Julia is a dynamic general-purpose programming language. As a high-level language, distinctive aspects of Julia's design include a type system with parametric polymorphism, the use of multiple dispatch as a core programming paradigm, just-in-time compilation and a parallel garbage collection implementation. Notably, Julia does not support classes with encapsulated methods but instead relies on the types of all of a function's arguments to determine which method will be called. By default, Julia is run similarly to scripting languages, using its runtime, and allows for interactions, but Julia programs can also be compiled to small binary standalone executables (or to small libraries for e.g. Python), with e.g. the JuliaC.jl compiler. Julia programs can reuse libraries from other languages, and vice versa. Julia has interoperability with C, C++, Fortran, Rust, Python, and R. Additionally, some Julia packages have bindings to be used from Python and R as libraries. Julia is supported by programmer tools like IDEs (see below) and by notebooks like Pluto.jl, Jupyter, and since 2025, Google Colab officially supports Julia natively. Julia is sometimes used in embedded systems (e.g. has been used in a satellite in space on a Raspberry Pi Compute Module 4; 64-bit Pis work best with Julia, and Julia is supported in Raspbian). == History == Work on Julia began in 2009, when Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman set out to create a free language that was both high-level and fast. On 14 February 2012, the team launched a website with a blog post explaining the language's mission. In an interview with InfoWorld in April 2012, Karpinski said about the name of the language, Julia: "There's no good reason, really. It just seemed like a pretty name." Bezanson said he chose the name on the recommendation of a friend, then years later wrote: Maybe julia stands for "Jeff's uncommon lisp is automated"? Julia's syntax is stable, since version 1.0 in 2018, and Julia has a backward compatibility guarantee for 1.x and also a stability promise for the documented (stable) API, while in the years before in the early development prior to 0.7 the syntax (and semantics) was changed in new versions. All of the (registered package) ecosystem uses the new and improved syntax, and in most cases relies on new APIs that have been added regularly, and in some cases minor additional syntax added in a forward compatible way e.g. in Julia 1.7. In the 10 years since the 2012 launch of pre-1.0 Julia, the community has grown. The Julia package ecosystem has over 11.8 million lines of code (including docs and tests). The JuliaCon academic conference for Julia users and developers has been held annually since 2014 with JuliaCon2020 welcoming over 28,900 unique viewers, and then JuliaCon2021 breaking all previous records (with more than 300 JuliaCon2021 presentations available for free on YouTube, up from 162 the year before), and 43,000 unique viewers during the conference. Three of the Julia co-creators are the recipients of the 2019 James H. Wilkinson Prize for Numerical Software (awarded every four years) "for the creation of Julia, an innovative environment for the creation of high-performance tools that enable the analysis and solution of computational science problems." Also, Alan Edelman, professor of applied mathematics at MIT, has been selected to receive the 2019 IEEE Computer Society Sidney Fernbach Award "for outstanding breakthroughs in high-performance computing, linear algebra, and computational science and for contributions to the Julia programming language." Version 0.3 was released in August 2014. Both Julia 0.7 and version 1.0 were released on 8 August 2018. Julia 1.4 added syntax for generic array indexing to handle e.g. 0-based arrays. The memory model was also changed. Julia 1.5 released in August 2020 added record and replay debugging support, for Mozilla's rr tool. The release changed the behavior in the REPL (to soft scope) to the one used in Jupyter, but keeps full compatible with non-REPL code (that retains hard scope). Julia 1.6 was the largest release since 1.0, and it was the long-term support (LTS) version for the longest time. Since Julia 1.7 development is back to time-based releases, and it was released in November 2021 with e.g. a new default random-number generator and Julia 1.7.3 fixed at least one security issue. Julia 1.8 added options for hiding source code when compiling Julia source code to executables. Julia 1.9 has added the ability to precompile packages to native machine code, done automatically; to improve precompilation of packages a new package PrecompileTools.jl was introduced, for use by package developers. Julia 1.10 was released on 25 December 2023 with new features such as parallel garbage collection. Julia 1.11 was released on 7 October 2024, and with it 1.10.5 became the next long-term support (LTS) version (i.e. those became the only two supported versions), since replaced by 1.10.10 released on 27 June, and 1.6 is no longer an LTS version. Julia 1.11 adds e.g. the new public keyword to signal safe public API (Julia users are advised to use such API, not internals, of Julia or packages, and package authors advised to use the keyword, generally indirectly, e.g. prefixed with the @compat macro, from Compat.jl, to also support older Julia versions, at least the LTS version). Julia 1.12 was released on 7 October 2025 (and 1.12.5 on 9 February 2026), and with it a JuliaC.jl package including the juliac compiler that works with it, for making rather small binary executables (much smaller than was possible before; through the use of new so-called trimming feature). Julia 1.10 LTS is an officially still-supported branch, but the 1.11 branch has also been maintained after 1.12 release, with 1.11.8 released and then 1.11.9 released on 8 February 2026. === JuliaCon === Since 2014, the Julia Community has hosted an annual Julia Conference focused on developers and users. The first JuliaCon took place in Chicago and kickstarted the annual occurrence of the conference. Since 2014, the conference has taken place across a number of locations including MIT and the University of Maryland, Baltimore. The event audience has grown from a few dozen people to over 28,900 unique attendees during JuliaCon 2020, which took place virtually. JuliaCon 2021 also took place virtually with keynote addresses from professors William Kahan, the primary architect of the IEEE 754 floating-point standard (which virtually all CPUs and languages, including Julia, use), Jan Vitek, Xiaoye Sherry Li, and Soumith Chintala, a co-creator of PyTorch. JuliaCon grew to 43,000 unique attendees and more than 300 presentations (still freely accessible, plus for older years). JuliaCon 2022 will also be virtual held between July 27 and July 29, 2022, for the first time in several languages, not just in English. === Sponsors === The Julia language became a NumFOCUS fiscally sponsored project in 2014 in an effort to ensure the project's long-term sustainability. Jeremy Kepner at MIT Lincoln Laboratory was the founding sponsor of the Julia project in its early days. In addition, funds from the Gordon and Betty Moore Foundation, the Alfred P. Sloan Foundation, Intel, and agencies such as NSF, DARPA, NIH, NASA, and FAA have been essential to the development of Julia. Mozilla, the maker of Firefox web browser, with its research grants for H1 2019, sponsored "a member of the official Julia team" for the project "Bringing Julia to the Browser", meaning to Firefox and other web browsers. The Julia language is also supported by individual donors on GitHub. === The Julia company === JuliaHub, Inc. was founded in 2015 as Julia Computing, Inc. by Viral B. Shah, Deepak Vinchhi, Alan Edelman, Jeff Bezanson, Stefan Karpinski and Keno Fischer. In June 2017, Julia Computing raised US$4.6 million in seed funding from General Catalyst and Founder Collective, the same month was "granted $910,000 by the Alfred P. Sloan Foundation to support open-source Julia development, including $160,000 to promote diversity in the Julia community", and in December 2019 the company got $1.1 million funding from the US government to "develop a neural component machine learning tool to reduce the total energy consumption of heating, ventilation, and air conditioning (HVAC) systems in buildings". In July 2021, Julia Computing announced they raised a $24 million Series A round led by Dorilton Ventures, which also owns Formula One team Williams Racing, that partnered with Julia Computing. Williams' Commercial Director said: "Investing in companies building best-in-class cloud technology is a strategic focus for Dorilton and Julia's versatile platform, with revolutionary capabilities in simulation and modelling, is hugely relevant to our business. We look forward to embedding Julia Computing in the world's most technologically advanced sport". In June 2023, JuliaHub received (again, now
Dominant resource fairness
Dominant resource fairness (DRF) is a rule for fair division. It is particularly useful for dividing computing resources in among users in cloud computing environments, where each user may require a different combination of resources. DRF was presented by Ali Ghodsi, Matei Zaharia, Benjamin Hindman, Andy Konwinski, Scott Shenker and Ion Stoica in 2011. == Motivation == In an environment with a single resource, a widely used criterion is max-min fairness, which aims to maximize the minimum amount of resource given to a user. But in cloud computing, it is required to share different types of resource, such as: memory, CPU, bandwidth and disk-space. Previous fair schedulers, such as in Apache Hadoop, reduced the multi-resource setting to a single-resource setting by defining nodes with a fixed amount of each resource (e.g. 4 CPU, 32 MB memory, etc.), and dividing slots which are fractions of nodes. But this method is inefficient, since not all users need the same ratio of resources. For example, some users need more CPU whereas other users need more memory. As a result, most tasks either under-utilize or over-utilize their resources. DRF solves the problem by maximizing the minimum amount of the dominant resource given to a user (then the second-minimum etc., in a leximin order). The dominant resource may be different for different users. For example, if user A runs CPU-heavy tasks and user B runs memory-heavy tasks, DRF will try to equalize the CPU share given to user A and the memory share given to user B. == Definition == There are m resources. The total capacities of the resources are r1,...,rm. There are n users. Each users runs individual tasks. Each task has a demand-vector (d1,..,dm), representing the amount it needs of each resource. It is implicitly assumed that the utility of a user equals the number of tasks he can perform. For example, if user A runs tasks with demand-vector [1 CPU, 4 GB RAM], and receives 3 CPU and 8 GB RAM, then his utility is 2, since he can perform only 2 tasks. More generally, the utility of a user receiving x1,...,xm resources is minj(xj/dj), that is, the users have Leontief utilities. The demand-vectors are normalized to fractions of the capacities. For example, if the system has 9 CPUs and 18 GB RAM, then the above demand-vector is normalized to [1/9 CPU, 2/9 GB]. For each user, the resource with the highest demand-fraction is called the dominant resource. In the above example, the dominant resource is memory, as 2/9 is the largest fraction. If user B runs a task with demand-vector [3 CPU, 1 GB], which is normalized to [1/3 CPU, 1/18 GB], then his dominant resource is CPU. DRF aims to find the maximum x such that all agents can receive at least x of their dominant resource. In the above example, this maximum x is 2/3: User A gets 3 tasks, which require 3/9 CPU and 2/3 GB. User B gets 2 tasks, which require 2/3 CPU and 1/9 GB. The maximum x can be found by solving a linear program; see Lexicographic max-min optimization. Alternatively, the DRF can be computed sequentially. The algorithm tracks the amount of dominant resource used by each user. At each round, it finds a user with the smallest allocated dominant resource so far, and allocates the next task of this user. Note that this procedure allows the same user to run tasks with different demand vectors. == Properties == DRF has several advantages over other policies for resource allocation. Proportionality: each user receives at least as much resources as they could get in a system in which all resources are partitioned equally among users (the authors call this condition "sharing incentive"). Strategyproofness: a user cannot get a larger allocation by lying about his needs. Strategyproofness is important, as evidence from cloud operators show that users try to manipulate the servers in order to get better allocations. Envy-freeness: no user would prefer the allocation of another user. Pareto efficiency: no other allocation is better for some users and not worse for anyone. Population monotonicity: when a user leaves the system, the allocations of remaining users do not decrease. When there is a single resource that is a bottleneck resource (highly demanded by all users), DRF reduces to max-min fairness. However, DRF violates resource monotonicity: when resources are added to the system, some allocations might decrease. == Extensions == Weighted DRF is an extension of DRF to settings in which different users have different weights (representing their different entitlements). Parkes, Procaccia and Shah formally extend weighted DRF to a setting in which some users do not need all resources (that is, they may have demand 0 to some resource). They prove that the extended version still satisfies proportionality, Pareto-efficiency, envy-freeness, strategyproofness, and even Group strategyproofness. On the other hand, they show that DRF may yield poor utilitarian social welfare, that is, the sum of utilities may be only 1/m of the optimum. However, they prove that any mechanism satisfying one of proportionality, envy-freeness or strategyproofness may suffers from the same low utilitarian welfare. They also extend DRF to the setting in which the users' demands are indivisible (as in fair item allocation). For the indivisible setting, they relax envy-freeness to EF1. They show that strategyproofness is incompatible with PO+EF1 or with PO+proportionality. However, a mechanism called SequentialMinMax satisfies efficiency, proportionality and EF1. Wang, Li and Liang present DRFH - an extension of DRF to a system with several heterogeneous servers. == Implementation == DRF was first implemented in Apache Mesos - a cluster resource manager, and it led to better throughput and fairness than previously used fair-sharing schemes.
Web testing
Web testing is software testing that focuses on web applications. Complete testing of a web-based system before going live can help address issues before the system is revealed to the public. Issues may include the security of the web application, the basic functionality of the site, its accessibility to disabled and fully able users, its ability to adapt to the multitude of desktops, devices, and operating systems, as well as readiness for expected traffic and number of users and the ability to survive a massive spike in user traffic, both of which are related to load testing. == Web application performance tool == A web application performance tool (WAPT) is used to test web applications and web related interfaces. These tools are used for performance, load and stress testing of web applications, web sites, web API, web servers and other web interfaces. WAPT tends to simulate virtual users which will repeat either recorded URLs or specified URL and allows the users to specify number of times or iterations that the virtual users will have to repeat the recorded URLs. By doing so, the tool is useful to check for bottleneck and performance leakage in the website or web application being tested. A WAPT faces various challenges during testing and should be able to conduct tests for: Browser compatibility Operating System compatibility Windows application compatibility where required WAPT allows a user to specify how virtual users are involved in the testing environment.ie either increasing users or constant users or periodic users load. Increasing user load, step by step is called RAMP where virtual users are increased from 0 to hundreds. Constant user load maintains specified user load at all time. Periodic user load tends to increase and decrease the user load from time to time. == Web security testing == Web security testing tells us whether Web-based applications requirements are met when they are subjected to malicious input data. There is a web application security testing plug-in collection for Fire Fox == Web API testing == An application programming interface API exposes services to other software components, which can query the API. The API implementation is in charge of computing the service and returning the result to the component that send the query. A part of web testing focuses on testing these web API implementations. GraphQL is a specific query and API language. It is the focus of tailored testing techniques. Search-based test generation yields good results to generate test cases for GraphQL APIs.
Plug compatibility
Plug compatibility is a characteristic of computer hardware that performs exactly like that of another vendor. Manufacturers who made replacements for IBM peripherals were referred to as plug-compatible manufacturers (PCMs). Later plug-compatible mainframe (also PCM) referred to IBM-compatible mainframe computers. PCM can also mean plug-compatible machine or plug-compatible module. == Plug compatibility and peripherals == Before the rise of the plug-compatible peripheral industry, computing systems were either configured with peripherals designed and built by the CPU vendor or designed to use vendor-selected rebadged devices. The first examples of plug-compatible IBM subsystems were tape drives and controls offered by Telex beginning 1965. Memorex in 1968 was first to enter the IBM plug-compatible disk market, followed shortly thereafter by a number of suppliers such as CDC, Itel, and Storage Technology Corporation. This was boosted by the world's largest user of computing equipment, the US General Services Administration, buying plug-compatible equipment. Eventually there were third-party plug-compatible alternatives to most first-party peripherals and first-party system main memory. == Plug compatibility and computer systems == A plug-compatible machine is one that is backward compatible with a prior machine. In particular, a new computer system that is plug-compatible has not only the same connectors and protocol interfaces to peripherals, but also binary-code compatibility—it runs the same software as the old system. A plug compatible manufacturer, or PCM, is a company that makes such products. One recurring theme in plug-compatible systems is the ability to be bug compatible as well. That is, if the forerunner system had software or interface problems, then the successor must have (or simulate) the same problems. Otherwise, the new system may generate unpredictable results, defeating the objective of full compatibility. Thus, it is important for customers to understand the difference between a bug and a feature, where the latter is defined as an intentional modification to the previous system (e.g. higher speed, lighter weight, smaller package, better operator controls, etc.). === Plug compatibility and IBM mainframes === The original example of plug-compatible mainframes was the Amdahl 470 mainframe computer which was plug-compatible with the IBM System 360 and 370, costing millions of dollars to develop. Similar systems were available from Comparex, Fujitsu, and Hitachi. Not all were large systems. Most of these system vendors eventually left the PCM market. In late 1981, there were eight PCM companies, and collectively they had 36 IBM-compatible models. == Non-computer usage of plug compatibility == Plug compatibility may also be used to describe replacement criteria for other components available from multiple sources. For example, a plug-compatible cooling fan may need to have not only the same physical size and shape, but also similar capability, run from the same voltage, use similar power, attach with a standard electrical connector, and have similar mounting arrangements. Some non-conforming units may be re-packaged or modified to meet plug-compatible requirements, as where an adapter plate is provided for mounting, or a different tool and instructions are supplied for installation, and these modifications would be reflected in the bill of materials for such components. Similar issues arise for computer system interfaces when competitors wish to offer an easy upgrade path. In general, plug-compatible systems are designed where industry or de facto standards have rigorously defined the environment, and there is a large installed population of machines that can benefit from third-party enhancements. Plug compatible does not mean identical. However, nothing prevents a company from developing follow-on products that are backward-compatible with its own early products.
Digital asset
A digital asset is anything that exists only in digital form and comes with a distinct usage right or distinct permission for use. Data that do not possess those rights are not considered assets. Digital assets include, but are not limited to: digital documents, audio content, motion pictures, and other relevant digital data currently in circulation or stored on digital appliances, such as personal computers, laptops, portable media players, tablets, data storage devices, and telecommunication devices. This encompasses any apparatus that currently exists or will exist as technology progresses to accommodate the conception of new modalities capable of carrying digital assets. This holds true regardless of the ownership of the physical device on which the digital asset is located. == Types == Types of digital assets include, but are not limited to: software, photography, logos, illustrations, animations, audiovisual media, presentations, spreadsheets, digital paintings, word documents, electronic mails, websites, and various other digital formats with their respective metadata. The number of different types of digital assets is exponentially increasing due to the rising number of devices that leverage these assets, such as smartphones, serving as conduits for digital media. In Intel's presentation at the 'Intel Developer Forum 2013,' they introduced several new types of digital assets related to medicine, education, voting, friendships, conversations, and reputation, among others. == Digital asset management system == A digital asset management (DAM) is an integrated structure that combines software, hardware, and/or other services to manage, store, ingest, organize, and retrieve digital assets. These systems enable users to find and use content when needed. == Digital asset metadata == Metadata is data about other data. Any structured information that defines a specification of any form of data is referred to as metadata. Metadata is also a claimed relationship between two entities, often used to establish connections or associations. Librarian Lorcan Dempsey says "Think of metadata as data which removes from a user (human or machine) the need to have full advance knowledge of the existence or characteristics of things of potential interest in the environment". At first, the term metadata was used for digital data exclusively, but nowadays metadata can apply to both physical and digital data. Catalogs, inventories, registers, and other similar standardized forms of organizing, managing, and retrieving resources contain metadata. Metadata can be stored and contained directly within the file it refers to or independently from it with the help of other forms of data management such as a DAM system. The more metadata is assigned to an asset the easier it gets to categorize it, especially as the amount of information grows. The asset's value rises the more metadata it has for it becomes more accessible, easier to manage, and more complex. Structured metadata can be shared with open protocols like OAI-PMH to allow further aggregation and processing. Open data sources like institutional repositories have thus been aggregated to form large datasets and academic search engines comprising tens of millions of open access works, like BASE, CORE, and Unpaywall. == Issues == Due to a lack of either legislation or legal precedent, there is limited existing governmental control and regulation surrounding digital assets in the United States and other large economies globally. Many of the control issues relating to access and transferability are maintained by individual companies. Some consequences of this include 'What is to become of the assets once their owner is deceased?' as well as can, and, if so, how, may they be inherited. This subject was broached in a bogus story about Bruce Willis allegedly looking to sue Apple as the end user agreement prevented him from bequeathing his iTunes collection to his children. Another case of this was when a soldier died on duty and the family requested access to the Yahoo! account. When Yahoo! refused to grant access, the probate judge ordered them to give the emails to the family but Yahoo! still was not required to give access. The Music Modernization Act was passed in September 2018 by the U.S. Congress to create a new music licensing system, with the aim to help songwriters get paid more.
Actionstep
Actionstep is a cloud-based legal practice management software for law firms and compliance-focused businesses. Actionstep is built to be a comprehensive practice management software with features for workflow automation as well as automatic document generation == History == Actionstep was created by Ted Jordan, CEO of Actionstep, in 2004. It was first used commercially in 2005 by a New Zealand construction franchise as well as a law firm. Actionstep soon expanded into central government and a wider range of small business users (mainly in New Zealand and Australia). After a few years the expanse of their legal client base prompted the company to add key legal specific features to the product with the aim of further expanding their legal market. Through Actionstep's tenure as a practice management software they have gradually expanded from their headquarters in New Zealand and offices located in the United Kingdom and the United States of America. In October 2020, private equity firm Serent Capital Partners purchased 84.25% stake in Actionstep. In April 2022, the company announced unlimited annual leave to its staff == Product == The premise of Actionstep is that it saves companies from having to purchase software tailored to their work flow and instead allows companies to modify the program without additional coding.{{Citation needed}} The founder and CEO Ted Jordan used cloud technology to allow the software to be continuously updated without the need to purchase or redesign new software. This theoretically allows businesses to remain current all the time and cut external I.T. costs.{{Citation needed}} Actionstep also integrates with software from other companies, such as Xero accounting, Microsoft Office & Office 365, Gmail, Google Drive, Dropbox, NetDocuments, QuickBooks, LawPay, BundleDocs, Box, HotDocs, Infotrack, GlobalX, PEXA, JOSEF and Zapier. Actionstep contains workflow automation features aimed at increasing office efficiency. These automated processes include automatic task assignment, information collection, document generation & automation, cataloguing, and matter generation. == Awards == Actionstep was named First International Best of SaaS Showplace Award Winner in 2009. Actionstep has also been a finalist in the ComputerWorld Excellence Awards (2007), and the Vero Excellence in Business Support (2010).
Fan loyalty
Fan loyalty is the loyalty felt and expressed by a fan towards the object of their fanaticism. Fan loyalty is often used in the context of sports and the support of a specific team or institution. Fan loyalties can range from a passive support to radical allegiance and expressions of loyalty can take shape in many forms and be displayed across varying platforms. Fan loyalty can be threatened by team actions. The loyalties of sports fans in particular have been studied by psychologists, who have determined several factors that help to create such loyalties. == Underpinning psychology == Given the extensive costs involved in managing and operating a professional team sport, it is beneficial for sports marketers to be conscious of the elements that establish a strong brand and the effect they have on fan loyalty, so they can best cater to their current fans while acquiring new ones. This is because fans and spectators are considered key stakeholders of professional sports organisations. Fans directly and indirectly influence the production of operating revenue through purchasing merchandise, buying game tickets and improving the value that can be obtained from television broadcasting deals and sponsorship. Therefore, fans are a key factor to consider in determining the economic success of a sports club. Deep psychological connections with new teams can be built with individuals before a team has even played a match revealing insights can develop quickly in the mind of consumers without direct encounters or experiences e.g. watching a team compete. Brand management approaches are helping sport organisations to expand the sport experience, appeal to new fans and enable long term business to consumer relationships through multi faceted connection such as social media. To affect consumers’ loyalty with a team, they must develop a compelling, positive and distinctive brand in order to stand out amongst competitor and vie for fan support. Loyalty programmes positively shape fan attachment and behaviour as it connects teams and their fans, aside from a club's season ticketholder database. It not only provides marketers with essential information about consumers and their thinking, but also acts as a channel to promote attendance and an opportunity to add value to their game day experience. Bauer et al. concludes that non product related attributes such as contextual factors (other fans, the club history and tradition, logo, club colours and the stadium atmosphere) hold a higher place in fan experience than product related attributes such as the team's winning record. Therefore, to increase fan loyalty (customer retention) Bauer et al. suggests sports marketers focus on targeting non product related benefits and brand attributes. As a result of fostering this loyalty, sports organisations can afford to charge prices at premium. Fan loyalty also leads to dependable ratings in broadcast media which means broadcasters can also charge premiums for advertising time in team broadcasts with loyal followings. A flow on effect from fan loyalty is the ability to create additional revenue streams outside of the core product such as merchandise shops and food venues that are close to the location of the game if the team chooses to own and operate ventures or share licensing agreements. Fan loyalty, particularly with respect to team sports, is different from brand loyalty, in as much as if a consumer bought a product that was of lower quality than expected, he or she will usually abandon allegiance to the brand. However, fan loyalty continues even if the team that the fan supports continues to perform poorly year after year. Author Mark Conrad uses the Chicago Cubs as an example of a team with a loyal fan following, where fans spend their money in support of a poorly performing team that (until 2016) had not won a pennant since 1945 or a World Series since 1908. They attribute it to the following factors: Entertainment Value The entertainment value that a fan derives from spectating motivates him/her to remain a loyal fan. Entertainment value of team sports is also valuable to communities in general. Authenticity This is described by Passikoff as "the acceptance of the game as real and meaningful". Fan Bonding Fan bonding is where a fan bonds with the players, identifying with them as individuals, and bonds with the team. Team History and Tradition Shank gives the Cincinnati Reds, all-professional baseball's oldest team, as an example of a team where a long team history and tradition is a motivator for fans in the Cincinnati area. Group Affiliation Fans receive personal validation of their support for a team from being surrounded by a group of fans who also support the same team. Fair Weather Fans Fans that engage when a team is good, and lose interest when a team is bad. Bandwagon Fans Fans who support the winning team, instead of supporting the same team year after year. Diehard Fans Fans who follow their team no matter if they are winning or losing. == Factors influencing fan loyalty == === Community === Fan loyalty attachment is strengthened through communal ties that connect fans around a team, forming a community that results in regular fan interaction. This interaction is particularly important as fans may not develop solely an intra-psychic team identity but predominantly display behavioural loyalty through the group consumption of indirect sport experiences instead, such as wearing the team colours, singing, cheering, flags and interaction between the sport's team's fans (e.g. laughing, talking) Through indirect sport experiences, the stadium atmosphere can be heightened and as a result, the frequency of fan attendance can increase. Furthermore, by wearing team apparel, fans can visually identify with one another resulting an increased likelihood of opportunities to engage with others socially through this point of connection. For example, a study on NASCAR fans found that their personal identity was connected to the brand itself as they felt connected to the larger community of NASCAR revealing an emotional connection to the brand. This indicates that their fan loyalty will result in the notion that fans are naturally more resistant to the promotional efforts of competing brands (e.g. lower-price offers) as their emotional commitment to NASCAR is greatly embedded in their sense of identity. When they associate themselves with the sponsors because of the sponsor's relation to the brand, they are solidifying their relationship with NASCAR and are therefore reinforcing their identity. Consequently, their fan loyalty translates into brand loyalty so long as the sponsor remains attached to the subject of their fanaticism, NASCAR, meaning they are less price sensitive and more willing to pay premium prices for sponsor's products or services. Another aspect of consumer behaviour regarding fan loyalty is the existence of consumption communities where members feel a sense of unity when they participate in the group consumption of brand sponsors’ goods and services further strengthening their ties to a brand and its sponsors. However, a strategy sports marketers use to appeal to a wider range of fan identities is to sponsor more than one club in sports such as soccer. This is so they are careful not to come across as a singularly affiliated club brand, where the opinion or perceptions of opposing teams’ fans would be one of disfavour towards them. === Brand association === Any benefit or characteristic connected to a brand as perceived by a consumer is called a brand association. These hold significance over the thoughts and opinions a consumer holds about a brand and can therefore influence one's loyalty. These associations provide a reference point to gauge the salience of a brand which is the perceived favourability associated with it. Brand salience is vital because it ultimately effects the likelihood of brand selection and loyalty leading to steadier spectator numbers, and an increase in attention from the media such as advertisers and sponsors. However, loyalty is a developmental process. According to Bee & Havitz (2010), spectators who are highly involved in the participation of a sport and exhibit psychological commitment, possess the capability to display high levels of behavioural loyalty as they develop into committed fans. On the other hand, neutral or negative feelings towards a team are found to foster indifference or cause an individual to disidentify with a team altogether. A model of ‘escalating commitment’, put forward by Funk and James (2001), demonstrates an individual's movement from ‘awareness’ of team to a subsequent ‘allegiance’ but came to the conclusion that more research was required to find out the key influences that lead one to the highest state of commitment. However, brand association development is fostered under brand management within a sports organisation. It is important for sports management research to identify t