Sunday, December 1, 2019

Socio Cultural Dimensions of Learning free essay sample

Lev Vygotsky’s theory focuses on Socio-Cultural dimensions of Learning and development, emphasizing that individual cognitive processes are continuously embedded on a social and cultural context. It is referred to as â€Å"social constructivist theory†. In order to understand the influence of Lev Vygotsky’s work addressing socio-cultural dimensions of learning and development, it is important to understand the three central concepts of his theory which all have direct implications for the classroom. These are the concepts of the zone of proximal development, scaffolding and the socio-cultural context of learning. Zone of Proximal Development. Vygotsky stated that a child follows an adults example and gradually develops the ability to do certain tasks without help or assistance. Vygotskys often-quoted definition of zone of proximal development presents it as the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance, or in collaboration with more capable peers. We will write a custom essay sample on Socio Cultural Dimensions of Learning or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Vygotsky among other educational professionals believes the role of education to be to provide children with experiences which are in their ZPD, thereby encouraging and advancing their individual learning. Scaffolding. For Vygotsky, scaffolding is the process of providing a child or adolescent with a good deal of support during the time he is learning something. To successfully apply it in a classroom, it is important to know not only where a child is functioning now and where that child will be tomorrow, but also how best to assist that child in mastering more advanced skills and concepts. This is where scaffolding comes in. Although not used by Vygotsky himself, the concept of scaffolding helps us understand how aiming instruction within a child’s ZPD can promote the child’s learning and development. Socio-cultural context of knowledge. Vygotsky emphasizes the important role of culture in influencing how individuals learn and think. His thinking has had a significant impact on research demonstrating that cognition is â€Å"situated† – occurs in content. Social Processes in Learning Situated Learning (Situated learning is related to Vygotsky’s notion of learning through social development. Situated learning is a general theory of knowledge acquisition. It has been applied in the context of technology-based learning activities that focus on problem-solving skills. Lave (1988) argues that learning as it normally occurs is a function of the activity, context and culture in which it occurs. This contrasts with traditional classroom learning activities which involve knowledge which is often presented in an abstract form and out of context. Social Interaction is a critical component of situated learning – learners become involved in a ‘community practice’ which embodies certain beliefs and behaviours to be acquired. Learning becomes a Social Process dependent upon transactions with others placed within a context that resembles as closely as possible the practice environment. Principles: 1. Knowledge needs to be presented in an authentic context, i. e. , settings and applications that would normally involve that knowledge. 2. Learning requires social interaction and collaboration. The two approaches to learning: decontextualized (classroom) versus contextualized (situated) learning. General Idea of situate learning: â€Å"If you put a learner in a real world situation (authentic context) and interact with other people then learning occurs. † Situated learning as well can be applied in technology based learning activities focused on problem solving skills. In this type of learning and participative methods can be used extensively by the teacher so that students will learn more effectively. Communication Patterns in Learning Sociolinguistics Is the study of language in society. Sociolinguistics is the study of the linguistic indicators of culture and power. Sociolinguistics is the study of the effect of any and all aspects of society, including cultural norms, expectations, and the way language is used. It studies how dialects differ between groups separated by certain social variables, e. g. , ethnicity, religion, status, gender, level of education, age, etc,. and how creation and adherence to these rules is used to categorize individuals in social class or socio-economic classes. Sociolinguists also study the grammar, phonetics, vocabulary, and other aspects of this sociolect much as dialectologists would study the same for a regional dialect. Sociolinguistics is the effect of the society on the language, while the latter’s focus is on the language’s effect on the society Fundamental concepts in sociolinguistics Speech community: describes a more or less discrete group of people who use language in a unique and mutually accepted way among themselves. High prestige and low prestige varieties; certain speech habits are assigned a positive or a negative value which is then applied to the speaker. Social network is another way of describing a particular speech community in terms of relations between individual members in a community. A social network may apply to the macro level of a country or a city, but also to the intrapersonal level of neighborhoods or a single family Recently, social networks have been formed by the intern. et, through chat rooms, online dating services. Types of Classroom Interaction To avoid overemphasizing the theory and memorization of the material presented in class, teachers employ classroom interaction to give students the ability to think critically, focus on specific details and practice what they have learned. Teachers have access to many methods of creating an interactive classroom. Common methods include classroom conversation, question-and-answer, reading aloud and role-playing. Reading Aloud Reading aloud is a classroom activity in which one person is reading while others listen. Reading aloud may be performed by the teacher or student. Reading aloud may be performed by a single person or by a group taking turns. This form of highly structured classroom interaction allows all students to be focused at exactly the same point in a reading. This allows students to easily focus on vocabulary and pronunciation. Conversation Classroom conversation is a form of classroom interaction in which students in the class discuss a given topic. The conversation may be held across the whole class or in smaller groups. Conversation is an important form of classroom interaction because it helps students develop their language skills. In a conversation, students may apply the skills and knowledge they have acquired in the class, making classroom conversation a practical form of interaction. Role-Play Role-playing is an activity in which students take on given or chosen roles and act out a scene with others. This form of interaction lends itself to almost any situation, and the only restriction is a students imagination. Role-playing allows students to demonstrate their creativity and knowledge about their roles, and it allows students to think outside of the constraints of the classroom and consider how they might apply the learned material to the real world. This form of interaction can integrate different subjects into one activity. Question-and-Answer Question-and-answer is a traditional form of classroom interaction in which a teacher or student explains and poses a question for the other. Questions asked by the teacher are usually for the purpose of assessment, while questions asked by the students are usually for obtaining new information. The Socratic method is also a form of question-and-answer interaction. The Socratic method is a form of asking questions with the intent of leading students to discover the answer themselves. Question-and-answer as a form of interactive learning allows students to have a large influence on the agenda of the classroom, because it allows them to freely express their thoughts and feelings.

Tuesday, November 26, 2019

Transparency and the Location Mystique

Transparency and the Location Mystique Free Online Research Papers Transparency In Retail Site Location Models Transparent: Free from pretense or deceit, frank. Obfuscate: Mask, conceal, disguise as part of a hidden agenda. Location Decisions: Science or Intuition? When it comes to selecting retail locations, knowledge is power. Experience plus intuition are the key drivers. The large majority of location decisions continue to be made on the basis of intuition and experience, not science. Why? Well, a large part of the reason is that this common sense decision process works pretty well in many situations. Good locations are good locations after all. Mom, my neighbor George, or even Aunt Sally can all tell the difference between good and bad locations to a point. The next step, however, that goes beyond good and bad to actually estimate a store’s performance in that location, does require special intuition and experience. This also works quite well to a point. The best decisions come when real estate expertise is complemented by objective, scientific knowledge that can come from demographics or other data and from predictive models. Because so many companies use models, or other kinds of intelligence, today to evaluate sites, it’s important to take our understanding one step further. Both intuitive predictions and model predictions tend to be used in ways that are not transparent. And, if a decision process is not transparent, it’s also not generally open to shared feedback and improvement. If you are on the receiving end of model predictions delivered as either mystery statistics or the oracle, as real estate or development personnel often are; then a lack of knowledge about how the model works may be beneficial. Usually, the normal â€Å"good† locations are not the ones people remember; it’s the dog sites. In the long run, however, everyone (especially your company) benefits when the basis for a decision is objective, understood and shared. This enables the criteria for current good locations to be extended into future â€Å"good locations†, and the problems with bad locations to be understood and avoided. Companies that have this attitude usually benefit enormously from predictive modeling because the model results are simply one piece of the decision pie, and they don’t step on anyone’s toes. If everything is on the table and above board transparent then new information is always welcome. Unfortunately, the more common problem is that a history of relying on the company’s version of a â€Å"location mystique† (which just means your company’s shared, intuitive understanding of what makes a good or bad location), is combined with a general lack of transparency and understanding around the factors that drive store sales. This results in a decision process that attempts to be good, open, and feedback-oriented, but ends up driven by tradition and opinion. Why Location Decisions Often Lack Transparency Real estate information and decision-making, certainly on the commercial side, lacks transparency because the key players benefit from controlling access to information at different points in the process. We see, for example, that commercial databases listing available sites are very incomplete because the local brokers have a wealth of inside knowledge. They know which property will be available soon, or could be available at the right price and their competitive advantage comes from controlling this knowledge. Sales Prediction Models Typically Do Not Please Everyone It won’t surprise anyone reading this to know that most real estate decisions are driven by experience and intuition, not models or objective criteria; or to learn that the decision-making process for site selection often loses transparency because of internal politics, communication barriers that prevent knowledge sharing, hierarchical administrative structures, and IT or technological issues. The truth is that, much of the time, brokers and real estate VPs don’t want the interference of a model unless it supports their decision. Why? Because models generally get in the way of doing deals. In cases where the model predictions and expert opinions converge, great! The model is not a problem, per se, but actually it’s agreement with the expert is setting up a big problem because now, anyone examining the results is likely to conclude that the model has some validity. Right? Well, look at what happens when a site comes up that the VP likes but the model is not enthusiastic about. Ouch. The VP wouldn’t have proposed the site if he/she didn’t think it would work. Now the model’s second opinion really hurts because the last site got high ratings and everyone was happy. Who is right in this case: the model or the VP? Transparent Site Features Help All Parties Communicate Objectively The problem here is transparency. Store sales are obviously based on a combination of factors. Some are location-related (like the site attributes or demographics), and others are not, like operations, marketing or even brand strength. If the VP and the model were talking the same language, they could agree or disagree on the strength of each of these factors, and eventually reach a consensus opinion on the site. Lacking this transparency in the decision process, both are left only with their differing opinions, and supporting justification. This matters little, since they are not speaking the same language. We addressed this issue in our modeling systems by listing specific site features, or rating the quality of certain demographic measures. This does help some. What stops it from being very beneficial, is that we didn’t take the issue of transparency far enough. The problem was confusion about the role of the location in store sales. This varies from one extreme, â€Å"If it’s a dog site then the location is at fault bring me the head of the real estate manager who picked this site:†, to the other, â€Å"The location may be part of the problem but lets take a good look at marketing, operations and competitive positioning first.† In general, the way this confusion appears, is that because the influence of all key factors on store performance is not measured quantitatively, or well understood, the role of the location is typically perceived to be much too strong. In my experiences over twenty-five years, with several hundred retail companies, I would have to admit that the number of companies willing to make the location decision process transparent could be counted on just a few fingers. Dick Riveria, the former president of TGI Friday’s did a rebuild of their sales prediction model just to open up the process and the information to a new team of real estate managers and executives. Ron Stegall, the founder of BizMart, insisted that everyone, both core staff and brokers, be familiar with their site model’s components so they could validate them. Gary Wyatt at Lowe’s introduced modeling to bring transparency to a process shared by both marketing and real estate. Jim Kirkpatrick at Applebee’s, and Jim Torcivia at Cracker Barrel, always tackle problem stores from a â€Å"bring me better measures so I can understand what is happening† perspective, rather than simply as location problems. As a forecaster, my biggest challenges in making store performance transparent were situations where the concept was part of the problem. In these situations, the concept name, the differences between store prototypes across markets, the competitive positioning of the concept, and so forth, often impacted sales. Yet, raising the concept flag was not something the owners were willing to handle. I won’t share any bad examples here, but Ruby’s Diners, Staples, and Red Robin are good examples because their hard work in the other direction to fine-tune the concept component of performance in objective, shared ways that impact the bottom line. One of the major ironies of location modeling is that it is common for sales prediction models to be the ultimate in non-transparency. They are often statistically complex (which is intimidating by itself for many people), difficult and time consuming to learn or use, access is often controlled and limited to a special group of analysts, and who gets to see what reports when may be determined by political agenda rather than â€Å"need to know.† You would be shocked at the major corporations for whom I’ve conducted workshops that have never had all the key people involved in location decisions real estate, finance, marketing, operations and executives in the same room together to discuss what is needed when from whom. Transparency Helps to Create Actionable Results Another problem with most models is that they are either largely based on statistical â€Å"mumbo jumbo† that could not be made transparent if you wanted (some of the early neural network models fit this description well) or are so simplistic (like common regression or gravity-based models) that they lack credibility because even a statistically naà ¯ve person can understand that they cannot explain the complexity of many stores in many retail situations in many markets. Even worse, what good is transparency if you cannot do anything with it? We’ve built models with fairly transparent reports for some time but so what! The user can disagree with what they see, but the battle is already lost because the results don’t change! If I disagree with the answer, and I can see why the answer is wrong because the system is transparent you had better let me be able to act on it. Now that you know my biases, you know what’s coming next. There is absolutely no good reason for Site Selection and Sales Prediction to be a covert, mystical process with hidden rules, hidden agendas, and controlled access to information. One of the biggest misses I’ve seen has to do with franchisees. I would guess that at least 25,000 franchisee locations have been selected using our modeling systems and reports. Most of these franchisees saw a Site Quality Rating, the factors that contributed to the rating, and a sales prediction; yet, not a single one has (to my knowledge) ever been able to sue the franchiser successfully over a poor performing store based on information in our report, largely because it is transparent. The courts have made it clear for many years: not sharing critical information is generally risky. Sharing (being transparent) with an appropriate disclaimer offers more benefits to all parties, and more protection from liability. Benefits of a Scientific Approach to Site Selection There actually is a science of site selection with a good deal of research on what factors matter where. My book, The Site Book, is a synthesis of much of this knowledge as we apply it in our modeling programs. Companies that have made the culture shift to use and share these objective rules for location decisions not just the financial estimates have seen tremendous benefit on many dimensions, not the least of which is their bottom line. One of the simplest statistics that illustrates the value of this scientific approach is sales volume. When we looked back over the last 7,000 locations for 13 different concepts that were evaluated using the logic in The Site Book, we found that locations that had a Site Quality rating of 65 or higher (the average rating is 50 on a 1-100 scale) also had 17% higher sales than the average store for that concept! Yes, there is a strong relationship between an objective measure of site quality and sales. PAI’S Approach to Modeling The rules used to make decisions in models need to match the rules of the retail world, not just fit a set of mathematical criteria. This statement, which is really talking about transparency, simply means the models need to be thinking about the world in ways that match the logic that you or I might use as real estate professionals. To some degree we met this goal in the past by creating reports that listed the criteria used to estimate site quality and sales but I don’t think we went far enough. Why? Because the best, transparent models are also: Logical: The rules in the model match what happens in reality Open: You can see the rules and logic operate Robust: The model can predict reliably despite the â€Å"noise† or error associated with most sales modeling Adaptive: There is a way to learn from the errors in prediction to improve the model If you had asked me five years ago, when I wrote The Site Book, I would have touted our logic for location analysis as pretty complete. And, I might have said the same about sales forecasting. Yet, how can this be when locations are responsible for only approximately 50% of store sales! What about the missing 50%? For many years in our presentations to clients, we represented this 50% with the following pie chart: This is transparent to a degree if you know the Location Factors and can specify their influence. Yet, you cannot have a truly transparent sales prediction without all of the components. It’s impossible to know if a store’s weak performance (when you think it should be doing well) is due to a problem with your location models, or store operations, marketing, etc. This shift in thinking led to a series of analyses to explain the other factors: the market, competitive positioning in the market, operations, the concept, the brand and many others. Could we explain the sales contribution of these other components? Typically the answer was â€Å"yes† because we had so much detailed customer data that let us get at problems with operations or marketing and in doing theses analyses, we also learned that once the non-location components could be predicted, it was also possible to predict the contributions of many small location factors in our original model. How much is a new sign worth? What about being on the corner versus down the street two blocks? What is the contribution of the tourist population to sales? What’s the contribution of Market A versus Market B to sales? Answering these questions led to a very detailed Transparent Report in which all of the factors underlying store sales could be viewed and analyzed. The high-level version of the report is illustrated in the table that follows: Making Changes to Model Predictions†¦ Adding Validity or Fudging Logical, open, robust, adaptive†¦ that’s a great list of attributes to define transparency; but they don’t mean much without the ability to make changes in the model’s predictions to reflect what you’ve learned. Model predictions, even from the best systems like PAI’s, the National Weather Service, or election polls, are not perfect. You should expect that they will change when knowledge improves. Remember that transparency in almost any context interpersonal, financial, business management, politics, and so forth usually goes hand in hand with getting good feedback and being flexible enough to act on it. Part of the value in watching the individual gears turning in the â€Å"big machine† is to be able to notice the wobble and make a correction when needed. This couldn’t be truer in many domains than it is for location analysis where the change in a single factor (a new manager, construction on the road in front of the store, o r a competitor opening down the street), can dramatically impact performance in one week! FOUL you say. A fudge is still a fudge. We wanted a model to provide an objective, unbiased prediction, not one that could be fudged to fit someone’s biases. Let me see if I understand what you are saying. When you are ill, would you rather rely solely on the results of blood tests and temperature readings than to add the interpretation that is part of the physician’s expert opinion? Or, if surgery is called for, to would you forego the expert opinions of several physicians? In the case of location analysis there is a lot of knowledge about a site, the market, competition, and experiences in similar markets that may not be in your modeling program. Besides, transparent is as transparent does. In other words, because it’s transparent, everyone can see the gears turning. There can be no mathematical cover-up, as you might experience with a single prediction driven by complex statistics. Transparency means you are looking at the facts, at least as the model sees the world. There can be no hiding because everyone else sees and shares the same view. If there is disagreement about a â€Å"fact† such as the quality of the ma rket, name recognition, the manager’s performance, visibility of the store, or any other parts of the transparent report that’s actually good! You are working at a concrete level where a consensus can probably be reached. If the consensus opinion is that the model is wrong about one of these components, you want to change it and determine how this change will impact the predictions. In our work with clients, disagreement with the model results, and the process to understand these disagreements (that we call â€Å"field validation†), is encouraged. We see the whole proposition as 50/50. Fifty percent belongs to the objective model results; fifty percent belongs to the intuition and experience of an expert in the field actually evaluating the site, or as a part of a real estate committee in a Board room. Historically, adjusting the models predictions meant adjusting or tinkering with the input parameters until you got the predictions you wanted. Today, what we attempting to do with our transparent approach to modeling, is encourage shared understanding and communication about all of the components of sales, especially the large, non-location components such as operations or marketing, that are not well understood in many companies. How much do operations or marketing contribute to store sales? Quite a lot and when you can measure this contribution, guess what happens to your location model. It gets a lot better because you are not trying to predict the performance of operations with a demographics report. You are predicting only that part of sales explained by demographics with demographic predictors, site sales with site features, market sales with market features, and so forth. We’ve witnessed that transparent models completely change the nature of the conversation when there is a disagreement between a model’s prediction and actual sales. Instead of a conversation that begins, â€Å"What’s wrong with the model. It’s under-predicting performance for this store by 25%;† It goes, â€Å"Here’s a large discrepancy. Let’s look at the transparent model report and see if we can understand where it’s coming from!† What you want from a model is â€Å"it’s† version of the truth, not an oracle. The key to understanding and utilizing this truth is transparency. Transparency transforms what traditionally was a black box process into an intelligent dashboard, with gauges that explain what is happening in each of your stores, and dials to make the needed adjustments. Now you have a wise partner in your modeling system, not an idiot savant! RETAIL SYNERGY By Dr. Richard Fenker, PhD For the last twenty years I (and more recently with two colleagues, Lynn Cherry and Selby Evans) have been tracking an elusive phenomenon, which at times is as plain as the nose on your face, and at others as elusive as a wisp of clouds on a clear day. Its a force in the world of retail that is as obvious and strong as the force of gravity when you toss a ball in the air. Yet, it is also as mysterious as the physical principles that underlie gravity and its relationship to other forces which are still not well understood by physicists. The phenomenon is retail synergy. As obvious as the value of a collection of synergistic retailers sharing a common shopping center may seem, especially their influence on the gravity of the center or its ability to attract customers, relatively little beyond common sense is known about this practical yet ephemeral concept. In our everyday experience, we live the concept of synergy as we are attracted to clusters of retailers that work well together. In fact, we even name these clusters with familiar labels such as shopping centers, malls, strip centers, power centers, lifestyle centers and the like. There is no mystery here, other than a wait for the next clever name to come from a developer in California, Minnesota, or New York. There is also no mystery about the fact that some of these centers work (meaning they are more effective draws for retail traffic) much better than others, or that certain types of people are attracted to certain types of centers. There is also an obvious parallel to atomic physics. We study the behavior of atoms and molecules, and the attractions between heavier particles such as a proton and the lighter particles such as electrons, or the special universe of quantum entities such as charm, flavor, spin and quarks more on this in a moment. The mystery for me started in the early 1980s. I was working with Norm Brinker and Ron McDougall on a customer research project for Chilis. In developing the research instrument, we asked the question: What other retailers are linked to a visit to Chilis either before or after? Our intention in asking this question is obvious. If there are supporting linkages, or other retailers likely to be visited in connection with my restaurant visit, why not locate a Chilis near centers containing these retailers, thus increasing the probability of a visit to Chilis? The first answers that emerged from our research made sense. While we couldnt identify specific retailers that mattered, we could say that being near upscale shopping or entertainment activity mattered a great deal for some locations. So, at the most general level, certain classes of retail activity were clearly synergistic with casual theme dining. I can sense that yawn beginning hold on for a moment. What we didnt understand at the time were any specifics. What types of retailers were best or did we need to get to the next level and deal with specific retailers? We could count the stores in a shopping center (although the databases at this time were much more limited than today), but we didnt understand the relationship between the stores or how much this mattered. Finally, there is obviously some relationship, or interaction, between the people who live and work in the neighborhood, and the retailers. However, it was not obvious how this interaction contributed to the synergy of the area. Yes, we were scientifically curious, but were driven by a much more practical problem. Our forecasting model was telling some of these early clients (such as Steak and Ale, TGI Fridays, or BizMart) that they would do well in certain centers because of the synergistic activity and we were dead wrong. I remember driving a site in Washington DC with a retailer that had used our model, in part, t o make the decision to open the store. The retail energy was awesome. With a regional mall nearby, it was also a great neighborhood filled with our clients customers. What was the problem? The answer wasnt obvious. However, as we studied the problem, one area where our thinking was muddled did become more clear. We had been mixing the idea of Retail Draw and Retail Synergy, essentially treating these as the same concept in our model. The more good retail around YOUR concept the better. Unfortunately the world doesnt work this way. As we studied the DC mall carefully, it was obvious that our client was near a hundred supporting retailers in the mall or in other shopping centers near the mall. However, they were actually adjacent to a smaller center filled with junk retail, and badly positioned with respect to the competition. Im sure youve heard this story before it was a great location, except for the At least the concept of Retail Draw seemed clear. And based on the results of a couple of million customer surveys that weve administered since, and over 100,000 sites evaluated, it has remained so. Retail Draw is essentially a measure of the pull of a retail area based largely on the number of businesses in that area. Regional malls have huge pulling power, while local strip centers have very little, with power centers and lifestyle centers in between. The importance of pull, or retail gravity as it is called in many modeling approaches, is that the following principle seems to be consistently valid: The larger the number of retailers in an area, the stronger the Retail Draw, and the larger the effective trade area for most of the retail businesses. There is little mystery here. The city center in healthy cities is the ultimate retail area, drawing from the entire city. Regional malls can have as much or more pull. Everyone in the community, to a degree, becomes a customer of the businesses in these areas. What was still a mystery, however, was the synergy of the area, or the synergy of the businesses in the malls or shopping centers of the area. A definition of retail synergy was emerging in our thinking: Retail Synergy describes the degree of compatibility between a collection of retailers such that customers who use one of these retailers are also likely to use other retailers in the same center. This is not a bad definition of synergy as we would view it today, but there was still a major flaw in our thinking that took several more years to fully appreciate. Can you see what we were missing? In any case, armed with this definition of synergy, we could begin to understand that the best centers were ones that attracted distant customers, not only because of Retail Draw, but because the stores in the center had some degree of Retail Synergy. Looking back, this seems like an obvious step, and in some respects this is true. However, as our understanding evolved in real-time twenty years ago, a philosophy was also evolving, and it is one that has influenced our thinking about location analysis and sales forecasting models, from the 1980s to the present. What was happening was that in the pursuit of answers for why the statistics in our models didnt work in some cases (this is a euphemism for bad predictions), we were forced to look more carefully at the behavior of the customers w e were trying to model. This shifted us from a find a better methododological approach, to one of explaining how shoppers and diners actually use the retail world. One of our first chances to test this thinking came with the modeling research for two clients, Eckerds and Cracker Barrel. As we designed their customer surveys, we added sections that gave us much more detail on the behavior patterns of users, and the specific combinations of adjacent retail businesses that helped or hurt sales. It may not surprise you to learn that a visit to Eckerds has a high probability of being linked to a visit to other kinds of retailers or institutions; or, that travelers, service stations, motels, and certain retailers all interact in certain ways to influence Cracker Barrels performance. At the time, it surprised and delighted us and even though we didnt see a large jump in the sales forecasting accuracy of our models, our risk models (models designed to spot potential dog locations) did get a nice bump in accuracy. We could now identify certain kinds of retail situations that just were not right for our clients. This synergy component has remained a par t of our models ever since. The Cracker Barrel and Eckerds research was moving us in the right direction, and several mall modeling projects finally got us to yet another plateau. What every developer and mall-based retailer who is reading this article understands, that we eventually figured out, was how important adjacency influences are in driving behavior. Shoppers have patterns, driven to a large degree by the occasion. On a practical Saturday, I may bounce from the grocery store, to the hardware store, to the drug store, to the post office or whatever. On a shopping Saturday, I visit the mall, going to six stores that sell my kind of clothing, have lunch, and then look for a wedding present. Which stores I visit in any time period is obviously influenced by my needs and the time factors; but both synergistic factors (which stores are in the retail area) and adjacency factors (which stores are near each other in the centers) also help determine which specific retailers I will visit. Retail Adjacency Effects describe the local spatial relationships between key retailers and any nearby concept. The strongest adjacency influences occur when the convenience of having certain retailers nearby increases the probability of a linked visit to your store. New car dealers figured out a decade ago that the best way to bring shoppers into their showrooms was to create a cluster of dealerships, because, when people shop for new cars, they typically visit a number of dealers on a single trip. Today, unless you are a Lexus, Mercedes or other destination dealer, you are asking for trouble if you ignore the presence of this common behavior pattern tied to Retail Adjacency. Now, we are getting a little warmer. As we incorporated adjacency influences into our models, predictions did improve. In fact, the combination of adjacency and synergy could make as much as a 30% influence on the bottom line for some types of clients. If you are a fast food concept, for example, how do you feel about locating in grocery-anchored shopping centers? Our research findings here might surprise you. (Send me $1,000, and a box-top from my favorite brand of cereal, and Ill share them with you.) What we had done was to get down to some very specific behaviors of consumers that were closely related to the way the retail world was arranged. Retail Draw, Retail Synergy, and Retail Adjacency what is next? The next step is in some respects a digression, since our discussion is about retail synergy; but, it happened so naturally and with little contribution on our part, other than to say, yes, we can do it, that it is worth mentioning. Two major big-box retailers in different industries, one grocery-related and one merchandise-related, asked essentially the same question at the same time. Can you help us design the layout of the store to increase the potential that customers who come to purchase one product have easy access to related products they might also purchase? The answer was, of course, yes, and within a couple of months, our list of synergy-related products had been expanded to include the concept of Product Adjacency. Product Adjacency, or Department Adjacency, describes the layout of store departments or merchandise in order to optimize synergistic patterns of purchasing behavior. To meet the needs of these retailers, we modified our standard customer research instrument to include in-store behavior patterns, so we could track what customers actually did while in the store and surprised ourselves with the results. The same strong proximity-related patterns we were observing for Retail Adjacency outside the store continued in the store. A few departments (the destination departments), for example, attracted most of the initial visits to the store. Other departments visited were strongly influenced by their proximity to these destinations as were purchasing behaviors. In one case, a radically new store design was created by clustering secondary departments, that appealed to certain customer segments, around the destination departments that attracted those same customers. This principal, while undoubtedly not novel, proved so effective that we have adopted it as one of our classic approaches to store design. Despite all of this work, there was still a missing piece in our definition of synergy. We could see from our customer research studies that some busy retail areas and malls seemed to attract customers from all over the market, while others of a similar size behaved almost like local malls or shopping centers, even when many of the same brands were present in both centers. Ridgemar Mall and Hulen Mall are two of the major shopping centers in the Fort Worth area of Texas. The difference between them is profound. Despite the presence of a Neiman Marcus in Ridgemar Mall, plus essentially the same retailers as the Hulen Mall, it behaves much like a local mall, drawing largely from a five-mile area, with many of the surrounding stores and centers struggling with marginal performance. Hulen, on the other hand, is a major destination mall that draws from the entire city with its mix of mid-scale and up-scale retailers and restaurants. Whats wrong with this picture? You already know the answer, dont you? You can see what obvious component is missing in our understanding for synergy - demand, destination-driven demand. Looking back, it is obvious, but it was not obvious at the time. The retail world is filled with centers of retail activity, each with some level of Retail Draw, ranging from a dozen stores to many hundreds. These centers can become strong attractors of shoppers and diners for the reasons explained above, primarily Retail Draw and Retail Synergy. However, the strength of the Retail Draw depends not just on the number of stores, and the tendency for people to link shopping visits to several different stores on a trip, but also on the lifestyle focus of the center. Secondarily (because customers will cross neighborhood boundaries if the draw is strong enough), it depends on the lifestyle fit of the center to the surrounding neighborhood. In other words, large clusters of retailers sharing a common focus on a certain set of customer segments or lifestyle groups, have by far the strongest drawing power. Not everyone in the market will visit these centers, but the core lifestyle groups will travel quite a distance, and deal with other hardships associated with traffic or locations, because the draw is so strong. Have you ever been to the IKEA location outside of New York City? Well, you cross the Hudson River into New Jersey, drive North on the freeway until you are in the middle of the warehouse, factory, and wasteland zone. Next, you proceed East a couple of miles into no mans land along the river bottom, and arrive at one of the most successful destination retailers in the world capable of creating their own draw and synergy because the lifestyle pull is so strong for some segments. Lifestyle Synergy describes the focus of the retail area, or shopping center, on a limited set of customer segments or lifestyle groups. The larger the retail mix, and the stronger the focus, the more Retail Draw for the targeted groups. Now, we are almost finished with the components needed to build a good model of synergy. They include Retail Draw, Retail Synergy, Retail Adjacency, Lifestyle Synergy and last but not least, the mystique that accompanies any successful business venture. By mystique I mean that for the best of everything, the whole is always greater than the sum of the parts wines, personalities, art, sex, and certainly the most successful centers or retail businesses. You cannot explain IKEAs remarkable performance without mystique! The concept of synergy speaks at the classical level to clusters of similar or related retailers. In reality, it too is a much richer concept, linked inevitably with the properties of the retail world that are one step beyond you get what you see, and more closely linked to an entangled, interdependent, universe where a healthy respect for the mystique of a Krispy Kreme, a McDonalds, an IKEA, or a Lowes is appreciated even if not completely understood! CUSTOMER KNOWLEDGE FIVE EASY QUESTIONS: CAN YOU ANSWER THESE FOR YOUR COMPANY? 1. Who are your customers? Knowing Who are your customers can guide planning, marketing, and site selection if your research asks the right questions. Bad answers here serve as little more than an afterthought in an annual report. 2. How do they use your concept? Usage patterns feed operations the information needed to improve perceived service by meeting the needs of each user type; this knowledge also helps real estate understand the site features most important to each group. 3. How far, and for how long, will they normally travel? A concept normally has three trade areas that matter, not one. Time and distance data for users coming from work, and for users coming from shopping or other retail activity, is as important as it is for people coming from home. 4. What drives their visit, and how well are you executing on these attributes? For improving operations and encouraging return visits, there is no comparison to directly matching customer expectations on key attributes with satisfaction ratings. Higher satisfaction ratings mean higher sales. If you want to know how to boost these ratings, just ask the right questions! 5. How are you positioned relative to your competitors? Knowledge of your competitive positioning goes hand in hand with location planning, marketing, and new market development. You are handicapped if you know your competitors, but dont know which user groups they impact or why. Research Papers on Transparency and the Location MystiqueIncorporating Risk and Uncertainty Factor in CapitalMoral and Ethical Issues in Hiring New EmployeesAnalysis of Ebay Expanding into AsiaThree Concepts of PsychodynamicThe Project Managment Office SystemResearch Process Part OneBionic Assembly System: A New Concept of SelfOpen Architechture a white paperRiordan Manufacturing Production PlanStandardized Testing

Saturday, November 23, 2019

Attila the Hun at the Battle of Chalons

Attila the Hun at the Battle of Chalons The Battle of Chalons was fought during the Hunnic Invasions of Gaul in present-day France. Pitting Attila the Hun against Roman forces led by Flavius Aetius, the Battle of Chalons ended in a tactical draw but was a strategic victory for Rome. The victory at Chalons was one of the last achieved by the Western Roman Empire.​ Date The traditional date for the Battle of Chalons is June 20, 451. Some sources indicate that it may have been fought on September 20, 451. Armies Commanders Huns Attila the Hun30,000-50,000 men Romans Flavius AetiusTheodoric I30,000-50,000 men Battle of Chalons Summary In the years preceding 450, Roman control over Gaul and its other outlying provinces had grown weak. That year, Honoria, the sister, of Emperor Valentinian III, offered her hand in marriage to Attila the Hun with the promise that she would deliver half the Western Roman Empire as her dowry. Long a thorn in her brothers side, Honoria had earlier been married to Senator Herculanus in an effort to minimize her scheming. Accepting Honorias offer, Attila demanded that Valentinian deliver her to him. This was promptly refused and Attila began preparing for war. Attilas war planning was also encouraged by the Vandal king Gaiseric who wished to wage war on the Visigoths. Marching across the Rhine in early 451, Attila was joined by the Gepids and Ostrogoths. Through the first parts of the campaign, Attilas men sacked town after town including Strasbourg, Metz, Cologne, Amiens, and Reims. As they approached Aurelianum (Orleans), the citys inhabitants closed the gates forcing Attila to lay siege. In northern Italy, Magister militum Flavius Aetius began mustering forces to resist Attilas advance. Moving into southern Gaul, Aetius found himself with a small force consisting primarily of auxiliaries. Seeking aid from Theodoric I, king of the Visigoths, he was initially rebuffed. Turning to Avitus, a powerful local magnate, Aetius finally was able to find assistance. Working with Avitus, Aetius succeeded in convincing Theodoric to join the cause as well as several other local tribes. Moving north, Aetius sought to intercept Attila near Aurelianum. Word of Aetius approach reached Attila as his men were breaching the citys walls. Forced to abandon the attack or be trapped in the city, Attila began retreating northeast in search of favorable terrain to make a stand. Reaching the Catalaunian Fields, he halted, turned, and prepared to give battle. On June 19, as the Romans approached, a group of Attilas Gepids fought a large skirmish with some of Aetius Franks. Despite foreboding predictions from his seers, Attila gave the order to form for battle the next day. Moving from their fortified camp, they marched towards a ridge that crossed the fields. Playing for time, Attila did not give the order to advance until late in the day with the goal of allowing his men to retreat after nightfall if defeated. Pressing forward they moved up the right side of the ridge with the Huns in the center and the Gepids and Ostrogoths on the right and left respectively. Aetius men climbed the left slope of the ridge with his Romans on the left, the Alans in the center, and Theodorics Visigoths on the right. With the armies in place, the Huns advanced to take the top of the ridge. Moving quickly, Aetius men reached the crest first. Taking the top of the ridge, they repulsed Attilas assault and sent his men reeling back in disorder. Seeing an opportunity, Theodorics Visigoths surged forward attacking the retreating Hunnic forces. As he struggled to reorganize his men, Attilas own household unit was attacked forcing him to fall back to his fortified camp. Pursuing, Aetius men compelled the rest of the Hunnic forces to follow their leader, though Theodoric was killed in the fighting. With Theodoric dead, his son, Thorismund, assumed command of the Visigoths. With nightfall, the fighting ended. The next morning, Attila prepared for the expected Roman attack. In the Roman camp, Thorismund advocated assaulting the Huns but was dissuaded by Aetius. Realizing that Attila had been defeated and his advance stopped, Aetius began to assess the political situation. He realized that if the Huns were completely destroyed, that the Visigoths would likely end their alliance with Rome and would become a threat. To prevent this, he suggested that Thorismund immediately return to the Visigoth capital at Tolosa to claim his fathers throne before one of his brothers seized it. Thorismund agreed and departed with his men. Aetius used similar tactics to dismiss his other Frankish allies before withdrawing with his Roman troops. Initially believing the Roman withdrawal to be a ruse, Attila waited several days before breaking camp and retreating back across the Rhine. Aftermath Like many battles in this time period, precise casualties for the Battle of Chalons are not known. An extremely bloody battle, Chalons ended Attilas 451 campaign in Gaul and damaged his reputation as an invincible conqueror. The following year he returned to assert his claim to Honorias hand and ravaged northern Italy. Advancing down the peninsula, he did not depart until speaking with Pope Leo I. The victory at Chalons was one of the last significant victories achieved by the Western Roman Empire. Sources Medieval Sourcebook: Battle of ChalonsHistorynet: Battle of Chalons

Thursday, November 21, 2019

Early Childhood Ed. Observation Assignment Essay

Early Childhood Ed. Observation Assignment - Essay Example Depending on the group and the individual child the teacher needs to have a good learning environment applying the prerequisite skills to harness and enhance the abilities of the children. This paper looks at the different activities and behaviors that have been observed in one of the centers and critically analyses what the center needs to improve or change in order to develop their children better. This paper uses the Merchantile Kindacare facility in Boston where there I observed children from the age of 3 to five years for three hours from 10am to 1pm on Tuesday. I had to go to the staffroom and observe the children’s timetables where I found that the children had a similar program for most parts of the week except for Friday where they had to rest and get off the center early. The facility is located in a serene environment where there are few noises that come out of the neighboring areas. The center has made sure that industries and other forms of facilities that may cause disturbance are far away from the area (Otto, 2014). The center has also equipped their classrooms with sound proof equipments that are meant to shield the classroom from any form of noises from the outside environment. There are building blocks and also dressing up clothes that are evident in the playing ground and the interactions that the children are able to form. They look confident when handling these forms of playing tools and are fascinated by the colors. The building blocks are especially popular with the kids with each child wishing to make their own castle and mimic their own homes through the use of the blocks. The dramatic plays that are directed by the teachers are also very popular with the children as they are looking to be the best at the plays (Koralek, 2003). There are also a lot of games that the children are engaged in looking to be competitive and match the wits of their peers. The materials that the center is using are well organized with the setting

Tuesday, November 19, 2019

Disparate Impact Essay Example | Topics and Well Written Essays - 2000 words

Disparate Impact - Essay Example aving high school diploma on the city by following the disparate impact theory of liability to prove its business requirement – not just a ploy to single out certain groups of society from getting employment (Lazarus, 2001). The Supreme Court first described the disparate impact theory in 1971, in Griggs v. Duke Power Co., 401 U.S. 424, 431-2 (1971): Title VII. It â€Å"proscribes not only overt discrimination but also practices that are fair in form, but discriminatory in operation. The touchstone is business necessity. . . . [G]ood intent or absence of discriminatory intent does not redeem employment procedures or testing mechanisms that operate as built-in headwinds for minority groups and are unrelated to measuring job capability.† In 1989, the Supreme Court minimized the defendant’s burden of proving business necessity to a burden of producing proof of business requirement in the case of Wards Cove Packing Co. v. Antonio, 490 U.S. Later, the Civil Rights Act of 1991 annulled that part of the Wards Cove decision (HR Guide 2001). "Disparate impact" is a legal theory for proving unlawful employment discrimination. But in practice, â€Å"disparate treatment† theory is practiced. Disparate impact is a thought that some recruitment practices adversely impact a group or community of people than the others. In the example of US Supreme Court Title VII case on the issue of disparate impact, in a particular case of employing laborers, the applicants needed to be high school diploma holders. This condition weeded out more blacks than whites, although there was no such intention on the part of the employer to discriminate against blacks. But as a result of the condition, there was a disparate impact on a particular race (Runkel, 2006). According to the Supreme Court, if the employees raise such a concern, the responsibility of proving the usefulness of the high school diploma lies with the employer, having â€Å"a manifest relationship to the employment in question.†

Sunday, November 17, 2019

Steps in the Medical Billing Process Essay Example for Free

Steps in the Medical Billing Process Essay The medical billing process and all of the functions that pertain to it are the responsibilities of the medical insurance specialist. It addresses all tasks that will be performed by the administrative staff members during the medical billing process. These functions are typically handled by front office staff members such as the receptionist (registration) and scheduling. Here are ten steps that will be explained which are the responsibility of the medical the medical insurance specialist. Step 1: Preregister patients * There are two main tasks that are involved when patients are at the preregistration period of their initial visit. These tasks include scheduling and bringing up to date any appointments that they may have. Step 2: Establish financial responsibility for visits * This is a very important step because it involves the determining of who is financially responsible for the visit. It also is used to establish what services may be covered under the type of insurance they have, along with payment options plan options if any, and what types may be available to the patient. Step 3: Check in patients * This step is used to check in patients, this is also the point at which new patients will provide information about themselves. A complete and detailed demographic review of their medical information will be collected at this time by the front desk. When returning patients arrive, they are asked to review the information and provide changes, if any. Step 4: Check out patients * The check-out procedure follows when the patient is done and ready to leave, once the physician and has given the patient their diagnosis and other procedures are complete. This is also the step in the visit where all expenses of the visit are tallied and the patient is brought to awareness of the amount owed in their ledger. Step 5: Review coding compliance * This area is formatted so that all official requirements are met. Meaning all official guidelines that are assigned to the codes will be followed to their standard purpose. Checking for errors when codes are assigned once diagnosis and procedures are selected is critical at this time so the patient will able to understand their charges. Step 6: Check billing compliance * Most medical practices have a standard set of fees listed, and each visit is related to a specific procedure code. Although each code is not necessarily billable, there are separate fees associated with each of the codes that are. Knowing the codes and the procedure that goes with it is important so that the correct charges are applied and the guidelines are still being followed. Step 7: Prepare and transmit claims * A claim is meant to communicate any information about the patient diagnosis given by their physician, it may also be used for the reimbursement of services that have been rendered. Most practices prepare the claim and send them off electronically. Step 8: Monitor payer adjudication * Here all procedures are listed and monitored, and any unpaid charges are explained. The codes on the payment transactions are viewed to make sure they match the on the claim form, and the payment listed for each procedure is correct in accordance to the contract with the payer Step 9: General patient statements * This procedure breaks down what bills will be covered by the insurance plan and what bills are expected to be covered by the patient. It will provide the patient with the service dates for each fee, and when and how much they are expected to pay for the services. Step 10: Follow up patient payments and handle collections * Patient medical records and financial records are filed and retained in accordance to the medical practice’s policy. They are regularly reviewed and analyzed to see if their financial responsibilities have been met. Federal and State regulations govern what documents are to be kept, and the amount of time. Reference Part 1: Chapter 1 Working with Medical Insurance and Billing pp. 16-21

Thursday, November 14, 2019

mcdonald :: essays research papers

In January 2003, McDonald, for a company that has enjoyed sizzling growth for decades announced its first ever-quarterly loss--$343.8 million. One of the main reasons for this is because McDonald has expanded too much and too fast both locally and internationally. Because of their fast growth, they sacrificed their customer services and quality. McDonald, the company that had been opening 1,700 stores a year over the past decade is dramatically reducing their number of new sores openings worldwide. According to David Grainer’s (2003) Can McDonald’s cook again? An article taken from the Fortune 5 Hundred magazine stated, â€Å"McDonald’s-the company that once made its living on prompt, friendly service-has ranked at the bottom of the fast-food industry since 1994. It now sits below every single airline as well as the IRS† (Grainer, 2003, p.120). More significantly, in 1993 due to the frustrated franchisees the company allowed the restaurant national grading system to be eliminated which in affect was a bad leadership move. McDonald has let its services slip because they thought they could afford to, in return it shows the arrogance in the leadership until they were slapped in the face by their fast growing competitors such as Taco Bell, Pizza Huts and Kentucky Fried Chicken. In addition, there are more subway restaurants than McDonald’s in the U.S. (Grainer, 2003, p.129). Furthermore, the company was mostly focusing on the growth of their real estate than to their quality and service. With every franchise McDonald sold, the more profit that they gained from the rent. They concentrated more on the spread of the franchise than they did on the quality of their service. They should be aware that if this franchisee does not do well, in return it would affect on McDonald’s overall profits.