unable to do in a decade. Even Microsoft co-founder Bill Gates warned Nadella against the OpenAI investment. Nadella proceeded with caution, using the in-house Microsoft Research group to check OpenAI’s work. Recounting the evolution of the relationship, the Information article continued: “Over time, those doubts began to fade. When Microsoft researchers compared OpenAI’s language models side by side with Microsoft’s internal models, collectively dubbed Turing, it became undeniable that the startup had built something far more sophisticated.” 55 As the partnership progressed, Microsoft began to value OpenAI as more than just a big customer for Azure or a long-term R&D bet. The more confidence Nadella gained in OpenAI’s generative AI capabilities, the more aggressively he pushed teams across the organization to integrate OpenAI’s models into its products. Microsoft’s subsequent investments, including a January 2023 investment reported to be worth $10 billion, reflected a growing confidence in its startup partner. 56 However, that confidence was called into question in November 2023 after a period of internal strife at OpenAI in which the non-profit’s board fired its CEO Altman and then was quickly forced to bring Altman back and along with a new board (Exhibit 4). Microsoft continued its own in-house AI efforts alongside its ongoing integration of OpenAI technology. Microsoft researchers worked on large-scale models across the organization, including the user-facing implementations of GPT in Office (see Exhibit 5) and Bing, and the less user-facing machine-learning infrastructure being built in Azure. Prometheus, a Microsoft-developed model that helped merge the search and chat functions in Bing, was released in February 2023. 57 An article in Wired described the ongoing internal work at Microsoft: 6 This document is authorized for use only by Jada Edgren (jada.edgren@snhu.edu). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. AI Wars 723-434 Microsoft has held back from going all-in on OpenAI’s technology. Bing’s conversational answers do not always draw on GPT-4, Ribas [Microsoft’s CVP of Search and AI] says. For prompts that Microsoft’s Prometheus system judges as simpler, Bing chat generates responses using Microsoft’s homegrown Turing language models, which consume less computing power and are more affordable to operate than the bigger and more wellrounded GPT-4 model. 58 Meta Since its founding in 2013, Facebook AI Research (FAIR), later Meta AI, had been led by Yann LeCun, a French-American AI researcher and professor of computer science at NYU. LeCun and other Facebook researchers played an important role in advancing the theoretical basis of generative AI e. 59 During the 2010s, Facebook steadily implemented this research in a range of internal applications from newsfeed ranking to content moderation, language translation, image recognition, etc. Like Google’s extensive in-house AI work, much of what Meta did in AI was never made public, so an observer could only guess at the full extent of their work based on their published papers and public contributions to open-source projects. 60 In August 2022, Meta made its chatbot prototype Blenderbot 3 available to the public. Although Blenderbot 3 was built on Meta’s open-source OPT-175B f, released in the midst of the early GPT-3 hype, and preceded ChatGPT by several months, the chatbot from Meta didn’t garner the same widespread attention or enthusiasm. Researchers and reviewers found Blenderbot underwhelming compared to the early version of GPT-3, as Vox noted in its headline from August 2022, “Why Is Meta’s New AI Chatbot So Bad?” 61 While Google traditionally led in open-source AI models and tools, Meta was quickly establishing a reputation as the new leader in open-source AI. On the open-source front, Meta developed and maintained PyTorch, a computing package and machine learning framework based on the open-source Torch package for Python. Like TensorFlow, PyTorch provided a pre-built set of developer tools that could be used to quickly set up and train deep-learning neural networks. Like Google’s investment in TensorFlow and other company-supported open-source projects, Meta’s contribution to the PyTorch ecosystem did not directly contribute to Meta’s bottom line, but its availability had valuable secondorder effects, generating goodwill and drawing the developer community to Meta’s preferred toolset. Meta’s PyTorch-based internal tools (which remained proprietary) nonetheless benefited from the rapid innovation that open source made possible, strengthening Meta’s appeal as a hub of innovation and an employer of top AI/ML engineers. In 2022, Meta transferred control of PyTorch to the Linux Foundation. 62 In February 2023, Facebook released LLaMA (Large Language Model Meta AI), also under a noncommercial GPL 3.0 open-source license, and shared it with the AI research community. Built on publicly available data, LLaMA was released in four sizes: 7 billion, 13 billion, 33 billion, and 65 billion parameters, making it more flexible for researchers with different computational capacities. LLaMA’s 13 billion-parameter model outperformed GPT-3 on most benchmarks, and LLaMA-65B was e One of their contributions was Generative Adversarial Networks (GANs), which involved pitting two neural networks (a generator and a discriminator) against each other to create an artificial intelligence co-evolutionary arms race capable of doing things like generating novel text and realistic-looking images. Throughout the 2010s, FAIR also made advances in self-supervised learning (SSL) on large unstructured data sets and rapid text classification, inventing a framework called fastText, a simplified approach to text classification that could run on basic inexpensive hardware. f In May 2022, Meta released OPT-175B (Open Pretrained Transformer), a large language model with 175 billion parameters under a non-commercial GPL 3.0 license. In November 2022, Meta AI announced CICERO, an AI agent that had achieved humanlevel performance at the strategy game Diplomacy. CICERO was a language model integrated with strategic reasoning to enable effective negotiation and cooperation with human players. 7 This document is authorized for use only by Jada Edgren (jada.edgren@snhu.edu). Copying or posting is an infringement of copyright. Please contact customerservice@harvardbusiness.org or 800-988-0886 for additional copies. 723-434 AI Wars competitive with the best LLMs in the world. 63 Meta also released details about how the model had been built and trained, including model weights that were proprietary for comparable models at OpenAI and Google. The relative openness of LLaMA made it possible for AI researchers outside of the biggest labs to examine how a genuinely advanced LLM was constructed. Though LLaMA was not originally released as open source, copies of it leaked shortly after it was shared with researchers. Within a few days, LLaMA had effectively become open source. 64 Meta followed through by officially licensing LlaMA 2 as open source, with limitations. The license appeared at first like many other open-source licenses, but reading further revealed additional fine print: If, on the Llama 2 version release date, the monthly active users of the products or services made available…is greater than 700 million monthly active users g in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 65 As of February 2024, Meta AI did not offer any cloud infrastructure services nor commercial API services to developers and had not announced any plans to do so. In fact, Meta looked to be doubling down on its approach of using proprietary internal tools built on open-source technologies, as they had done with PyTorch and Open Compute h. 66 In Meta’s Q1 2023 earnings call, Zuckerberg said: Right now most of the companies that are training large language models have business models that lead them to a closed approach to development. I think there’s an important opportunity to help create an open ecosystem. If we can help be a part of this, then much of the industry will standardize on using these open

Description Follow all directions. make sure to choose an event that actually happened between september and decemeber 2025 in san diego. No plagerism or ai. I am chaldean first generation american so I would look different and feel like a minority at the event. This assignment is designed to familiarize students with experience of being a minority in some social environment of their choosing. To complete the assignment, you must go by yourself to a place you have not visited before, make observations of what is going on around you, and report your feelings about being different in that environment. Please see rubric and assignment details for additional information. options for the paper More Events to Consider for the Minority Report! Day of the Dead Event Opportunities: https://www.fiestadereyes.com/Links to an external site. https://www.eventbrite.com/e/day-of-the-dead-presented-by-yank-tv-tickets-1557638030279?aff=ebdiglgoogleliveeventsLinks to an external site. https://www.facebook.com/events/officer-jeremy-henwood-memorial-park/2025-city-heights-day-of-the-dead-celebration/1097886755877235/Links to an external site. If you can't access these try googling "Day of the Dead Events Near Me" and you'll find a ton of opportunities over the next week here in San Diego! https://nrc.sdsu.edu/programs/heritage-month?utm_source=salesforce&utm_medium=emailLinks to an external site. Some Event Options for the "Being a Minority Report" https://apida.sdsu.edu/about/calendarLinks to an external site. APIDA Center Moon/Mid-Autumn Festival: Mooncakes & Tea PartyLinks to an external site. Monday, October 6, 2025. 3:00 PM - 4:00 PM Aztec Student Union, 210A-K APIDA Center Moon/Mid-Autumn Festival: Bo BingLinks to an external site. Monday, October 6, 2025. 4:00 PM - 5:00 PM APIDA Center Aztec Student Union, 210A-K APIDA Center Moon/Mid-Autumn Festival: Hoi An Decorated Paper LanternsLinks to an external site. Monday, October 6, 2025. 5:00 PM - 7:00 PM APIDA Center Aztec Student Union, 210A-K APIDA Center Game Night: Yut NoriLinks to an external site. Tuesday, October 7, 2025. 5:00 PM - 7:00 PM APIDA Center Aztec Student Union, 210A-K APIDA Center Paniolos: Hawaiian Cowboys & CowgirlsLinks to an external site. Wednesday, October 8, 2025. 5:00 PM - 7:00 PM APIDA Center APIDA Center Cultural Education Program: Filipino Repatriation Act of 1935Links to an external site. UNFORMATTED ATTACHMENT PREVIEW Dr. Quarles Mgt 467 “Being a Minority” Paper This assignment is designed to familiarize students with experience of being a minority in some social environment of their choosing. To complete the assignment, you must go by yourself to a place you have not visited before, make observations of what is going on around you, and report your feelings about being different in that environment. Examples of being the minority include a White/Caucasian American attending a Black/African-American church, a heterosexual going to a gay/lesbian event, or a non-disabled individual attending an event geared to disabled individuals. Be sure to stretch yourself by going to a place where you are in fact a “minority.” For example, a White/Caucasian American Protestant who goes to a Catholic Church service would not have as rich a minority experience as if the person went to a Black Methodist church. Minorities should also place themselves where they too are the “minority” such as a Hispanic person attending a Jewish synagogue or some other setting where they are different from others. Use your better judgment to not visit a dangerous place or where your attendance would be disruptive. In completing your paper, include the following elements: 1. 2. 3. 4. 5. 6. 7. 8. 9. The date and place where the experience took place A brief description of the setting Your reactions to the situation in terms of your thoughts and feelings The reactions of others to you What did the experience teach you about being different from others in an environment? How did it feel to be the minority? Were you identifiable? Did you have differential power from the majority? What discussions or theories from the class/book could be applied to this situation? What insights did the experience give you that you could apply to your current or past work situations? ‘A’ level papers are those that comprehensively address the questions above and incorporate theories/research discussed in your textbook, outside readings, videos, content discussed in class, and contribute insights/observations beyond regurgitation of class materials. Papers should be typed, double-spaced, in 12-in font, have 1” margins, and no longer than 5 pages in length. Late papers are accepted but they will have a 10% deduction of points for each day it is late (e.g., one day late = 10 point penalty).

Description Week 3discussion Respond to the following in a minimum of 175 words: As you contemplate starting a new business, how do you determine if there is a genuine need for your product or service? Based on the need Ngozi Opara has identified, do you feel her business will be sustainable in the long run? Why or why not? Do you feel the hydration product Allen Lim has introduced will continue to meet the need of his target market over time? Why or why not? What is the best test to confirm that people need and want your product? week 5 discussion Change is a difficult thing to navigate for many people and many organizations, but change is needed to grow. Employees and employers alike need managers to support change and go beyond accepting it to normalize change. Discuss strategies managers can use for creating positive, organizational change with their teams. How does collaboration influence positive organizational change? Provide an example scenario to illustrate the implementation of one strategy. week 5 assignment To prepare for the Week 6 Assessment, you will complete an organizational change chart, like you did in Week 1, only this time for a well-known multi-national corporation. Choose an organization such as Samsung, Starbucks, Ford Motor Company, or Waste Management that implemented a major change. For example, a sustainability initiative at Starbucks or Apple making FaceTime available to non-Apple users. Analyze the organization’s change process based on Kotter’s 8 Steps to Leading Change using the Organizational Change Chart. Make sure to complete a new analysis, do not copy any information from a previous week. (Uploaded) Consider questions such as the following as you complete your analysis: Do you think this was a positive organizational change? Why or why not? What strategies and tactics do you think would be effective in creating positive organizational change? What strategies and tactics might have worked better? UNFORMATTED ATTACHMENT PREVIEW 7:52 ? 5G+ LDR/535 v4 University of Phoenix® Organizational Change Chart Organizational Information Select an organization that needed a change to its culture as you complete the organizational change information chart. For each type of information listed in the first column, include details about the organization in the second column. Indicate your suggested actions for improvement in the third column. Type Vision Suggested Actions for Improvement Details Insert the organization's vision. Mission Insert the organization's mission. Purpose Insert the organization's purpose. Values Insert a list of the organization's values. Diversity and Equity Insert the types of the diversity and equity Inclusion Goal Strategy observed in the organization. Insert examples of overall involvement of diverse groups inclusion in decision-making and process change. Identify the goal set for organizational change. Identify the implementation strategies followed to implement the organizational change. Communication Identify the communication methods used to communicate organizational change and the change progress. Organizational Perceptions Considering the same organizational culture and change goal, rate your agreement from 1 to 5 in the second column with the statement in the first column. Use the following scale: 1. Strongly disagree 2. Somewhat disagree 3. Neither agree nor disagree 4. Somewhat agree 5. Strongly agree Statement Employees know the organization's vision. Rating (1-5) Employees know the organization's mission. Employees know the organization's purpose. Employees know the organization's values. Overall, the organization is diverse and equitable. Diverse groups are included in decision making and processes for change. The change goal was successfully met. The implementation strategies were effective. The organization's communication about the change was effective. Kotter's 8-Steps to Change Consider the goal for organizational change that you identified and the existing organizational culture. For each of Kotter's 8-Steps to Change listed in the first column, rate whether you observed that step during the implementation process in the second column. Use the following scale to rate your observation: 1. Never observed 2. Rarely observed 3. Sometimes observed 4. Often observed Identify actions you suggest for improvement in the third column. Step Name Step 1: Create Urgency. Step 2: Form a Powerful Coalition. Rating (14) Suggested Actions for Improvement Step 3: Create a Vision for Change. Step 4: Communicate the Vision. Step 5: Remove Obstacles. Step 6: Create Short-Term Wins. Step 7: Build on the Change. Step 8: Anchor the Changes in Corporate Culture. ultimedia.phoenix.edu

Description Directions: You just started your new position as a Portfolio Manager at a wealth management company. You have been asked to create a stock portfolio to offer clients. Specifically, they would like you to create a 10 stock portfolio that follows a goal or objective. Given your role and the size of the firm, they have asked you to select the investments and run risk vs. reward analysis. You must classify your portfolio as one of the following strategies: Conservative: This strategy has a focus on preserving capital. This would include companies that have been around for a long time, been through market fluctuations, and likely offer income to shareholders through dividends. Moderate: This strategy has a focus on balancing risk and reward. Designed to preserve capital, but also take on some risk to have higher returns than inflation. This would include a combination of medium to large capitalization companies that have proven consistent growth. Aggressive: This strategy is to maximize returns by taking higher risk (relative to other investments). Designed for clients to stay in over long periods of time to withstand market fluctuations. The companies in this strategy are likely small to medium in capitalization, relatively volatile in price, and reinvest earnings to keep up with growth. The firm has suggested that you adhere to two rules: Select ten stocks that you believe as a portfolio represent the strategy (risk/reward) Always diversify! This can be done by geography, industry, etc. After selecting your strategy and deciding on your portfolio: Collect price information for a recent 5 year time horizon from Yahoo! Finance (finance.yahoo.com) as follows: Enter the stock symbol. On that page click “Historical Data” For the time period, enter the “start date” and the “end date” as a recent five year period. For example: Jan 1, 2018 - Jan 1, 2023. Choose the frequency as monthly. Click “Apply”. Highlight the data from the chart. Copy and then paste the data into Excel. Delete all the columns except the date and the adjusted close. This takes into account any stock splits and dividends paid. Label the adjusted close to the name of the stock selected. Enter the next stock symbol in the main search box and search for the next stock. Do this for all 10 stocks. In addition, do this for the symbol “SPY”, the Exchange Traded Fund (ETF) for the S&P 500. Use this data to estimate the overall market. Repeat the same steps above for each stock, maintaining the same time frame. Make sure the first and last prices are in the same rows and lined up correctly. Convert these stock prices to monthly percent change (hint: create a separate worksheet within the Excel file). Compute the mean monthly returns and standard deviations for the monthly returns of each of the stocks. Convert the statistics to annual for easier interpretation (multiply the mean return by 12, and the standard deviation by square root of 12: ?12). You should now have an annual risk (standard deviation) vs. return for each stock selected. Add a column in your Excel worksheet with the average return across stocks for each month (not including SPY). As a heading (title), label it “Portfolio”. This is the monthly return of an equally weighted portfolio of these 10 stocks. Compute the mean and standard deviation of monthly returns for the equally weighted portfolio. Convert these monthly statistics to annual (see step 3). In addition to the 10 stocks, portfolio, and SPY calculated statistics, look up the symbol “^TNX”. The current price of the 10-year treasury can be used as a risk-free rate. With all of the statistics gathered, create an Excel plot with the annual standard deviation (volatility) on the x-axis and annual average return on the y-axis. You should create the Securities Market Line (SML) with the risk-free rate and market return (SPY). The SML should look similar to page 268 of the textbook (standard deviation on x-axis). All the axis, data points, should be labeled and presented in a professional manner. Create three columns on your spreadsheet with the statistics you solved. The first column will have the ticker (symbol) and “Portfolio”, the second will have annual standard deviation, and the third will have annual mean return. The table should include all 10 stocks selected, the Portfolio, SPY, and ^TNX (standard deviation = 0). Highlight the data in the last two columns (standard deviation and mean), choose: > Insert > Chart > XY Scatter Plot. Complete the chart wizard with labels, titles, and headings. Write a 1-2 page paper with: Introduction: Identify the strategy, the goal or objective, and what portfolio you selected. Body/Analysis: Compare and contrast the investments from a risk / reward perspective over the last five years. Describe the graph provided in the spreadsheet created. What do you notice about the average of the volatilities with the individual stocks compared to the volatility of the equally weighted portfolio? What is the advantage of owning the portfolio? What is above, below, and on the SML? Conclusion: Summarize the findings from your portfolio analysis. Provide any insight about future investment. Deliverables: Upload the Word document and Excel file to the Assignments Week 8 folder. Make sure to cite the sources using APA format.

Description Week4 finance discussion ALTERNATIVES TO BANKRUPTCY What are the pros and cons of filing bankruptcy? What alternatives are out there? Is it moral not to pay creditors? When collectively considering the moral and long-term implications for declaring bankruptcy, how wise of an action is it really? Week 4 assignment finance Develop a plan for managing your debt. How many sources of debt do you currently have, and what are the balances owed on each? What specific behavioral steps do you personally need to take to match your debt aspirations with your debt reality? How will you practically implement these steps? Use the attached assignment document to complete and submit for grading. (Uploaded) week 4 computer discussion SUM The SUM function is the most commonly used function in spreadsheets which is covered this week in our learning materials. Describe a scenario where you could personally use the SUM function in a worksheet and briefly discuss what factors you would consider in deciding to take the time to set up SUM functions in a spreadsheet vs. just manually updating totals with a calculator. Additionally, submit an Excel spreadsheet as an attachment that contains a column of at least three values and a Total row at the bottom that uses the SUM function to add those cells. To better showcase your data, look to label your values in a column to the left and label your Total row. Feel free to get creative with formatting, the kind of numerical values you are adding, and your subject matter. Include your full name in a cell below your data. (Remember: do not include any personal data from you or friends/family in these submissions) Week 4 computer assignment Computer Applications for Business – Week 4 Assignment MS Excel: Comparing Televisions Start with a new spreadsheet in Microsoft Excel online or installed version of Microsoft Excel with no template applied. A template is not allowed for assignment. NOTE:You will not need to use files from the textbook for the assignment Apply your creative thinking and problem-solving skills to design and implement a solution. Part 1 You are shopping for a new television and want to compare the prices of three televisions. Research new televisions. • Create a worksheet that compares the type, size, and the price for each television, as well as the costs to add an extended warranty. • Use the concepts and techniques presented in this Excel module 1 to calculate the average price of a television and average cost of an extended warranty and to format the worksheet. • Include a chart to compare the different television costs. Part 2 Based upon the data you found, how could you chart the information to show the comparisons? Which chart would be the best to use? Answer these questions in comments or submission text area when submitting.. Save the file as CS155Week4lastnamefirstname. Ensure that you use yourlastname and firstname in the filename. The following areas will be part of grading criteria: • Descriptive title, subtitle, and headings • Data included • Type, size, and price of each television included • Average function used to average price of televisions • Average function used to average cost of extended warranty • Total for television and extended warranty for each using sum function • Total results formatted as currency displaying $ symbols • Sheet formatted • Columns adjusted so that text is not cut off, and excess space is minimized • Chart type appropriate for data it represents • Chart title is descriptive of contents • Chart formatted so that data makes sense and is easy to read • Part 2 questions answered • Correct filename Save file after completing and submit work by attaching file to week 4 assignment submission area. UNFORMATTED ATTACHMENT PREVIEW 1 7:58 5G+ 0 + Personal Debt Management - W4A FIN210 Develop a plan for managing your debt. How many sources of debt do you current have, and what are the balances owed on each? What specific behavioral steps do you personally need to take to match your debt aspirations with your debt reality? How will you practically implement these steps? Criteria: The requirements below must be met for your paper to be accepted and graded: • Write a minimum of 400 words (approximately two pages) using Microsoft Word. Attempt APA style. • • Use font size 12 and 1" margins. • Include cover page and reference page. • • • • • • At least 60 percent of your paper must be original content/writing. No more than 40 percent of your content/information may come from references. Use at least two references from outside the course. Textbook, lectures, and other materials in the course may be used, but are not counted toward the two reference requirement. Reference material (data, dates, graphs, quotes, paraphrased words, values, etc.) must be identified in the paper and listed on a reference page. Reference material (data, dates, graphs, quotes, paraphrased words, values, etc.) must come from sources such as, scholarly journals, online newspapers such as The Wall Street Journal, government websites, etc. Sources such as Wikis, Yahoo Answers, eHow, etc. are not acceptable. < ??? n-saas.blackboard.com Purchase answer to see full attachment

Description UNFORMATTED ATTACHMENT PREVIEW FACULTY OF BUSINESS & LAW MSC INTERNATIONAL BUSINESS AND MANAGEMENT CSR DISCLOSURE AND COMPANY PERFORMANCE IN THE UK SUPERMARKET INDUSTRY Module Name and Code: Independent Study Project: Strategy, Marketing and Innovation (M33561) Student Name: Apple Mahmud Supervisor Name: Gerhard Bezuidenhout Student number: 2299958 Date of Submission: 0 Abstract This paper analyses the quality of the CSR and ESG disclosure between four large UK-based supermarkets (Tesco, ASDA, Morrisons, and Sainsbury’s) in 2022-2024. Through content analysis of 298 disclosure statements, it was found that there was an industry-wide quality enhancement of 21.4% between 2022 and 2023, with environmental reporting showing the best results (3.7 average) and governance the worst (2.9 average). Sainsbury had leadership (3.4 to 3.9) whilst Tesco had transformational improvement (2.4 to 3.6). The qualitative analysis reveals that retailers incorporated ESG into the executive compensation (25 percent of the Performance Share Plans) and components of steering groups. Financial health facilitated the ability to invest in sustainability by saving £1.2-1.3 billion through cost reduction and investing in the ESG initiatives and £150-500 million in the workforce in times of economic strains. There are also major weaknesses in terms of transparency: measures of the environment are always measured in a meticulous sense of carbon tracking, but governance indicators are not very standardized in terms of linking executive compensation and board representation. The measures of the social aspect differ significantly, where community investments are entirely reported, but the indicators of employee diversity are not yet fully reported. The next competitive frontier is governance transparency and complex stakeholder disclosure, which has become an industry standard after environmental reporting. Stakeholder trust has now been pegged into detailed and quantified reporting on the entire ESG spectrum. Keywords: CSR disclosure, ESG reporting, UK supermarkets, Sustainability, Transparency, Corporate Governance 1 Table of Contents 1. 2. 3. 4. Introduction ........................................................................................................................... 5 1.1 Research Questions ......................................................................................................... 6 1.2 Research Aim .................................................................................................................. 6 Literature Review ................................................................................................................. 7 2.1 Theoretical Foundations of CSR Disclosure................................................................... 7 2.2 CSR Communication Strategies ..................................................................................... 8 2.3 Contemporary Research on Disclosure Quality .............................................................. 8 2.4 Sector-Specific Applications and Challenges ................................................................. 9 2.5 Research Gaps and Implications ..................................................................................... 9 Methodology .........................................................................................................................11 3.1 Prepare the Data .............................................................................................................11 3.2 Define the Unit of Analysis............................................................................................11 3.3 Develop Categories and Coding Scheme ...................................................................... 13 3.4 Ethical Considerations .................................................................................................. 14 Analysis ................................................................................................................................ 15 4.1 Preliminary Quantitative Overview .............................................................................. 15 4.2 Analytical Framework Validation ................................................................................. 15 4.3 Overall CSR Performance............................................................................................. 17 4.4 CSR Performance by Category (2022-2024 Average) .................................................. 18 2 4.5 Time-Based Analysis .................................................................................................... 19 4.6 Qualitative Content Analysis of CSR Disclosures ........................................................ 20 4.7 Financial Review .......................................................................................................... 23 5. Results and Discussion........................................................................................................ 24 6. Conclusion ........................................................................................................................... 27 7. References ............................................................................................................................ 29 8.0 Appendices ............................................................................................................................. 33 Appendix 1 Profiling Table of Literature Review .................................................................... 33 Appendix 2 Synthesis Table ..................................................................................................... 34 Appendix 3: Raw Data From annual Reports ........................................................................... 37 Appendix 4: Ethics Form .......................................................................................................... 99 List of Tables Table 1: Unit Categorization Framework ..................................................................................... 12 Table 2: Quality Assessment Scale and Dimensions.................................................................... 13 Table 3: Coding Scheme Validation - Tesco Sample Extract ....................................................... 15 Table 4: Reliability and Scale Statistics ....................................................................................... 16 Table 5: Item Analysis and Correlations ..................................................................................... 17 Table 6: CSR Performance by Company and Year ...................................................................... 17 3 Table 7: CSR Performance by Category (2022-2024 Average) ................................................... 18 Table 8: Time-Based Analysis (Companies with Multi-Year Data) ............................................. 19 Table 9: Comparative Analysis of CSR, ESG Governance, and Strategic Priorities ................... 20 4 1. Introduction Corporate Social Responsibility (CSR) refers to the voluntary initiatives that businesses undertake to address social, environmental, and ethical concerns alongside their economic goals (Wirba, 2024). The concept gained prominence in the mid-20th century when scholars and practitioners began recognizing that firms hold responsibilities beyond profit maximization. Carroll’s (1991) Pyramid of CSR, which highlights economic, legal, ethical, and philanthropic responsibilities, remains one of the most influential frameworks in defining its scope. Over time, globalization, environmental challenges, and corporate scandals intensified the call for greater accountability, leading to the evolution of CSR as a central element of corporate governance and stakeholder management (tKusyk, 2021). In today’s business environment, CSR is closely linked to Environmental, Social, and Governance (ESG) frameworks, which provide measurable benchmarks for disclosure and performance evaluation (Alsayegh et al., 2020). Consumers, investors, and regulators increasingly expect companies to demonstrate transparency, sustainability, and social impact, making CSR not just a reputational tool but also a strategic necessity. In industries with complex supply chains, such as the UK supermarket sector, CSR has become particularly significant, as companies are under pressure to address issues like fair trade, carbon emissions, plastic reduction, and community support. Thus, CSR has transformed from a philanthropic add-on into an integral part of corporate strategy that directly influences long-term competitiveness and financial performance. Corporate social responsibility (CSR) and the environment have become a critical tool for firms to pursue their ethical behaviour, build stakeholders' trust, and improve long-term competitiveness (Li et al., 2020). Transparency and visibility are crucial given the high profile of the sector in an industry with complex supply chains and where the consumer is demanding more. FMCGB companies like Tesco, Sainsbury’s, Morrisons, and ASDA have developed exhaustive sustainability programs and ESG Disclosure policies to live up to their growing stakeholder's expectations and their appetite for regulation in this domain. 5 Supermarkets are now more and more pressured to reduce their environmental impact and to undertake corporate social responsibility, and to do so in such a way that it is honest and convincing with regard to investors, regulatory agencies and customers (Megale, 2020). While the literature has widely investigated CSR and ESG, and its impact on firm performance, very few studies have focused on the effects of such disclosures on the performance of UK supermarket companies. Differences in the quality and strategy of the disclosure have called into question the way they actually influence the economic and financial aspects, the operational risk, customers' loyalty, and the brand image. The motivation for this study lies in the growing significance of CSR and ESG disclosures in driving firm performance. In the UK supermarket industry, where competition and consumer expectations are high, companies such as Tesco, ASDA, Morrisons, and Sainsbury’s are increasingly judged on their sustainability practices. Understanding how CSR reporting influences brand image, customer loyalty, and long-term competitiveness is essential. This study is therefore motivated by the need to provide practical insights on how supermarkets can strategically use sustainability disclosures to build trust and create lasting value. 1.1 Research Questions • What is the extent and quality of CSR and ESG disclosure of selected companies in the UK supermarket industry?” • How consistent are the CSR and ESG disclosure practices across Tesco, ASDA, Morrisons, and Sainsbury’s?” 1.2 Research Aim • To evaluate the extent and quality of CSR and ESG disclosures of Tesco, ASDA, Morrisons, and Sainsbury’s. • To compare and analyze the differences and consistencies in CSR and ESG reporting practices among the selected UK supermarkets. • To assess how CSR and ESG disclosures contribute to stakeholder trust, brand image, and overall firm performance. 6 2. Literature Review This literature review explores the theoretical basis and up-to-date research addressing the CSR disclosure practices. This chapter starts with the pyramid of CSR and integration of the institutional-stakeholder theory as proposed by Carroll, where organizations strike a balance between conflicting duties and stresses. Then it examines the communication strategy that drives the degree of stakeholder engagement, beginning with one-way information delivery and ending with collaborative stakeholder engagement. 2.1 Theoretical Foundations of CSR Disclosure The emergence of CSR disclosure by corporations has transformed what was initially an optional practice of charity reporting into a systematic process, which implies building a strong theoretical base to understand the underlying causes as well as the differences in the actual practice of CSR reporting. The pyramidal structure of CSR, as described by Carroll (1991), helps us to establish the necessary framework in which corporations can attain the highest level of responsibility to their stakeholders. This paradigm concludes that economic profitability has to be the baseline, although businesses increasingly have to take care of legal aspects, ethical issues, and philanthropic endeavors. The model has been used worldwide regardless of economic context, but its hierarchical nature simplifies the interrelationship dynamics of the responsibility dimensions in the modern business world. Herold (2018) builds on the work conducted by Carroll and provides a theoretical explanation of the differences in sustainability reporting practices by incorporating institutional theory and stakeholder theory over the traditional explanations offered by institutional theory. Institutional theory helps to explain why firms in an industry will tend to gravitate towards a common disclosure form and, indeed, institutionalize it in their reporting standards and formats, as a form of field-level isomorphism. Stakeholder theory also holds that managers consider the salience of various audiences in the design of the disclosure planning and the powerful focus of management content. Herold critically identifies that the theories work at different levels of analysis, such that institutional forces cause convergence at the sector levels, whereas stakeholder considerations cause differentiation at firm levels in terms of the depth and focus of disclosures. 7 2.2 CSR Communication Strategies The strategic aspect of CSR disclosure is related to the effectiveness of communication and engagement strategies with stakeholders. Morsing and Schultz (2006) offer a theoretical basis that defines three different CSR communication approaches that permit a growing depth of stakeholder participation. The stakeholder information strategy is one way companies communicate their level of achievement and actions without consulting stakeholders. The twoway communications feature of stakeholder response strategy is managed by the companies, which solicit stakeholder opinions on the company's CSR performance and priorities. The most advanced approach, one of the stakeholder involvement strategies, emphasizes the joint development of a CSR initiative through continuous dialogue and partnership building. Such communication efforts have significant consequences for credibility and trust-building, especially at a time when stakeholder expectations regarding authentic engagement have been increasing. The option of information sharing, consultative dialogue, and participatory engagement will indicate the maturity of the organizations in integrating CSR and their commitment to a more profound sense of relationships with stakeholders, instead of compliance reporting. 2.3 Contemporary Research on Disclosure Quality Literature overview of recent studies indicates more systematic trends on factors explaining the quality of CSR disclosures in various situations. In one study, Abdul Latif et al. (2023) examine plantation companies in Malaysia, finding that factors associated with internal governance also have a significant role in the determination of disclosure sophistication. Their study finds board composition and specifically gender diversity, as well as the presence of a sustainability committee, to be key factors in reporting quality. The analysis finds a positive relationship between family ownership and disclosure, and that concentrated ownership of CEOs hinders disclosure. The results indicate that the governance structure is more important than external compliance requirements after the basis reporting standards are set in place. In Dincer and Dincer (2024), a systematic review of 242 publications on the topic of qualitative sustainability reporting was conducted, and an in-depth examination of the theoretical 8 perspectives underlying these studies was undertaken. They found that legitimacy theory and stakeholder theory are already the most common modes of inquiry in academic circles, neglecting moral legitimacy and critical orientations. Eco-friendly issues are often given undue emphasis in studies at the expense of social aspects, such as employee relations, product responsibility, and community engagement. Such a trend shows a difference in measurement standardization, with environmental measures having more commonly agreed-on frameworks, whereas social measures have no consensus. 2.4 Sector-Specific Applications and Challenges The retail industry poses distinct issues of CSR disclosure in terms of a complicated supply chain and consumer contact. Pimentel et al. (2022) analyze disclosure practices in the context of food waste reduction among retailers and find 44 factors affecting how disclosure is approached. Their study shows that the pattern of disclosures on a macro-scale is shaped by the pressure of the regulator, the integration of new technologies, and the work with the customers in educating them, whereas collaboration with suppliers and the flexibility of their contracts shape the level of disclosures on an operational level. This piece of work demonstrates that sector-related risks and stakeholders' requirements determine disclosure agendas, whereby the existence of disclosure gaps regarding operational enactment is always present. These retail results can supplement theoretical models by demonstrating the nature of the complex interaction (of institutional pressures such as regulation and technology standards, with stakeholder demands such as customer education and supplier relationships) which leads to the development of alternative disclosure strategies. Firms will deal with the various requirements posed by the audience on the one hand and the sector-related challenges, which in some instances may be inconsistent with the reporting frameworks. 2.5 Research Gaps and Implications This literature identifies various key gaps that need to be filled with additional research. First, there is a relative lack of studies that establish the connection between disclosure quality and operational performance, with few longitudinal investigations to show continuity between reported commitments and practices on the ground. Second, the institutional and stakeholder 9 theories can be helpful, but their combination in explaining firm-level variation needs more advanced development of theory. Third, sector-specific variables that can be identified in a retail food waste study show that comprehensive frameworks might not provide an adequate measure of industry-specific CSR issues and challenges. The theoretical abstractions that correspond to the pyramid of CSR that was proposed by Carroll, with additions by Herold because of the institutional-stakeholder integration and subsequent developments, with the currently seen in the practices of CSR disclosures through the lenses of the communication strategies that Morsing and Schultz advance are essential in providing the necessary grounding within which the current CSR disclosure processes in today are occurring. Nevertheless, the evidence provided by the governance studies conducted by Abdul Latif et al. and the theoretical analysis provided by Dincer and Dincer shows that the disclosure quality is predetermined by the interactions between the internal organizational factors, external institutional forces, and the specific stakeholders' requirements that are applicable to the particular sector. Future research should therefore come up with more refined theoretical explanations that can capture these multi-level interactions in addressing the continued gap that exists between disclosure content and the operational reality. The sectoral study by Flores Pimentel et al emphasizes the need to investigate the sectors separately since stakeholder configuration and regulations may vary across sectors and lead to different findings. Further, research studies comparing the sectors can help to come up with a theoretical explanation of CSR disclosure effectiveness and its authenticity. 10 3. Methodology 3.1 Prepare the Data Data Collection Process Data Collection Process is the systematic method of gathering relevant information from various sources to ensure accurate and reliable analysis (Taherdoost, 2021). Data collection methods and tools for research; a step-by-step guide to choose data collection technique for academic and business research projects. The report on Sustainability was systematically gathered in four major supermarkets of the UK, namely: Tesco, ASDA, Morrisons, and Sainsbury's. The data contains the reports of the period 2022-2024, including a total of ten documents over the duration of the study. All the reports were retrieved on official corporate web pages, making sure that the source materials are authentic and complete. Data Format and Organization Data Format and Organization is the structured arrangement of data into clear, consistent categories or formats, ensuring easy access, analysis, and reliable interpretation (Kim, 2020). All the reports were available in PDF format or had been converted to a searchable format for systematic study. All documents were marked with the name of a company, year of reporting, and the type of documents to ensure they were followed throughout the coding procedure. A master database was developed which relates each report to that report's associated metadata, such as publication date, number of pages, and compliance to reporting framework (GRI, TCFD, SASB). Efficient retrieval and cross-references could be released to use this structure of organization in the analytical process. 3.2 Define the Unit of Analysis Primary Unit of Analysis The unit of analysis was illustrated as a particular CSR disclosure statement in each report and was a specific claim, measure, or commitment of sustainability performance. All the disclosure reports are coherent pieces of information that address a particular social responsibility issue in 11 the corporations. The units were constrained by topic coherence and consistency between the thematic contents of reporting sections, such that there were well-founded analytical delimitations. Unit boundaries and scope Minimum unit size was determined by a single complete sentence making a substantive claim of CSR. In contrast, a larger maximum size consisted of contiguous paragraphs of a single topic of CSR without crossing thematic boundaries. The redundancy of statements in different parts was coded as different units in order to reflect the emphases and communication patterns. This reduced the amount of information that was covered and enabled analytical clarity without false chopping up of sense. Unit Categorization Framework Units were initially categorized into six major domains based on established CSR frameworks. Based on the coded dataset, the distribution shows: Table 1: Unit Categorization Framework Category Environmental (ENV) Description Climate, resources, sustainability People, community, stakeholders Leadership, oversight, compliance Subcategories ENV-1 to ENV7 Performance (PERF) Metrics, progress, benchmarking PERF-1 to PERF-6 Communication (COMM) Reporting, messaging, integration COMM-1 to COMM-4 Social (SOC) Governance (GOV) SOC-1 to SOC6 GOV-1 to GOV-6 12 Sample Coverage Carbon emissions, Energy, Waste, Sourcing, Water, Packaging, Supply chain Employees, Health & safety, Community, Customer health, Human rights, Local sourcing Board composition, Executive compensation, Risk management, Stakeholder engagement, Transparency, Ethics KPIs, Progress tracking, Certifications, Benchmarking, Future commitments, Investment Presentation, Stakeholder messaging, Business integration, Digital innovation Each unit received unique identifiers linking to source document, page number, and section location for traceability throughout the analytical process. 3.3 Develop Categories and Coding Scheme Deductive Framework Development Deductive Framework Development is building a coding scheme based on established theories and literature to guide data interpretation (Fife & Gossner, 2024). The deductive approach was taken to develop the coding scheme using the recognized literature base of CSR reporting frameworks, mainly the GRI Standards, the SASB frameworks, and the academic literature of CSR. This theoretical approach has guaranteed consistency with internationally renowned principles of sustainability reporting. The six broad categories were defined: Environmental (ENV), Social (SOC), Governance (GOV), Performance (PERF), Communication (COMM), and a more specific subdivision in each of the above-mentioned categories. Category Definitions and Structure Environmental category encompasses seven subcategories (ENV-1 to ENV-7) covering carbon emissions, energy use, waste management, sustainable sourcing, water conservation, packaging, and supply chain impacts. Social category includes six areas (SOC-1 to SOC-6) addressing employee relations, health and safety, community investment, customer health, human rights, and local sourcing. Governance category covers six dimensions (GOV-1 to GOV-6) including board composition, executive compensation, risk management, stakeholder engagement, transparency, and ethics. Performance and Communication categories each contain specific measurement and reporting elements. Quality Assessment Scale and Dimensions A four-point quality scale was developed with specific operational definitions based on the actual disclosure patterns observed: Table 2: Quality Assessment Scale and Dimensions Quality Level 1 - Minimal Definition Basic statements without detail Coding Criteria General commitments, no specific metrics or context 13 2 - Basic Some detail provided 3 - Good 4-Comprehensive Comprehensive information Detailed, quantified, contextualized Limited quantification, basic context provided Detailed metrics with contextual information Full metrics, targets, methodologies, thirdparty validation Time dimension coding captured temporal orientation: C=Current data (present period reporting), R=Recent data (historical reference points), F=Forward-looking commitments (future targets and commitments). Quantitative presence was measured on a four-point scale: 0=No quantitative data, 1=Some quantitative elements, 2=Substantial quantitative data, 3=Comprehensive quantitative reporting with benchmarks and targets. 3.4 Ethical Considerations This study will maintain ethical considerations in all phases of the research. The risk of harm is minimal due to the nature of the research, which is a secondary analysis of publicly accessible corporate reports. Nonetheless, I intend to cite all my sources and report the data in good faith to uphold the academic honour code. They will not include personally identifiable information or sensitive information, and only publicly accessible corporate sustainability reports will be used. Transparent inclusion criteria and open access sources adhere to the principles of fairness, replicability, and accountability (Lynch et al., 2020) 14 4. Analysis 4.1 Preliminary Quantitative Overview The content analysis considered 298 independent CSR disclosure statements in 10 reports of 2022-2024. Distribution across the four companies included Tesco with 89 units (29.8%), Sainsbury's with 78 units (26.2%), ASDA with 67 units (22.5%), and Morrisons with 64 units (21.5%). The coverage was primarily Environmental (35%), Social (28%), Performance (18%), Governance (12%), and Communication (7%). Quantitative data was found in 67% of disclosures, with the environmental category ranked high in its ability to quantify at 84 percent, and the Governance category at a low 43 percent Commitments made into the future made up 45 percent of the temporal orientations, current statistics 38 percent, and past references 17 percent. The rate of target-setting rose by 32 percent between 2022 and 2023 among all firms. The scores of the quality ranged between 1.8 and 4.0, whereas the industry average reflected an increase of 2.8 (2022) to 3.4 (2023). Third-party frameworks compliance refers to: GRI (67% of reports), TCFD (58%), and SASB (25%). The quantitative patterns form the baseline of the further qualitative analysis of positioning and disclosure sophistication. 4.2 Analytical Framework Validation Testing the Coding Scheme on Tesco (Sample 2022 Extract) Table 3: Coding Scheme Validation - Tesco Sample Extract Text Extract ESG Code "Tesco aims to achieve carbon neutrality in its Group ENV1 Category operations by 2035, aligned with a 1.5?C pathway for Scope 1 climate targets and 2 emissions. The target is to reduce absolute carbon emissions by 60% by 2025 compared to a 2015 baseline." 15 Carbon emissions & "Tesco switched to 100% renewable electricity in its ENV2 Energy use & operations by 2030, achieving this goal 10 years ahead of renewable transition schedule." "Tesco donated £93 million in the UK and €6 million in SOC3 Community Ireland to local community projects and charities." engagement & charitable giving "From 2022, Tesco includes sustainability targets in its GOV2 Executive Performance Share Plan, with 25% of awards linked to carbon compensation reduction and food waste reduction." to ESG "Tesco achieved a 52% reduction in Scope 1 and 2 emissions PERF2 Progress vs targets linked compared to a 2015 baseline." The Tesco validation confirms the coding scheme's methodological rigor. Each disclosure mapped to exactly one subcategory without overlap, demonstrating mutual exclusivity. All content proved codable, ensuring exhaustive coverage. The structured definitions enable consistent inter-coder reliability standardized examples like "£93m community donations" reliably align with SOC3, while emissions targets consistently fall under ENV1, validating the framework's practical applicability. Reliability Statistics This IBM SPSS reliability analysis evaluated the internal consistency of a two-item scale (Quality and Quant) using 298 valid cases with complete data. Table 4: Reliability and Scale Statistics Measure Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items Scale Mean Scale Variance Scale Std. Deviation 16 Value .952 .960 2 5.56 4.335 2.082 Table 4 demonstrates exceptional reliability with Cronbach's Alpha of .952 (.960 standardized), far exceeding the .70 acceptability threshold. The overall scale has a mean of 5.56 with standard deviation of 2.082, indicating good variability across the two-item measure. Table 5: Item Analysis and Correlations Item Mean Std. Deviation N Inter-Item Correlation Quality 3.35 .963 298 .924 Quant 2.21 1.159 298 .924 Table 5 reveals Quality (M=3.35, SD=.963) and Quant (M=2.21, SD=1.159) show strong interitem correlation of .924, confirming they measure closely related constructs. Quality demonstrates less variability than Quant, but both items contribute meaningfully to the scale. The high correlation supports uni-dimensionality, while the excellent Cronbach's Alpha establishes this two-item scale as highly reliable for research applications. 4.3 Overall CSR Performance Table 6: CSR Performance by Company and Year Company 2022 Average Quality Tesco 2.4 ASDA 3.2 Morrisons 3.1 (2022/23) Sainsbury's 3.4 (2022/23) 2023 Average Quality 3.0 3.6 3.3 (2023/24) 3.7 (2023/24) 2024 Average Quality 3.6 3.9 (2024/25) Improvement +1.2 +0.4 +0.2 +0.5 Sainsbury's Maintains Leadership with Accelerating Performance Sainsbury's demonstrates consistent leadership throughout the period, maintaining the highest CSR disclosure quality across all years (3.4 ? 3.7 ? 3.9). Their steady improvement trajectory shows sustained excellence, reaching near-optimal disclosure quality by 2024/25. This suggests established CSR practices being continuously refined rather than reactive improvements. 17 Tesco Shows Remarkable Transformation Tesco demonstrates the most dramatic improvement, starting from the lowest position in 2022 (2.4 average quality) but showing consistent year-on-year growth, reaching 3.6 in 2024. This represents a 50% improvement over three years, suggesting a major strategic shift toward comprehensive CSR disclosure and potentially positioning themselves as a sustainability leader. 4.4 CSR Performance by Category (2022-2024 Average) Table 7: CSR Performance by Category (2022-2024 Average) Category Environmental Social Governance Performance Communication Tesco 3.2 2.8 2.6 3.1 2.7 ASDA 3.6 3.4 2.8 3.7 3.2 Morrisons 3.9 3.5 2.8 3.7 2.7 Sainsbury's 4.0 3.2 3.2 3.9 3.2 Categ

Description ASSIGNMENT#1 HEA 610 Overview Higher education is at a crossroads. There are tremendous forces of change that are pulling at the heartstrings of how higher education works, who has access to it, who pays for it, and how it is valued in the workplace. The forces for change can come from both the external and internal environments. As Bontrager, Ingersoll, and Ingersoll explain in Strategic Enrollment Management, “Because institutions react to these forces, it is critical to have a firm grasp on the nature of the forces in order to make appropriate adaptations.” This paper will examine these forces, as defined in policy issues paper by the American First Policy Institute, with an emphasis on what these changes mean at the campus level. Nearly all of these policy issues impact the strategic enrollment plan. Prompt Submit a short paper on higher education policy. Review the 25 State Higher Education Policy Priorities of The America First Agenda. Select one policy issue and do additional research to find at least two articles from publications that focus on higher education and explore how the institutions are addressing the issue. Specifically, the following critical elements must be addressed: Current Issue. Summarize the policy issue facing colleges and universities. Reflect on why this issue is important in the broader context of education policy. Impact on Institution. Explain how this issue impacts your chosen institution, with special consideration of the impact on strategic enrollment planning and resources. Implications for the Future. Describe how this issue has implications across your institution. How do you see your institution (and particularly enrollment management) being changed by this issue in 10 or 20 years? What to Submit This short paper should follow these formatting guidelines: 3–5 pages in length with double spacing, 12-point Times New Roman font, one-inch margins, and APA-style citations. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!aSSIGNMENT #2 HEA 540 WEEK 7 While this module’s discussion is about how to communicate information about evaluating recommendations, this third milestone is about how to get the evaluation done. Prompt In this milestone, you will determine a plan for evaluating the success of your recommendations once they are implemented for the program you chose in Milestone One. This evaluation plan will include a process for measuring success, including a discussion of how the recommendations will foster continuous improvement and an explanation, with specific examples, of how you would mitigate institutional challenges. Specifically, the following critical elements must be addressed: Outline the process by which you will measure the success of your recommendations. In other words, what are the steps you will need to take to measure the success of your recommendations, and what further information or indicators will you need to find and verify? Be sure to include appropriate visuals (e.g. flowcharts, pie charts) to illustrate the time sequence. Discuss how the evaluation of your recommendations will foster continuous improvement within the academic program. What ongoing processes will be in place? How will adjustments be identified and made? Explain how these continuous improvement processes address institutional challenges. How do they respond to the ever-changing academic environment? Use specific examples to support your explanation. What to Submit Your paper should be submitted as a Microsoft Word document of at least 5 pages (including title and reference pages) with headings, double spacing, 12-point Times New Roman font, one-inch margins, and citations in APA format.

Description Order Number: S82-112709 subject?IBUS6000 International Engagement Project Assessment 2-Project Analysis 2000words The writing requirements for this assignment should be fulfilled in accordance with the general framework explained in class. Draft Due:(2025-12-02) Final Version Due (writer):(2025-12-02) University Submission Deadline:(2025-12-03) 2 attachments Slide 1 of 2 attachment_1 attachment_1 attachment_2 attachment_2 UNFORMATTED ATTACHMENT PREVIEW DISEASE FIGHT ????? 1424 India Kuppen. ? s ? Four Major Benefits 1. Increased employee satisfaction 2. Improved public image 3. Increased customer loyalty 4. Increased creativity Employee Satisfaction Recess Scho TURNITIN DETECT AI 5202 ?INTERVIEW QUAS. FILL UP AI FORM. 716746 USAFIO b TIRTAK F 1ST METHOD. NEVER USE AI: XOMARK E TEACHER LIESA) You SPUDERY Cand Fa SIT CLASSROOM. NO HAMPHIE 2MP METH WRITE ESTAY. 4t 2nd 31 4th th -54-103 -10% 105 1osEPSON Context-complementary perspectives (2) Many MNEs/NGOs assess supplier compliance with codes of conduct on working conditions, but do not measure real workplace conditions Comparison of two Mexican firms subject to Nike monitoring, but very different in working conditions Nike developed the M-Audit, consolidating into a single score the performance on more than 80 items (hiring practices, worker treatment, worker-management communications, and compensation) But: even composite measures of compliance do not lead to a complete understanding of differences in working conditions among supplier factories Copyright ©2021 Cambridge University Press Purchase answer to see full attachment

Description Choose any one of the Omani companies you are familiar with or known to you. You are required to analyse sustainable and ethical business operations by exploring how the chosen company address global challenges through responsible practices, sustainability innovations, and ethical supply chain strategies. Your assignment should demonstrate your understanding of sustainability, ethics, and their application in real-world business operations. Introduction: (300 words) Introduce the concept of sustainable and ethical business practices (with ‘in-text’ citation and corresponding reference list). Explain why these practices are important in today’s business environment. Briefly state what the assignment will cover and what should be learned from it. ----------------------------------------- Task 1: Analysis of Business Operations (1,200 words) Evaluate how businesses operate ethically and sustainably using these two perspectives: a. Corporate Sustainability Perspective Discuss how companies integrate sustainability into their core strategies (e.g., environmental protection, social responsibility, economic strategy). Include examples of sustainable business models (e.g., circular economy, carbon neutrality). b. Technological Advances for Performance Improvement Analyse how technology (e.g., AI, blockchain, green technology) helps improve business efficiency and sustainability. Discuss the potential risks or ethical concerns related to using these technologies. ------------------------------------------------------------ Task 2: Sustainable and Ethical Global Supply Chain (1,200 words ) Analyse the concepts of sustainable and ethical supply chain management (SCM). Explain how global businesses apply ethical standards in sourcing, manufacturing, and logistics. Provide examples of companies using ethical SCM practices (e.g., fair labour, sourcing transparency). Evaluate the impact of ethical SCM on stakeholders and brand reputation. ----------------------------------------- Conclusion (300 words) Summary of your main findings. Provide practical recommendations for businesses to improve their ethical and sustainable operations. -------------------------------- References: Provide a list of references in Harvard referencing style to support your analysis. Ensure that academic journals, books, and reputable sources are cited appropriately. “in-text’ citations should be consistent to the list of references. PROVIDE SIMILARITY AND PLAGIARISM REPORTS

Description I need a word document and a power point please. With the instructions mentioned below. UNFORMATTED ATTACHMENT PREVIEW Group Project (20 marks) Deadline December 6, 2025 In this project, you are going to work in a group of (2-3 students). Write a paper using the blogs from the Program on Negotiations by the Harvard Law School at https://www.pon.harvard.edu/, and will present for 10 minutes about them in class August 6. After going to the PON website, access the “Daily Blogs” at https://www.pon.harvard.edu/blog/ Read 10 blogs and provide your reflection and interpretation for each blog that you read. Organize with other groups to be sure there is no duplication in article selection. The title of the blog, author, date and website link must be present at the beginning of each blog reflection. Assignment specifications: - Submission is through blackboard as a soft copy. - Use APA writing style. - The paper should be not less than 1000 words/ PowerPoint slides flexible length. - There is no need to write references in the end, as your reference is already known and you mentioned the blog information at the beginning of your interpretation. - Your paper should reflect your readings of 10 blogs from the PON blog. - Do not copy and paste, rely on your paraphrasing 100%. - Plagiarism % should not exceed 10%. - Deadline for the assignment submission is December 6, 2025 before midnight. - Late submissions receive a ZERO. - Presentations will be held in class august 6, all group members should present, and duration of presentation should be no less than 10 minutes. - Grading will be 10 marks for the paper, and 10 marks for the presentation. Grading Assessment: A. Report [10 marks]: 1- APA style, overall organization & length of the document. (3) 2- Quality of information. (7) B. Presentation [10 marks]: 1- Eye contact with audience, loud and clear voice and readiness and time management (2). 2- Power point slides organization (2). 3- Knowledge about the topic, fluency of thought and quality of information (2). 4- Slides quality (design and content) (4