A Key To The Development Of Information Theory Accounting Essay

Category: Accounting

This chapter discusses and summarizes the literature on all variables under study. The first part of this chapter discusses information theory. The literature is arranged according to the dependent variable and independent variables. The dependent variable that is firm performance is discussed first. The rest of the chapter summarizes all past research on the independent variables. There five independent variables involved in the study: accuracy, consistency, completeness, timeliness and uniqueness. And also this study gives short information what is the difference between Information Quality versus Data Quality.

2.2. Information theory

Central to the concept of information quality is an understanding of the nature of the information. This section explores the basic theory of this agreement. Information theory, developed primarily by Claude Shannon and his colleagues at Bell Labs in 1940 (Shannon, 1948 Shannon and Weaver, 1949). A key to the development of information theory was the new application of the concept of thermodynamic entropy as a representation of uncertainty. According to this theory is that which serves to reduce this uncertainty. One aspect of the information excluded from information theory is the meaning.

Information theory: The work of the twentieth century, the mathematician Claude Shannon is widely regarded as the most influential work in the field of information theory (Avery, 2003; Bovee, 2004; McEliece, 2002). In describing the importance of this work, McEliece observed, while, of course, Shannon did not work in space in 1940, its results were so incredibly original that even the journalists of the day were at a loss understand their meaning. Gradually, as the theorems of Shannon were digested by the community of engineers / math, it became clear he had created a new science, and others have begun to contribute to the first floor of their own. Early research conducted at Bell Labs, Shannon, was focused on the processing of information through a communication channel (Avery; McEliece). As Shannon (1948) describes it, "the fundamental problem of communication is to play at one point either exactly or approximately a message selected at another point.

Among the main contributions of Shannon (1948) was the recognition that information to reduce uncertainty. The uncertainty in this sense is compared with the thermodynamic concept of entropy, where the entropy term was introduced in the study of information theory as well. In a system with two tracks, as a symbol, the entropy is maximum when the probability of encountering each symbol is approximately equal. And Shannon (1948) based his work partly on research by Henry Nyquist, who had examined the ability of information management telegraph lines. Shannon's work extended Nyquist account "where the signal is disturbed by noise during transmission or one or other of the terminals." Noise, in this sense, the increase of entropy in a channel, thus limiting the amount of information the channel can transmit. As Bove (2004) described, emphasized the potential of an original message to reduce the uncertainty between the two states in the system is maximized at this point. He then noted that the information provided in the original version is related to the reduction of uncertainty associated with the received message. The concept of reducing uncertainty was also discussed by Patterson and Handscomb (2004), who described the amount of information that "the relationship between the number of possible responses before and after

receiving the information.

The information theory refers to the study of electronic communications networks, other branches of knowledge has begun to use methods and ideas. For example, using students, psychologists, sociologists, educators and business leaders, the theory of information to learn more about how people relate to each other. Librarians are also trying to improve methods of collecting information, organize and retrieve information using the theory. The company also aims to improve their performance through the provision of high quality information (Shannon, 1948). After reviewing the aspects of the theory that in the latter indicates that there is a relationship between information quality and performance of the company. Whenever the information is accurate, consistent, complete, unique and timeless to help the company to offer excellent service and understanding of the main objectives of the company, which helps to improve the performance of the company.

2.3. Information quality

Quality is a word that has multiple meanings. A prevailing definition is that "quality is the lack of freedom" (Juran, 1988). English (1999) maintains a useful definition of quality as constant as the expectations of "customers, which means that the data does not allow the company to accomplish its mission has no quality, no matter how it is accurate. So there are two different starting points when it comes to quality, focusing on freedom of constraints, and on expectations. This reflects the division (English, 1999) inherent in IQ, describing the shortcomings of freedom on the restrictions of MPC systems planning, policies and procedures, and the connected user (pragmatic) IQ, indicating whether the information meets implicit needs of staff, two important pieces in terms of high performance MPC process. But with different terminology, this division is implemented by several authors in other disciplines (Delone and McLean, 1992;Rai et al, 2002; Rieh, 2002).

For several decades, researchers have also considered various ways to conceptualize and define the size of IQ as the utility, timeliness and importance (Gallagher 1974), accuracy, adequacy, completeness, recovery, access security, and speed, accuracy, timeliness (Bailey and Pearson, 1983). Pazera & Ballou (1985) expanded the definition of IQ beyond accuracy, indicating that IQ also "includes the timeliness, consistency, completeness, relevance and reliability." The researchers used a variety of attributes for the quality of information. Nelson et al. (2005) have used the concepts of accuracy, completeness, and format for the quality of information, in addition to the construction used by these authors involves the layout of the presentation of results of information. The quality of information includes attributes such as consistency, accuracy, completeness and timeliness of information (Bailey and Pearson, 1983, Ives et al, 1983). Other attributes such as accessibility and ability to interpret are also used in the literature on information quality (Wang et al, 1993; Wang et al, 1995). But this study will focus on accuracy, consistency, completeness, uniqueness and timeliness.

quality of information is generally conceived as a multidimensional concept (F. Naumann, 2002) as "employability" may depend on various factors such as accuracy, completeness, relevance, objectivity, credibility, the ability of understanding, coherence, conciseness availability and verifiability (C. Bizer, 2007). The information is characteristic of the quality of products offered by the SI as accuracy, timing, reliability and completeness (Peter and McLean (2009). Quality of information is the basis for all phases of communication in contemporary business (Michnick, 2009). Information quality (IQ) was measured in terms of accuracy, timing, completeness, relevance and coherence (Delone and McLean, 2003). Meanwhile, Michnick (2009) tested the quality of information piece has the quality of information in four areas of assessment aspects are inherent ability, contextual, representation and access to and quality of information.

Quality of information refers to the quality of the results of product information system (Delone and McLean, 1992), which may be in the form of reports and online screens. Huh et al. (1990) defines four dimensions of information quality: accuracy, completeness, consistency and completeness. Precision is an attribute of an agreement with a real world entity, a value stored in another database, or the result of an arithmetic calculation. Completeness is defined relative to a specific application, and refers to the fact that all data relevant to this application are present. Although coherence refers to an absence of conflict between the two sets of data, completeness addresses the question of whether all information is included qualifying.

 The quality of information refers to the consistency, accuracy, completeness and timeliness of information (Ballou and Pazera 1985, 1995, 2002, Chengalur-Smith, Ballou; Pazera 1999, 2007; Wakib Samwel, 2008 and Wang and Strong, 1996).

Accuracy refers to closeness of measured values, observations or estimates of the real or true value, without political or personal bias and manipulation. In other words, accuracy is a measure of the extent to which the data reflect reality. Guiding questions to achieve accuracy relate to the reliability of data sources and the process of data collection ( Samwel Wakib, 2008). And also Accuracy indicates that it is error free, objective and comes from reputable sources, and completeness means the information covers all relevant dimensions (McNair and Carr, 1991; Stair, 1992; Wang and Strong, 1996). According to that Accuracy refers to the degree of conformity between the value actually used and the correct value. For example, if a report provides stock levels for 80 products, only 40 of which are correct, then the accuracy of the communication would be 50 percent (Ballou and Pazer 1985, 1995, 2002; Chengalur-Smith, Ballou, and Pazer 1999, 2007).

Consistency describes the absence of apparent contradictions and is a measure of internal validity and reliability. Guiding questions to assess consistency include the extent to which the same definitions, codes and formats are followed ( Samwel Wakib, 2008). Accorging to that Consistency refers to the underlying formats or processes used to communicate information (Ballou and Pazer 1985, 1995, 2002; Chengalur-Smith, Ballou, and Pazer 1999, 2007).

Completeness refers to lack of errors of omission, such as omitted records in a dataset or a variable without data. Completeness addresses the question of whether all eligible data are included( Samwel Wakib, 2008). Completeness has two components: structural completeness and content completeness. Structural completeness refers to presence of data in each cell. For example, information gathered by means of an online form is structurally complete if all the blanks have been filled. Content completeness refers to the use of the most informative metric for information. For example, if a report converts supplier evaluations based on a 10-point scale into a simple ranking of suppliers, the latter information is less complete (Ballou and Pazer 1985, 1995, 2002; Chengalur-Smith, Ballou, and Pazer 1999, 2007).

Timeliness refers to availability of data when required. Related factors are knowledge about the period when the data were collected, when they were last updated, how long they are likely to remain current and whether they are processed to give information in time to conduct daily business or inform decisions( Samwel Wakib, 2008). According to that Timeliness is the age when information is actually communicated to a user viewed in the context of the data's shelf life. Timeliness relates the age of data to the age sensitivity of the data. The age of the data is calculated by summing the time when it is collected, plus the time it has spent in processing and storage, plus the time required to communicate it to a user. For example, three month old information concerning a supplier's location may be quite acceptable, while three month old information concerning "current" inventory levels is nearly useless.

According to Yang, Diane, Beverly, Richard (2002) they mentioned in their study the quality of information has five characteristics. The high quality information is accuracy, completeness, consistency, uniqueness, and timeliness.

Information needs to be of high quality to be useful and accurate. The information that is input into a data base is presumed to be perfect as well as accurate. The information that is accessed is deemed reliable. Flaws do arise with database design but do not let something in your control, accurate and reliable data, be one of them. A database design that is accurate and reliable will help achieve the development of new business ideas as well as promoting the organizational goals.Â

Completeness is another attribute of high quality information. Partial information may as well be incomplete information because it is only a small part of the picture. Completeness is as necessary as accuracy when inputting data into a database.

Consistency is key when entering information into a database. For example, with a column for a phone number entry 10 digits is the expected length of the field.  Once the fields have been set in the database, a number more or less than 10 digits will not be accepted. The same applies for any field, whether it is an entry that requires a number, a series of numbers, an address, or a name, etc. If the fields are not set to a specific limit for information then consistency is even more important.Â

Uniqueness is the fourth component of high quality information. In order to add value to any organization, information must be unique and distinctive. Information is a very essential part of any organization and if used properly can make a company competitive or can keep a company competitive.Â

A fifth important aspect of information is timeliness. New and current data is more valuable to organizations than old outdated information. Especially now, in this era of high technological advances, out-of-date information can keep a company from achieving their goals or from surviving in a competitive arena. The information does not necessarily need to be out of date to have effect; it just needs to not be the most current. Real-time information is an element of timeliness.

2.4. Information quality (accuracy, consistency, completeness, timeliness and uniqueness)

Literature has begun to emerge in which a conceptual model of quality of information takes shape in the context of information technology. Key concepts are the separation of roles between the operators a variety of information and the identification and definition of various aspects of quality of information (Feltham, 1985). Most research in this period focused on accuracy, consistency, completeness, timeliness, uniqueness, although some have begun to explore other dimensions (Bovee, 2004, Wang et al, 2003). Feltham (1985) noted that the accuracy, consistency, completeness, timeliness, and uniqueness are often referred to as desirable attributes of information. In terms of speed, wrote of delays in reporting and collecting change information until a specified condition or time interval occurs, and then set the value of timeliness in terms of cost / benefit create change in both time signalling or communication range. In this context, his work focused on developing a model to assess the value of a transition to an information system, measures the cost of change and the benefits of change. As such, it represents an early example of literature to assess the quality of information management in terms of costs and benefits.

For most of the last two decades, researchers have explored a variety of ways to conceptualize the quality of information. For example, Gallagher (1984) took into account factors such as usefulness, timeliness, significance and relevance, among others, in determining the value of information systems. Halloran et al. (1988) focused on accuracy, consistency, completeness, timeliness, uniqueness and parameters specified for each of these terms throughout the system. With regard to the accuracy, Halloran et al. writing, an organization can keep statistics on the accuracy of error information. Consistency is defined as the system inputs, transactions, and expenditures in line with current needs of the population and the goals it supports. A few years later, Bailey and Pearson (2000) to measure satisfaction measurement system, the accuracy, consistency, completeness, timeliness, uniqueness, and other similar attributes of quality information.

The most important classifications of quality dimensions are provided by Wand & Wang (2001); Wang & Strong (2003) Redman (2002); and Bovee et al. (2004). By analyzing these classifications, it is possible to define a basic set of information quality dimensions, including 1) accuracy, which means the recorded value conforms to the real world value, 2) completeness, the degree to which values are present (Ballou & Pazer 2000), and focuses on whether all values for a certain variable are recorded, 3) timeliness, which means the recorded value are up-to-date ( Ballou & Pazer 2000; Wang et al.2001; Klein, et al. 2002), and 4) consistency and uniqueness which means representing the values in the same format at all times (Wang & Strong 2003; Redmond 2003; Ballou & Pazer 2000). Given the different characteristics of information quality, logic suggests that certain mixtures of information quality improvements fit more naturally within one information quality or the other, depending on the type of advantage sought. For instance, improvements that focus on delivering more timely and unique information can help an organization respond more quickly to changes in the competitive environment. On the other hand, improvements that focus on maximizing accuracy, consistency, completeness, timeliness and uniqueness of information can help a firm extract more value from its resources (John P. Slone, 2009).

Paul H. Schurr (2003) he draw upon theory found in the information management literature specifically, the information-quality constructs of accuracy, completeness, consistency, and timeliness. These constructs provide useful insights into business-to-business (B2B) relationships that either take place on a Web site or are supported by Web based information system tools. He briefly set the stage with a discussion of relationship development theory.

William and Karriker (2006) similarly recognized that the issue goes beyond accuracy alone, observing that errors can be amplified or diminished by processing and noting that it has become apparent that infromation quality is a relative rather than absolute term . They also explored what they referred to as the accuracy, consistency, completeness, timeliness and proposed a theoretical framework and algorithm for calculating the effect of this trade-off. As they explained it, information regarding some situation or activity at a fixed point in time becomes better with the passage of time. However, as a consequence of the dynamic nature of many environments, the information also becomes less relevant over time. They made a strong case for expanding the scope of information quality beyond a mere focuses on accuracy, stating that other attributes "include timeliness, consistency, completeness, relevance, and consistency. It was not until the mid-1990s that information quality research began to coalesce around a common framework. In particular, Wang et al. (2003) proposed a framework derived from ISO 9000 for use in categorizing data quality research. Wang et al. systematically categorized research on the topic up to that point in time. In addition to the literature cited above, they identified dozens of other articles. Among these, they found numerous examples using different combinations of dimensions, as well as a variety of approaches to the research. Of the dimensions they observed, the ones most commonly occurring were accuracy, timeliness, completeness, uniqueness and consistency.

Differences in the nature of quality between information and physical products can be explained in part by considering specific dimensions of information quality that lack physical counterparts. For instance, as Wang (2001) observed, one could say that a raw material arrived just in time, but one would not ascribe an intrinsic property of timeliness to the raw material. Similarly, dimensions such as believability simply do not have a counterpart in product manufacturing. These differences also manifest themselves in the aggregate, in that for information products, the quality of the individual data items that make up an information product are as important to the consumer as the quality of the overall product (Shankaranarayanan et al., 2000). Another difference between information quality and product quality has to do with the difficulties associated with measuring information, given that information has no physical properties to measure (Redman, 2002). With respect to the accuracy dimension, which Wang and Strong (1999) identified as one of the intrinsic dimensions, accuracy cannot be measured intrinsically; its measurement must always reference something else, such as the real world situation that the data represent (Baskarada, Gao and Koronios, 2006; Wand & Wang, 2003).

In terms of the usage context Baskarada, Gao and Koronios (2006) pointed out a subtle, but important distinction between information quality and physical product quality, namely that most useful data are novel or unique. As a hypothetical example, he considered the absurdity of including genus and species fields in employee records. With every entry identifying the employee as Homo sapiens, the data would be highly accurate, but uninteresting. Instead, it is the uniqueness of the values that makes them interesting. This stands in contrast with most manufacturing processes where one strives for uniformity, and standard measures can be applied. To handle this uniqueness while maintaining quality control, Pierce (2005) suggested the use of automatic range checking or an assortment of feedback mechanisms, such as customer-driven, staff driven, or management-driven feedback, or a combination thereof.

Monczka et al. (2003) stressed that information quality should encompassed the elements of accuracy, consistency, and competency, uniqueness and timeliness of information exchanged. Similar attributes were used in the studies of Li and Lin (2006), Li et al. (2006), Forslund and Jonsson (2007) to measure information quality. Moberg et al. (2002) measured information quality in terms of accuracy, consistency, and competency, uniqueness and timeliness. However, Miller (2005) measured information quality based on accuracy, believability, objectivity, precision and reliability of the information, relevancy, timeliness, completeness and information appropriateness, comprehensibility, interpretability, consistency, conciseness, format and appearance of the information, accessibility , security and availability. These attributes are suitable for service product and to supplement physical products. Inadequate information exchange and poor quality of information seems to have an impact on the effectiveness and efficiency of the supply chain performance.