What is the primary purpose of marketing?

To create value through exchange processes

To operate within financial constraints

What are the four main marketing orientations?

Production, Product, Selling, and Marketing Orientation

Advertising, Selling, Market Research, and Distribution

Financial, Human Resources, Operations, and Marketing

Innovative, Customer Focused, Data Driven, and Environmental

What is relationship marketing primarily focused on?

Building long-term customer relationships

What is strategic marketing management primarily concerned with?

Aligning marketing strategies with business objectives

Conducting market research only

What best describes the marketing process?

A series of steps that includes planning, executing, and evaluating marketing strategies

A one-time effort to sell a product

A strictly financial analysis of market trends

What is the knowledge gap in marketing?

The difference between customer expectations and management's perception of those expectations

The gap in production capabilities

The difference between advertising reach and customer awareness

The gap between sales targets and actual sales

What is considered a key element of the marketing mix?

Product, Price, Place, and Promotion

Market, Message, Media, and Measurement

Research, Resources, Reach, and Recognition

Profit, Process, People, and Partnership

What does the concept of exchange in marketing refer to?

The process of obtaining a desired product or service by offering something in return

The negotiation of prices with suppliers

The introduction of new products to the market

What is the marketing process?

The steps that involve planning, executing, and evaluating marketing strategies

A one-time event focused only on product launch

A series of random advertising campaigns without direction

A method of setting prices based solely on production costs

What is one of the key management tasks in marketing?

Conducting market research to understand customer needs

Enforcing strict product pricing policies

Overseeing production line efficiency

Limiting marketing budgets to save costs

Define what marketing is; · Explain the concept of exchange; · Explain the marketing gaps that exist; · Describe the marketing activities; · Discuss the four main marketing orientations; · Discuss the importance of relationship marketing; · Explain the marketing process; Theme 2: The Marketing functions in an organisation · Discuss the marketing function in an organisation; · Describe the various other functions within an organisation; · Explain the management tasks in marketing; Theme 3: Strategic Marketing Management · Explain the concept of strategic marketing management.

Why and How Managers Plan Importance of planning The planing process Benefits of planning Planning and time management Types of PLans used by managers Long term and short term plans Strageic and tactical plans Operational plans Planning Tools and Techiqunes Forecasting Contrigency planning Scenario planning Benchmaking Use of staff planners Implementing Plans to Achive Results Goal setting Goal management Goal alignment Participation and involvement Planning Def: The process of setting objectives and determining how best to accomplish them Planning at Eaton Corporation “Making the hard decision before events force them upon you, an anticipating the future needs of the market before the demand asset itself Objectives and goals Identifity the specific results or desired outcomes that one intends to achieve Plan Def: A statement of action steps to be taken in order to accomplish the objectives (goals) Steps in the planning process: Define your objectives Determine where you stand vis-a-vis objectives Develpo premises reagrdsing future conditions Analyze alternatives and make a plan Implement the plan and evaluate results What are the benefits of planning Improves focus and flexibility Imporves action orteitation Imporves coordination and control Imporves time management Time Managment Personal time management tips Do say “no” to request that distract you form what you should be doing Dont get bogged down inn details that can be addressed later Do screen telephone calls, emails and meeting request Dont let drop in visitors, text messaging use up your time Do prioritize your important and urgent work Dont become calendar bound by letting other control your schedule Do follow priorities; do most important and urgent work first Some 77% of mangers in one survey said that digital age has increased th number of decisions they have to make 43% said there was less time available to make these decisions Types of plans used by Managers What is teh time horizon Long term vs Short term Long term Look three or more years into teh future Short term plans Typically cover one year or less However: the increasing environmental complexity and dynamism of recent years has severely tested the concept of “long-term” planning Plans are subject to frequent revisions Most executives would likely agree that these complexities adn uncertainties challenge how er actually go about planning and how far ahead we can really plan At the very least we can conclude that there is a lot less permanency to long term plans today and that tey are subject to frequent revision Managment reaeracher Eillot Jaques believes tha people vary in their capability to think with different time horizons Types of Plans used by Managers (3 of 5) Strategic plans Set broad, comprehensive and linger term action directions for teh entire organization or major division Vision Clarifies purpose of the organization and what it hopes to be on the future Typical plans Specify how the organizations resources are used to implement strategy Tactical plans in business often take the form of functional plans Functional plans Incidate how different component within the organiztion will help accompnlish the overall strategy Production plans Finacial plans Facilites Plans Logisitc plans Marketing plans Human Resource Plans Operation plans Describe short-term activities to implement strategic plans Policies: Are standing plans that communicate guidelines for decisions Ex: Policies on office romances: The media is quick to report when a top executive or public figures runs into trouble over an office affair. Are there ant policies on office romances? Employer polices on office raltioshiis vary. One survey find teh following: 24% prohibit relationships among employees in the same department 13% prohibit relationships among employees who have the smae supervisor 80% prohibit relationships between supervisors and subordinates 5% have no restrictions on office romances Procedures: Are rules that describe actions to be taken in specific situations Budgets: are single use plans that commit resources to projects or activities Zero based budgets: allocate resources as if each budget were brand new There is no guarantee that any past funding will be renwer. All propsales, old and new, must compete for available funds at teh start of each new budget cycle Forcasting Attempts to predict the future Qualitaive forecasting uses expert opinions Quantitative forecasting uses mathematical models and statiscal aanylsis of historical data dna surveys Contingency planning Identify alternative course of action to take when things go wrong Anticipate changing conditions Contain trigger points to indicate when to activate plan (or a specific course of action) Scenario planning A long term version of contingency planning Identifying alternative future scenarios Plans made for each future scenario Increases organizations flexibility and preparation for future shocks Benchmarking Use of external and internal comparisons to better evaluate current performance Adopting best practices: things people adn organization do that lead to superior performance Staff Planners Experts who assist in all steps of the planning process They help bring focus and expertise to a wide variety of planning tasks Important: Communication between staff planers landline managers is essential for teh success of teh planning process Goal Setting - Always set SMART goal The solution: Goal Aligment Between Team Leader and Team Member Jonintly plan: Set objectives, set standards, choose actions Individually acy: Perform tasks (member), provide support (leader) Jointly control: Review results, discuss implications, renew cycle x4 Collective effort and commitment Participatroy planning Includes in all planning steps that people who will be affected by the plans adn askedd to help implement them Unloacks motivational potential of goal setting Management by objective (MBO) promotes participation Participation increases understanding and acceptance of plan and commitment to success Participatory planning - Number of people involved in teh decision making process Amazon is intensely focused on what it does. It believes in creating tight single-threaded teams, also known as “2 pizza team.” Data and Decision Making What are some of the important competencies managers must have today? Delegate Marketing and technology Manager must have Technological competency Ability to understand new technologies and to use them to their best advantage Information competency Ability to locate, gather, organize and display information for decision-making and problem solving Analytical competency Ability to evaluate and analyze information to make actual decisions and solve real problems What is the difference between Data and Information Data Raw facts and observation Information Data made useful and meaningful for decision-making Important concepts Big data Exists in huge quantities and is difficult to process without sophisticated mathematical and analytical techniques Data production today Bernard Marr is an internationally best-selling author. He helps organizations improve their business performance, use data more intelligently Data mining The process of analyzing data to produce useful information for decision-makers Management Analytics The systematic evaluation and analysis of data to make informed decision Information drives management Bad Data Refers to information that can be erroneous, misleading, and without general formatting The challenge: Can er use the data that is available in the “Big Data” Needs to be valid Can not trust everything out there Being ethical Look at the trends Data is structured and unstructured Data BIg Data = Structured + Unstructured Information Drive Management decision making What are the characteristics of useful information Easy to access If its credible Accurate Characteristics of useful information: Timely High quality Complete Relevant Understandable What about bad data It's not credible Miss information If it is not structured/ organized Bias based on opinions Confusing If its updated Bad data Refers to information that can be erroneous miss What are some examples of Management information system Business intelligence -BI Information systems to extract and report data in organized ways that are useful to decision-makers Executive dashboards Visually update and display key performance metrics (or Key Performance Indicators -KPIs) and information on a real-time basis Information needs in organization External Environment Information exchanges with the external environment Gather intelligence information Provide public information Information needs within the organizations (internal Enviroement) Information exchange within the organization Facilitate decision making Facilitate problem-solving Managers as information processors Continually gather, share and receive information Now as much electronic as it is face-to-face Always on, always connected How many people telecommute at least once a week 70% of people globally work remotely at least once a week, Work at home after covid 19 our forecast Our best estimate it that 25-30% of the workforce will be working form home multiple days a week by the end of 2021 As of 2023, 12.7% of full time employees work from home, while 28.2% work a hybrid model Managers as problem solvers Problem-solving The process of identifying a discrepancy between actual and desired performance and taking action to resolve it Ishikawa Fishbone diagram To identify the cause of problems Decision A choice among possible alternative courses of action Performance threat Something is wrong or has the potential to go wrong Performance opportunity The situation offers the chance for a better future if the right steps are taken Problem-solving approaches or style - from textbook Problem avoiders Inactive in information gathering and solving problems Problem seekers Proactive in anticipation of problems and opportunities and taking appropriate action to gain an advantage Problem solvers Reactive in gathering information and solving problem Managers - can approach problems in a systematic or intuitive manner Systematic thinking approaches problem in rational, step-by-step and analytical fashion Intuitive thinking approaches problems in a flexible and spontaneous fashion Multidimensional thinking- applies both intuitive and systematic thinking Managers face structured and unstructured problems Structure problems Are ones that are familiar, straight forward, and clear with respect to information needs Program decisions apply solutions that are readily available from past experiences to solve structured problems Know how to solve them Familiar Know what we are dealing with Unstructured problems Are ones that are full of ambiguities and information deficiencies Nonprogrammed decisions apply a specific solution to meet the demands of a unique problem Commonly faced by higher-level management Crisis decision making A crisis involves an unexpected problem that can lead to disaster if not resolved quickly and appropriately Ruled for crisis management Figure out what is going on Remember that speed matters Remember that slow counts, too Respect the danger of the unfamiliar Value the skeptic Be ready to “fight fire with fire” Managers make decisions with various amounts of information Certain environment Offers complete information on possible action alternatives and their consequences Risk environment Lacks complete information but offers probabilities of the likely outcomes for possible action alternatives Uncertain environment Lacks so much information that it is difficult to assign probabilities to the likely outcomes of alternative Ex: Certain and uncertain environments: The worldwide Governance Indicators for over 200 countries, comparing distinct environments (Canada-Brazil) Step 1-Identify and define the problem Focuses on information gathering information processing and deliberation Decision objectives should be established What are some common mistakes in definding problems? Common mistakes in defining problems Defining the problem too broadly or too narrowly Focusing on symptoms instead of causes Choosing the wrong problem to deal with Step 2- Generate and Evaluate Alternative Courses of Action Potential solutions are formulated and more information is gathered, data are analyzed, the advantages and disadvantages of alternative solutions are identified Common mistakes: Abandoning the search for alternatives too quickly Step 3- Decide on a preferred course of Action Two different approaches Behavioural model leads to satisficing decisions Classical model les to optimising decisions Behavioural Model Rationality is bounded because: There are limits our thinks capacity Available information (incomplete) Time constraints Step 4-Implement the decision Involves taking action to make sure the solution decided upon becomes a reality Managers need to have the willingness and ability to implement action plans Problems: Lack of participation error should be avoided Step 5 - Evaluate Results Involves comparing actual and desired results The positive and negative consequences of the chosen course of action should be examined If actual results fall short desire results, the manager returns to earlier steps in the decision-making process At all steps, check ethical reasoning Ask these spotlight questions Utility Does teh decision satisfy all constituents or stakeholders Rights Does the description respect the rights and duties of everyone? Justice Is the decision consistent with the canons of justice Caring Is the decision consistent with my responsibilities to care? Issues in decision-making How do errors happen? Heuristics: are strategies for simplifying decision-making Availability Bias: Bases a decision on recent information or events Representativeness bias: Bases a decision on similarity to other situations Anchoring and Adjustment Bias: Bases a decision on incremental adjustment from a prior decision point Framing error: Tring to solve a problem in the context perceived, positive or negative Confirmation Error: Focusing on information that confirms a decision already made Escalating commitment: Continuing a course of action even though it is not working Creative Decision making Creativity is the generation of a novel idea or unique approach that solves a problem or crafts an opportunity Big C: Creativity occurs when extraordinary things are done by exceptional people Little C: Creativity occurs when average people come up with unique ways to deal with daily events and situations The three types of situational creativity drivers Chapter review What are objectives and goals? The specific results or desired outcomes What are the 5 characteristics of great (SMART) goals? Forecasting - Attempts Qualitative forecasting uses options Quantitative forecasting uses mathematical models and statistical analysis of historical data and surveys Scenarios-Oracle’s crystal ball combines qualitative and quantitative methods

Define what recycling is and provide the answer

Define what a habitat is. Identify and describe the three main types of habitats: terrestrial, aquatic, and aerial. Give examples of plants and animals that live in each type of habitat. Understand the basic adaptations of organisms to their habitats.

“There’s No Such Thing as Sound Science” by By Christie Aschwanden was a lead science writer for FiveThirtyEight. FiveThirtyEight, Science, Dec. 6, 2017 Science is being turned against itself. For decades, its twin ideals of transparency and rigor have been weaponized by those who disagree with results produced by the scientific method. Under the Trump administration, that fight has ramped up again. In a move ostensibly meant to reduce conflicts of interest, Environmental Protection Agency Administrator Scott Pruitt has removed a number of scientists from advisory panels and replaced some of them with representatives from industries that the agency regulates. Like many in the Trump administration, Pruitt has also cast doubt on the reliability of climate science. For instance, in an interview with CNBC, Pruitt said that “measuring with precision human activity on the climate is something very challenging to do.” Similarly, Trump’s pick to head NASA, an agency that oversees a large portion the nation’s climate research, has insisted that research into human influence on climate lacks certainty, and he falsely claimed that “global temperatures stopped rising 10 years ago.” Kathleen Hartnett White, Trump’s nominee to head the White House Council on Environmental Quality, said in a Senate hearing last month that she thinks we “need to have more precise explanations of the human role and the natural role” in climate change. The same entreaties crop up again and again: We need to root out conflicts. We need more precise evidence. What makes these arguments so powerful is that they sound quite similar to the points raised by proponents of a very different call for change that’s coming from within science. This other movement strives to produce more robust, reproducible findings. Despite having dissimilar goals, the two forces espouse principles that look surprisingly alike: Science needs to be transparent. Results and methods should be openly shared so that outside researchers can independently reproduce and validate them. The methods used to collect and analyze data should be rigorous and clear, and conclusions must be supported by evidence. These are the arguments underlying an “open science” reform movement that was created, in part, as a response to a “reproducibility crisis” that has struck some fields of science.1 But they’re also used as talking points by politicians who are working to make it more difficult for the EPA and other federal agencies to use science in their regulatory decision-making, under the guise of basing policy on “sound science.” Science’s virtues are being wielded against it. What distinguishes the two calls for transparency is intent: Whereas the “open science” movement aims to make science more reliable, reproducible and robust, proponents of “sound science” have historically worked to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. “Our criticisms are founded in a confidence in science,” said Steven Goodman, co-director of the Meta-Research Innovation Center at Stanford and a proponent of open science. “That’s a fundamental difference — we’re critiquing science to make it better. Others are critiquing it to devalue the approach itself.” Calls to base public policy on “sound science” seem unassailable if you don’t know the term’s history. The phrase was adopted by the tobacco industry in the 1990s to counteract mounting evidence linking secondhand smoke to cancer. A 1992 Environmental Protection Agency report identified secondhand smoke as a human carcinogen, and Philip Morris responded by launching an initiative to promote what it called “sound science.” In an internal memo, Philip Morris vice president of corporate affairs Ellen Merlo wrote that the program was designed to “discredit the EPA report,” “prevent states and cities, as well as businesses from passing smoking bans” and “proactively” pass legislation to help their cause. The sound science tactic exploits a fundamental feature of the scientific process: Science does not produce absolute certainty. Contrary to how it’s sometimes represented to the public, science is not a magic wand that turns everything it touches to truth. Instead, it’s a process of uncertainty reduction, much like a game of 20 Questions. Any given study can rarely answer more than one question at a time, and each study usually raises a bunch of new questions in the process of answering old ones. “Science is a process rather than an answer,” said psychologist Alison Ledgerwood of the University of California, Davis. Every answer is provisional and subject to change in the face of new evidence. It’s not entirely correct to say that “this study proves this fact,” Ledgerwood said. “We should be talking instead about how science increases or decreases our confidence in something.” The tobacco industry’s brilliant tactic was to turn this baked-in uncertainty against the scientific enterprise itself. While insisting that they merely wanted to ensure that public policy was based on sound science, tobacco companies defined the term in a way that ensured that no science could ever be sound enough. The only sound science was certain science, which is an impossible standard to achieve. “Doubt is our product,” wrote one employee of the Brown & Williamson tobacco company in a 1969 internal memo. The note went on to say that doubt “is the best means of competing with the ‘body of fact’” and “establishing a controversy.” These strategies for undermining inconvenient science were so effective that they’ve served as a sort of playbook for industry interests ever since, said Stanford University science historian Robert Proctor. The sound science push is no longer just Philip Morris sowing doubt about the links between cigarettes and cancer. It’s also a 1998 action plan by the American Petroleum Institute, Chevron and Exxon Mobil to “install uncertainty” about the link between greenhouse gas emissions and climate change. It’s industry-funded groups’ late-1990s effort to question the science the EPA was using to set fine-particle-pollution air-quality standards that the industry didn’t want. And then there was the more recent effort by Dow Chemical to insist on more scientific certainty before banning a pesticide that the EPA’s scientists had deemed risky to children. Now comes a move by the Trump administration’s EPA to repeal a 2015 rule on wetlands protection by disregarding particular studies. (To name just a few examples.) Doubt merchants aren’t pushing for knowledge, they’re practicing what Proctor has dubbed “agnogenesis” — the intentional manufacture of ignorance. This ignorance isn’t simply the absence of knowing something; it’s a lack of comprehension deliberately created by agents who don’t want you to know, Proctor said.2 In the hands of doubt-makers, transparency becomes a rhetorical move. “It’s really difficult as a scientist or policy maker to make a stand against transparency and openness, because well, who would be against it?” said Karen Levy, researcher on information science at Cornell University. But at the same time, “you can couch everything in the language of transparency and it becomes a powerful weapon.” For instance, when the EPA was preparing to set new limits on particulate pollution in the 1990s, industry groups pushed back against the research and demanded access to primary data (including records that researchers had promised participants would remain confidential) and a reanalysis of the evidence. Their calls succeeded and a new analysis was performed. The reanalysis essentially confirmed the original conclusions, but the process of conducting it delayed the implementation of regulations and cost researchers time and money. Delay is a time-tested strategy. “Gridlock is the greatest friend a global warming skeptic has,” said Marc Morano, a prominent critic of global warming research and the executive director of ClimateDepot.com, in the documentary “Merchants of Doubt” (based on the book by the same name). Morano’s site is a project of the Committee for a Constructive Tomorrow, which has received funding from the oil and gas industry. “We’re the negative force. We’re just trying to stop stuff.” Some of these ploys are getting a fresh boost from Congress. The Data Quality Act (also known as the Information Quality Act) was reportedly written by an industry lobbyist and quietly passed as part of an appropriations bill in 2000. The rule mandates that federal agencies ensure the “quality, objectivity, utility, and integrity of information” that they disseminate, though it does little to define what these terms mean. The law also provides a mechanism for citizens and groups to challenge information that they deem inaccurate, including science that they disagree with. “It was passed in this very quiet way with no explicit debate about it — that should tell you a lot about the real goals,” Levy said. But what’s most telling about the Data Quality Act is how it’s been used, Levy said. A 2004 Washington Post analysis found that in the 20 months following its implementation, the act was repeatedly used by industry groups to push back against proposed regulations and bog down the decision-making process. Instead of deploying transparency as a fundamental principle that applies to all science, these interests have used transparency as a weapon to attack very particular findings that they would like to eradicate. Now Congress is considering another way to legislate how science is used. The Honest Act, a bill sponsored by Rep. Lamar Smith of Texas,3 is another example of what Levy calls a “Trojan horse” law that uses the language of transparency as a cover to achieve other political goals. Smith’s legislation would severely limit the kind of evidence the EPA could use for decision-making. Only studies whose raw data and computer codes were publicly available would be allowed for consideration. That might sound perfectly reasonable, and in many cases it is, Goodman said. But sometimes there are good reasons why researchers can’t conform to these rules, like when the data contains confidential or sensitive medical information.4 Critics, which include more than a dozen scientific organizations, argue that, in practice, the rules would prevent many studies from being considered in EPA reviews.5 It might seem like an easy task to sort good science from bad, but in reality it’s not so simple. “There’s a misplaced idea that we can definitively distinguish the good from the not-good science, but it’s all a matter of degree,” said Brian Nosek, executive director of the Center for Open Science. “There is no perfect study.” Requiring regulators to wait until they have (nonexistent) perfect evidence is essentially “a way of saying, ‘We don’t want to use evidence for our decision-making,’” Nosek said. Most scientific controversies aren’t about science at all, and once the sides are drawn, more data is unlikely to bring opponents into agreement. Michael Carolan, who researches the sociology of technology and scientific knowledge at Colorado State University, wrote in a 2008 paper about why objective knowledge is not enough to resolve environmental controversies. “While these controversies may appear on the surface to rest on disputed questions of fact, beneath often reside differing positions of value; values that can give shape to differing understandings of what ‘the facts’ are.” What’s needed in these cases isn’t more or better science, but mechanisms to bring those hidden values to the forefront of the discussion so that they can be debated transparently. “As long as we continue down this unabashedly naive road about what science is, and what it is capable of doing, we will continue to fail to reach any sort of meaningful consensus on these matters,” Carolan writes. The dispute over tobacco was never about the science of cigarettes’ link to cancer. It was about whether companies have the right to sell dangerous products and, if so, what obligations they have to the consumers who purchased them. Similarly, the debate over climate change isn’t about whether our planet is heating, but about how much responsibility each country and person bears for stopping it. While researching her book “Merchants of Doubt,” science historian Naomi Oreskes found that some of the same people who were defending the tobacco industry as scientific experts were also receiving industry money to deny the role of human activity in global warming. What these issues had in common, she realized, was that they all involved the need for government action. “None of this is about the science. All of this is a political debate about the role of government,” she said in the documentary. These controversies are really about values, not scientific facts, and acknowledging that would allow us to have more truthful and productive debates. What would that look like in practice? Instead of cherry-picking evidence to support a particular view (and insisting that the science points to a desired action), the various sides could lay out the values they are using to assess the evidence. For instance, in Europe, many decisions are guided by the precautionary principle — a system that values caution in the face of uncertainty and says that when the risks are unclear, it should be up to industries to show that their products and processes are not harmful, rather than requiring the government to prove that they are harmful before they can be regulated. By contrast, U.S. agencies tend to wait for strong evidence of harm before issuing regulations. Both approaches have critics, but the difference between them comes down to priorities: Is it better to exercise caution at the risk of burdening companies and perhaps the economy, or is it more important to avoid potential economic downsides even if it means that sometimes a harmful product or industrial process goes unregulated? In other words, under what circumstances do we agree to act on a risk? How certain do we need to be that the risk is real, and how many people would need to be at risk, and how costly is it to reduce that risk? Those are moral questions, not scientific ones, and openly discussing and identifying these kinds of judgment calls would lead to a more honest debate. Science matters, and we need to do it as rigorously as possible. But science can’t tell us how risky is too risky to allow products like cigarettes or potentially harmful pesticides to be sold — those are value judgements that only humans can make.

Understanding Quantum Theory of Electrons in Atoms The goal of this section is to understand the electron orbitals (location of electrons in atoms), their different energies, and other properties. The use of quantum theory provides the best understanding to these topics. This knowledge is a precursor to chemical bonding. As was described previously, electrons in atoms can exist only on discrete energy levels but not between them. It is said that the energy of an electron in an atom is quantized, that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels. The energy levels are labeled with an n value, where n = 1, 2, 3, …. Generally speaking, the energy of an electron in an atom is greater for greater values of n. This number, n, is referred to as the principal quantum number. The principal quantum number defines the location of the energy level. It is essentially the same concept as the n in the Bohr atom description. Another name for the principal quantum number is the shell number. The shells of an atom can be thought of concentric circles radiating out from the nucleus. The electrons that belong to a specific shell are most likely to be found within the corresponding circular area. The further we proceed from the nucleus, the higher the shell number, and so the higher the energy level (Figure 9.4.1). The positively charged protons in the nucleus stabilize the electronic orbitals by electrostatic attraction between the positive charges of the protons and the negative charges of the electrons. So the further away the electron is from the nucleus, the greater the energy it has. This quantum mechanical model for where electrons reside in an atom can be used to look at electronic transitions, the events when an electron moves from one energy level to another. If the transition is to a higher energy level, energy is absorbed, and the energy change has a positive value. To obtain the amount of energy necessary for the transition to a higher energy level, a photon is absorbed by the atom. A transition to a lower energy level involves a release of energy, and the energy change is negative. This process is accompanied by emission of a photon by the atom. The following equation summarizes these relationships and is based on the hydrogen atom: The values nf and ni are the final and initial energy states of the electron. The principal quantum number is one of three quantum numbers used to characterize an orbital. An atomic orbital, which is distinct from an orbit, is a general region in an atom within which an electron is most probable to reside. The quantum mechanical model specifies the probability of finding an electron in the three-dimensional space around the nucleus and is based on solutions of the Schrödinger equation. In addition, the principal quantum number defines the energy of an electron in a hydrogen or hydrogen-like atom or an ion (an atom or an ion with only one electron) and the general region in which discrete energy levels of electrons in a multi-electron atoms and ions are located. Another quantum number is l, the angular momentum quantum number. It is an integer that defines the shape of the orbital, and takes on the values, l = 0, 1, 2, …, n – 1. This means that an orbital with n = 1 can have only one value of l, l = 0, whereas n = 2 permits l = 0 and l = 1, and so on. The principal quantum number defines the general size and energy of the orbital. The l value specifies the shape of the orbital. Orbitals with the same value of l form a subshell. In addition, the greater the angular momentum quantum number, the greater is the angular momentum of an electron at this orbital. Orbitals with l = 0 are called s orbitals (or the s subshells). The value l = 1 corresponds to the p orbitals. For a given n, p orbitals constitute a p subshell (e.g., 3p if n = 3). The orbitals with l = 2 are called the d orbitals, followed by the f-, g-, and h-orbitals for l = 3, 4, 5, and there are higher values we will not consider. There are certain distances from the nucleus at which the probability density of finding an electron located at a particular orbital is zero. In other words, the value of the wavefunction ψ is zero at this distance for this orbital. Such a value of radius r is called a radial node. The number of radial nodes in an orbital is n – l – 1. Consider the examples in Figure 9.4.2. The orbitals depicted are of the s type, thus l = 0 for all of them. It can be seen from the graphs of the probability densities that there are 1 – 0 – 1 = 0 places where the density is zero (nodes) for 1s (n = 1), 2 – 0 – 1 = 1 node for 2s, and 3 – 0 – 1 = 2 nodes for the 3s orbitals. The s subshell electron density distribution is spherical and the p subshell has a dumbbell shape. The d and f orbitals are more complex. These shapes represent the three-dimensional regions within which the electron is likely to be found. Principal quantum number (n) & Orbital angular momentum (l): The Orbital Subshell: https://youtu.be/ms7WR149fAY If an electron has an angular momentum (l ≠ 0), then this vector can point in different directions. In addition, the z component of the angular momentum can have more than one value. This means that if a magnetic field is applied in the z direction, orbitals with different values of the z component of the angular momentum will have different energies resulting from interacting with the field. The magnetic quantum number, called ml, specifies the z component of the angular momentum for a particular orbital. For example, for an s orbital, l = 0, and the only value of ml is zero. For p orbitals, l = 1, and ml can be equal to –1, 0, or +1. Generally speaking, ml can be equal to –l, –(l – 1), …, –1, 0, +1, …, (l – 1), l. The total number of possible orbitals with the same value of l (a subshell) is 2l + 1. Thus, there is one s-orbital for ml = 0, there are three p-orbitals for ml = 1, five d-orbitals for ml = 2, seven f-orbitals for ml = 3, and so forth. The principal quantum number defines the general value of the electronic energy. The angular momentum quantum number determines the shape of the orbital. And the magnetic quantum number specifies orientation of the orbital in space, as can be seen in Figure 9.4.3. Figure 9.4.4 illustrates the energy levels for various orbitals. The number before the orbital name (such as 2s, 3p, and so forth) stands for the principal quantum number, n. The letter in the orbital name defines the subshell with a specific angular momentum quantum number l = 0 for s orbitals, 1 for p orbitals, 2 for d orbitals. Finally, there are more than one possible orbitals for l ≥ 1, each corresponding to a specific value of ml. In the case of a hydrogen atom or a one-electron ion (such as He+, Li2+, and so on), energies of all the orbitals with the same n are the same. This is called a degeneracy, and the energy levels for the same principal quantum number, n, are called degenerate energy levels. However, in atoms with more than one electron, this degeneracy is eliminated by the electron–electron interactions, and orbitals that belong to different subshells have different energies. Orbitals within the same subshell (for example ns, np, nd, nf, such as 2p, 3s) are still degenerate and have the same energy. While the three quantum numbers discussed in the previous paragraphs work well for describing electron orbitals, some experiments showed that they were not sufficient to explain all observed results. It was demonstrated in the 1920s that when hydrogen-line spectra are examined at extremely high resolution, some lines are actually not single peaks but, rather, pairs of closely spaced lines. This is the so-called fine structure of the spectrum, and it implies that there are additional small differences in energies of electrons even when they are located in the same orbital. These observations led Samuel Goudsmit and George Uhlenbeck to propose that electrons have a fourth quantum number. They called this the spin quantum number, or ms. The other three quantum numbers, n, l, and ml, are properties of specific atomic orbitals that also define in what part of the space an electron is most likely to be located. Orbitals are a result of solving the Schrödinger equation for electrons in atoms. The electron spin is a different kind of property. It is a completely quantum phenomenon with no analogues in the classical realm. In addition, it cannot be derived from solving the Schrödinger equation and is not related to the normal spatial coordinates (such as the Cartesian x, y, and z). Electron spin describes an intrinsic electron “rotation” or “spinning.” Each electron acts as a tiny magnet or a tiny rotating object with an angular momentum, even though this rotation cannot be observed in terms of the spatial coordinates. The magnitude of the overall electron spin can only have one value, and an electron can only “spin” in one of two quantized states. One is termed the α state, with the z component of the spin being in the positive direction of the z axis. This corresponds to the spin quantum number ms=12. The other is called the β state, with the z component of the spin being negative and ms=−12. Any electron, regardless of the atomic orbital it is located in, can only have one of those two values of the spin quantum number. The energies of electrons having ms=−12 and ms=12 are different if an external magnetic field is applied. Figure 9.4.5 illustrates this phenomenon. An electron acts like a tiny magnet. Its moment is directed up (in the positive direction of the z axis) for the 12 spin quantum number and down (in the negative z direction) for the spin quantum number of −12. A magnet has a lower energy if its magnetic moment is aligned with the external magnetic field (the left electron) and a higher energy for the magnetic moment being opposite to the applied field. This is why an electron with ms=12 has a slightly lower energy in an external field in the positive z direction, and an electron with ms=−12 has a slightly higher energy in the same field. This is true even for an electron occupying the same orbital in an atom. A spectral line corresponding to a transition for electrons from the same orbital but with different spin quantum numbers has two possible values of energy; thus, the line in the spectrum will show a fine structure splitting. The Pauli Exclusion Principle An electron in an atom is completely described by four quantum numbers: n, l, ml, and ms. The first three quantum numbers define the orbital and the fourth quantum number describes the intrinsic electron property called spin. An Austrian physicist Wolfgang Pauli formulated a general principle that gives the last piece of information that we need to understand the general behavior of electrons in atoms. The Pauli exclusion principle can be formulated as follows: No two electrons in the same atom can have exactly the same set of all the four quantum numbers. What this means is that electrons can share the same orbital (the same set of the quantum numbers n, l, and ml), but only if their spin quantum numbers ms have different values. Since the spin quantum number can only have two values (±12), no more than two electrons can occupy the same orbital (and if two electrons are located in the same orbital, they must have opposite spins). Therefore, any atomic orbital can be populated by only zero, one, or two electrons. The properties and meaning of the quantum numbers of electrons in atoms are briefly

1 What is the Australian Qualifications Framework (AQF)? 2 Describe three nationally accredited training products. 3 Define CBA. How is CBA different from other types of assessment? Briefly describe how the four (4) principles of assessment impact the CBA process. 4 Briefly describe how the rules of evidence impact the collection of evidence. 5 Describe different methods of gathering evidence for CBA . Describe the three main assessment methods. Describe the two different assessment pathways. What is recognition of prior learning (RPL), and what are the benefits? 6 What are the three endorsed components of training packages? 7 Briefly describe the components of a unit of competency (UofC). 8 What is the ACSF? Identify the five core skills. 9 How do the dimensions of competency guide assessment practices? 10 What is the purpose and context of assessment? How can you contextualise an assessment while maintaining the integrity of the Unit of Competency? 11 What is the difference between assessment tools and assessment instruments? What are the essential parts of an assessment tool? 13 What is the purpose of mapping against units of competency? 14 Describe reasonable adjustment in competency-based assessment. What are the limitations? 15 What would you do if a candidate provided you with evidence that was not authentic? 16 How does the marker’s guide support you in objective assessment judgement? 17 Give an example of inclusive language when giving assessment instructions. 18 What are the key WHS instructions that are required prior to commencing assessment? 19 What do you look for when clustering units of competency for an assessment tool? 20 What are the key characteristics of a cohort of learners that need to be considered in the planning an assessment tool?

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