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Information Processing Model of Executive Decision
Abstract
Company information systems demand a better understanding of the nature of the executive's decision process. Above all his information requirements must be specified. The capacity to specify these requirements is the hallmark of the information-processing model. An example of the form of the simulation of a pricing decision process is presented. The model, a binary flow chart type, simulates the pricing decision in a differentiated oligopolistic market. The validity of the model is established by comparing the two kinds of predictions that it makes--output and process--with reality. A number of problems encountered in developing models of this type are discussed. Some of the broader implications of these models are described and others are briefly mentioned.
... However, while including strategy as a political process, behavioral pricing theory goes beyond this perspective to include cognitive science theory and findings especially on how executives process information into knowledge and how they create and apply highly useful heuristics (i.e., rules on how-to-decide usually leading to desirable outcomes) in selecting choices outcomes (e.g., specific price points and increases/decreases in prices). Examples of such cognitive science advances in behavioral pricing in business-to-business contexts include the studies by Morgenroth (1964), Howard and Morgenroth (1968), Woodside and Wilson (2000), and Woodside (2003). These B2B studies and additional studies in business-toconsumer contexts (e.g., Woodside, Schpektor, & Xia, 2013) support the conclusion that the general theory of pricing is an insightful and useful blending of cognitive science, economics, marketing, psychology, and implemented practices in explicit contexts. ...
... The findings from the wholesale pricing study by Morgenroth (1964) and Howard and Morgenroth (1968) Fig. 5. Wholesale Pricing of Petroleum. Key: P = price; t = time, at present; PA = price analyst; r = retail; (t + l) = time, subsequent to considering price change; w = wholesale; Q = quantity; ≠ is not equal to or is different from; x = our company; l = local market, wherein price change being considered is greater than; o→ is other major competitor in local market; n = nearby market with funnel influences; ↑ = raise price; x = our price; o = other major competitor initiator; DSO = district sales office; ↓ = drop price. ...
... Such findings in behavioral pricing as in Fig. 1 by Woodside and Wilson (2000) and Fig. 5 by Morgenroth (1964) and Howard and Morgenroth (1968) support the causal asymmetry stance for theory development and theory testing. Relying solely on symmetric testing tools such as MRA and structural equation modeling does not reflect the reality of asymmetric relationships in behavioral pricing. ...
... However, while including strategy as a political process, behavioral pricing theory goes beyond this perspective to include cognitive science theory and findings especially on how executives transform information into knowledge and how they create and apply useful algorithms (i.e., rules on how-to-decide that usually lead to desirable outcomes) in selecting choices outcomes (e.g., acceptable specific price-points and increases/decreases in prices). Examples of such cognitive science advances in behavioral pricing in business-to-business contexts include the studies by Morgenroth (1964), Howard and Morgenroth (1968), Joskow (1973), Woodside and Wilson (2000), and Woodside (2003). These B2B studies and additional studies in business-to-consumer contexts (e.g., Woodside, Schpektor, & Xia, 2013) support the conclusion that the general theory of behavioral pricing is an insightful and useful blending of cognitive science, complexity theory, economics, marketing, psychology, and implemented practices in explicit contexts. ...
... The development of ethnographic pricing models using the price-collusion original data set awaits the researcher willing to wade into the court records and the FBI (U.S. Federal Bureau of Investigation) files-the multiple decision processes and outcomes in these processes that are available over a five-year period. Such research on decision processes of price setting and changes in B2B contexts rarely is available but the literature does include example studies (e.g., Howard & Morgenroth, 1968;Morgenroth, 1964;Woodside & Wilson, 2000). ...
... In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy" (Gigerenzer & Brighton, 2009, p. 107). Morgenroth (1964) and Howard and Morgenroth (1968) describe the use of holdout samples for testing for predictive validity and the achievement of high predictive validity for parsimonious algorithms in B2B pricing decisions. Gigerenzer and Brighton (2009) describe how, "In the 1970s, the term "heuristic" acquired a different connotation, undergoing a shift from being regarded as a method that makes computers smart to one that explains why people are not smart. ...
Building behavioral-pricing models-in-contexts enriches one or more goals of science and practice: description, understanding, prediction, and influence/control. The general theory of behavioral strategy includes a set of tenets that describes alternative configurations of decision processes and objectives, contextual features, and beliefs/assessments associating with different outcomes involving specific price-points. This article explicates these tenets and discusses empirical studies which support the general theory. The empirical studies include the use of alternative data collection and analytical tools including true field experiments, think aloud methods, long interviews, ethnographic decision-tree-modeling, and building and testing algorithms (e.g., fuzzy-set qualitative comparative analysis). The general theory of behavioral pricing involves the blending of cognitive science, complexity theory, economics, marketing, psychology, and implemented practices. Consequently, behavioral pricing theory is distinct from context-free microeconomics, market-driven, and competitor-only price-setting. Capturing and reporting contextually-driven alternative routines to price setting by a compelling set of tenets represents what is particularly new and valuable about the general theory. The general theory serves as a useful foundation for advances in pricing theory and improving pricing practice.
... The core focus of the present chapter is to advocate formulating parsimonious theoriesdescriptions, explanations, and predictions of patterns in phenomena-and to show research method fundamentals for testing such theories. Implemented decision rules by firms are parsimonious patterns which are operational algorithms (e.g., Howard and Morgenroth 1968;Morgenroth 1964); related to consumer research profiles of buyers are examples of parsimonious patterns. ...
... However, while including strategy as a political process, behavioral pricing theory goes beyond this perspective to include cognitive science theory and findings especially on how executives transform information into knowledge and how they create and apply useful algorithms (i.e., rules on how-to-decide that usually lead to desirable outcomes) in selecting choices outcomes (e.g., acceptable specific price-points and increases/decreases in prices). Examples of such cognitive science advances in behavioral pricing in business-to-business contexts include the studies by Morgenroth (1964), Howard and Morgenroth (1968), Joskow (1973), Wilson (2000), andWoodside (2003). These B2B studies and additional studies in business-to-consumer contexts (e.g., ) support the conclusion that the general theory of behavioral pricing is an insightful and useful blending of cognitive science, complexity theory, economics, marketing, psychology, and implemented practices in explicit contexts. ...
... In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy" (Gigerenzer and Brighton 2009, p. 107). Morgenroth (1964) and Howard and Morgenroth (1968) describe the use of holdout samples for testing for predictive validity and the achievement of high predictive validity for parsimonious algorithms in B2B pricing decisions. ...
Prior research on the association between corporate social performance (CSP) and corporate financial performance (CFP) includes conflicting perspectives and inconclusive findings as to whether or not CSP has a positive, negative, or neutral association with CFP. While Wang et al. (2015) meta-analysis confirms the relationship between CSP and CFP to be significant and positive, in some contexts CSP and CFP associate negatively; CSP may need to receive “good management” support to yield positive financial outcomes [Luo and Bhattacharya (J Mark 73:198–213, 2009)]. The study here tests and supports the perspective that “good management” occurs in configurations (i.e., business models) with high CSP to indicate high CFP. A configurational theoretical stance implies that recipes of bad management with high or low CSP are likely to associate with low CFP. Configurational analysis supports this theoretical perspective. Building from complexity theory, a configurational analysis includes the propositions that complex multiple recipes lead to the same outcome (equifinality tenet) whereby variables (ingredients) found to associate causally in one configuration may be absent in another recipe or even inversely related in a third recipe associated with this same outcome. The present study employs a mixed methods research design (using surveys of senior executives, independent CSP firm assessments using ESG factors (environment, social (or human rights), and governance), and analysis of corporate annual reports of 82 mostly highly-global Swedish firms). The study overcomes the mismatch between case-level theory proposals and variable-based data analyses that is widespread in the relevant literature. The study’s findings support the core tenets of complexity theory.
... Recipes of antecedents to using bad practices are likely to include combinations of the following features: lack of experience (most scholars submitting most papers are likely to submit twenty or fewer studies based on completing twenty different data files in their lifetimes); lack of training beyond building and testing theories centering on the net effects of independent variables on a single dependent variable; modeling their own research behavior by reading published studies exhibiting several bad practices; and having zero to very limited exposures to the relevant literature on adopting good practices in behavioral/business research (e.g., here are some primary sources that include exceptional insights and advice for designing and implementing good practices in research and data analysis: Armstrong, 2012;Campbell & Stanley, 1963;Dillman et al., 2014;Eskin & Baron, 1977;Feldman & Lynch, 1988;Gigerenzer & Brighton, 2009;Golder, 2000;Howard & Morgenroth, 1968;Levitt & List, 2007;McClelland, 1998;Nisbett & Wilson, 1977;Ordanini, Parasuraman, & Rubera, 2014;Ragin, 2008;Sawyer & Ball, 1981;Shadish, Cook, & Campbell, 2002;Whyte, 1984). ...
... The usefulness of a model depends on its predictive accuracy using additional samples to test for accuracy. It is a point that a few iconic studies emphasize (e.g., Armstrong, 2012;Brighton & Gigerenzer, 2015;Gigerenzer & Brighton, 2009;Howard & Morgenroth, 1968;McClelland, 1998;Morgenroth, 1964) but ignored pervasively in most articles using MRA in all journals. Fig. 5 illustrates the bad practice of reporting fit validity only as well as how overfitting a model (i.e., using 5 or more terms in a regression model) generates the false illusion of a good model. ...
... Consider using second and third rounds of asking questions separately of the same respondents to clarify responses and deepen knowledge about answers. Morgenroth (1964) completed 10 rounds of interviews of different persons in the same firm along with completing direct observations and document analyses in his iconic study of how executives in a firm make pricing decisions (see also Howard & Morgenroth, 1968). ...
... Recipes of antecedents to using bad practices are likely to include combinations of the following features: lack of experience (most scholars submitting most papers are likely to submit twenty or fewer studies based on completing twenty different data files in their lifetimes); lack of training beyond building and testing theories centering on the net effects of independent variables on a single dependent variable; modeling their own research behavior by reading published studies exhibiting several bad practices; and having zero to very limited exposures to the relevant literature on adopting good practices in behavioral/business research (e.g., here are some primary sources that include exceptional insights and advice for designing and implementing good practices in research and data analysis: Armstrong, 2012;Campbell & Stanley, 1963;Dillman et al., 2014;Eskin & Baron, 1977;Feldman & Lynch, 1988;Gigerenzer & Brighton, 2009;Golder, 2000;Howard & Morgenroth, 1968;Levitt & List, 2007;McClelland, 1998;Nisbett & Wilson, 1977;Ordanini, Parasuraman, & Rubera, 2014;Ragin, 2008;Sawyer & Ball, 1981;Shadish, Cook, & Campbell, 2002;Whyte, 1984). ...
... The usefulness of a model depends on its predictive accuracy using additional samples to test for accuracy. It is a point that a few iconic studies emphasize (e.g., Armstrong, 2012;Brighton & Gigerenzer, 2015;Gigerenzer & Brighton, 2009;Howard & Morgenroth, 1968;McClelland, 1998;Morgenroth, 1964) but ignored pervasively in most articles using MRA in all journals. Fig. 5 illustrates the bad practice of reporting fit validity only as well as how overfitting a model (i.e., using 5 or more terms in a regression model) generates the false illusion of a good model. ...
... Consider using second and third rounds of asking questions separately of the same respondents to clarify responses and deepen knowledge about answers. Morgenroth (1964) completed 10 rounds of interviews of different persons in the same firm along with completing direct observations and document analyses in his iconic study of how executives in a firm make pricing decisions (see also Howard & Morgenroth, 1968). ...
... However, while including strategy as a political process, behavioral pricing theory goes beyond this perspective to include cognitive science theory and findings especially on how executives process information into knowledge and how they create and apply highly useful heuristics (i.e., rules on how-to-decide usually leading to desirable outcomes) in selecting choices outcomes (e.g., specific price points and increases/decreases in prices). Examples of such cognitive science advances in behavioral pricing in business-to-business contexts include the studies by Morgenroth (1964), Howard and Morgenroth (1968), Woodside and Wilson (2000), and Woodside (2003). These B2B studies and additional studies in business-toconsumer contexts (e.g., Woodside, Schpektor, & Xia, 2013) support the conclusion that the general theory of pricing is an insightful and useful blending of cognitive science, economics, marketing, psychology, and implemented practices in explicit contexts. ...
... The findings from the wholesale pricing study by Morgenroth (1964) and Howard and Morgenroth (1968) Fig. 5. Wholesale Pricing of Petroleum. Key: P = price; t = time, at present; PA = price analyst; r = retail; (t + l) = time, subsequent to considering price change; w = wholesale; Q = quantity; ≠ is not equal to or is different from; x = our company; l = local market, wherein price change being considered is greater than; o→ is other major competitor in local market; n = nearby market with funnel influences; ↑ = raise price; x = our price; o = other major competitor initiator; DSO = district sales office; ↓ = drop price. ...
... Such findings in behavioral pricing as in Fig. 1 by Woodside and Wilson (2000) and Fig. 5 by Morgenroth (1964) and Howard and Morgenroth (1968) support the causal asymmetry stance for theory development and theory testing. Relying solely on symmetric testing tools such as MRA and structural equation modeling does not reflect the reality of asymmetric relationships in behavioral pricing. ...
The general theory of behavioral strategies includes a set of propositions supporting alternative configurations of objectives, contextual features, and beliefs/assessments by executives. The theory includes the outcomes of selections of specific decision alternatives. Building behavioral-strategy models in contexts enriches one or more goals of science and practice: description, understanding, prediction, and influence/control. This chapter is a primer to the general theory. A brief review of relevant empirical studies supports the general theory. The empirical studies include the use of alternative data collection and analytically tools including true field experiments, think aloud methods, long interviews, statistical hypothesis testing, ethnographic decision tree modeling, and building and testing algorithms (e.g., qualitative comparative analysis, QCA). The general theory is the blending of cognitive science, economics, marketing, psychology, and implemented practices in explicit contexts. Consequently, behavioral-strategy theory is distinct from context-free microeconomics, market-driven, and competitor-only decision-making. Capturing and reporting contextually driven alternative routines to strategy setting by a compelling set of propositions represents what is particularly new and valuable about the general theory. The general theory serves as a useful foundation for advances theory and improving the practice of implemented strategies.
... In Figure 1, the positive and negative linkages, the arrows, between nodes are casual propositions. The causal propositions illustrated are developed from reviews of the economic 6 and marketing literatures concerning price (e.g., see Ferrell, Lucas, Bush 1989;Nagle 1984;Rao 1984;Tellis 1986;Monroe 1990;Narasimhan, Ghosh, and Mendez 1993;Simon 1989) and the literature relating price and strategic management/marketing issues (e.g., Howard and Morgenroth 1968;Farley, Hulbert, and Weinstein 1980;Hall 1984;Hall 1992) . Figure 1 is a summary of a pricing system model presented in a system flow diagram. ...
... Increases in competitiveness of the marketplace (box 6) reduces the pricing strategist's focus on profit as the firm's principal pricing objective, and increases his/her focus on sales and market share. Increasing competitiveness causes the likelihood of price wars to occur (e.g., see Morgenroth 1964;Howard and Morgenroth 1968). If competitiveness increases in a relatively small geographic market in the form of substantial special price offers (e.g., a promotion by an airline), the pricing strategist may decide to attempt to signal his/her competitors by responding with a smaller price move; however, when a major competitor's, special price offers spread to larger markets, the focus on protecting market share in these larger markets becomes paramount, and this increase in importance of market share causes the strategist to match, or beat, the competitor's price decreases. ...
... The Exhibit is a brief case study that includes a discussion of three alternatives in pricing a new fencing product. In reading the Exhibit many of the policy concepts related to Behavioral pricing models in industrial marketing are found in studies by Howard and Morgenroth (1968);Farley, Hulbert, and Weinstein (1980), and Woodside (1992b). ...
The article studies price behavior of oligopolies in industrial market where price competition is replaced by non-price competition. There is a developed technology for pricing management of the products of industrial enterprises, which, unlike the existing ones, takes into account the dynamics of changes in consumer preferences and changes in the pricing policy of the enterprise competitor and is based on usage of system dynamics models to simulate the financial and economic performance of enterprises and the fuzzy model for situational analysis and decisionmaking on changes in prices for the products. A pricing simulation model is offered. It is based on system-dynamic modeling method, which takes into account the complex cause-to-effect concatenation of factors on price such as product quality, cost, price competition, price elasticity of economic demand, competitors’ quantity of output and estimates the impact of changing factors of internal and external enterprise environment on the effectiveness of its activities.The simulation model allows us to conduct diverse experiments and analyze the impact of management decisions on the efficiency of the enterprise. Based on the fuzzy approach a price decision-making model is developed. It operates not only precise (numeric) values, but also qualitative assessments of variables and provides an adequate use of logical relationships and the laws of the mutual influence of market and production and economic factors. Qualitative dependences, which establish the influence of external and internal factors on the price change, are identified as a result of the study of economic laws and legal conformity that are in the context of rapid economic change and market turbulence may not be strictly formalized and take the form of linguistic statements, which express the conditional relationship between the qualitative assessments of initial factors and changes in the relative price.
... Recipes of antecedents to using bad practices are likely to include combinations of the following features: lack of experience (most scholars submitting most papers are likely to submit twenty or fewer studies based on completing twenty different data files in their lifetimes); lack of training beyond building and testing theories centering on the net effects of independent variables on a single dependent variable; modeling their own research behavior by reading published studies exhibiting several bad practices; and having zero to very limited exposures to the relevant literature on adopting good practices in behavioral/business research (e.g., here are some primary sources that include exceptional insights and advice for designing and implementing good practices in research and data analysis: Armstrong, 2012;Campbell & Stanley, 1963;Dillman et al., 2014;Eskin & Baron, 1977;Feldman & Lynch, 1988;Gigerenzer & Brighton, 2009;Golder, 2000;Howard & Morgenroth, 1968;Levitt & List, 2007;McClelland, 1998;Nisbett & Wilson, 1977;Ordanini, Parasuraman, & Rubera, 2014;Ragin, 2008;Sawyer & Ball, 1981;Shadish, Cook, & Campbell, 2002;Whyte, 1984). ...
... The usefulness of a model depends on its predictive accuracy using additional samples to test for accuracy. It is a point that a few iconic studies emphasize (e.g., Armstrong, 2012;Brighton & Gigerenzer, 2015;Gigerenzer & Brighton, 2009;Howard & Morgenroth, 1968;McClelland, 1998;Morgenroth, 1964) but ignored pervasively in most articles using MRA in all journals. Fig. 5 illustrates the bad practice of reporting fit validity only as well as how overfitting a model (i.e., using 5 or more terms in a regression model) generates the false illusion of a good model. ...
... Consider using second and third rounds of asking questions separately of the same respondents to clarify responses and deepen knowledge about answers. Morgenroth (1964) completed 10 rounds of interviews of different persons in the same firm along with completing direct observations and document analyses in his iconic study of how executives in a firm make pricing decisions (see also Howard & Morgenroth, 1968). ...
... When possible, collecting multiple instances of making decisions for the same and different contexts within the same firm or interfirm situation is one method of attaining sufficient data for constructing algorithm and/or regression models. The studies by Morgenroth (1964;Howard and Morgenroth, 1968) and Woodside and Wilson (2000) are examples of such "within subjects" design within business-to-business marketing contexts. See Figure 1 Some researchers have created tools for improving the usefulness and theoretical confirmability of single-shot case studies. ...
... However, Üstüner spent time in the field (lengthy face-to-face time in one firm) for her Ph.D. dissertation work under the supervision of Thompson. Such research is possible for research in interfirm contexts (e.g., Morgenroth's (1964) dissertation-based study under the direction of John Howard; see also, Howard and Morgenroth, 1968). Urry (2005) provides a far-ranging literature review of complexity theory in the natural and social sciences and offers many useful insights. ...
... Price decreases risk the initiation of price wars. Morgenroth (1964) and Howard and Morgenroth (1968) provide additional details for the algorithms as well as findings for testing the predictive validity with a holdout sample of the firm's pricing decisions. ...
Purpose: This article describes the principal limitations frequently observable in variable-based and case-based research in business-to-business (B-to-B) marketing. Focus: The limitations relate to B-to-B theory construction, data collection, and data analysis and interpretation. The principal limitations in variable-based research include the reliance on regression (net effects) models in theory construction and the pervasive failure to test resulting empirical regression models for predictive validity. The principal limitations of case-based research are the frequent absence of rigorous theory formulation either pre- or postdata collection and failures to test propositions for predictive validity. Recommendations: The article directs attention to iconic studies that break free in part from these and additional limitations. The article offers useful steps on how to break free completely from the principal limitations. Managerial implications: B-to-B executives should insist that researchers include asymmetric algorithm models in reports that include symmetric (regression analysis) and analysis of variance findings.
... However, while including strategy as a political process, behavioral pricing theory goes beyond this perspective to include cognitive science theory and findings especially on how executives transform information into knowledge and how they create and apply useful algorithms (i.e., rules on how-to-decide that usually lead to desirable outcomes) in selecting choices outcomes (e.g., acceptable specific price-points and increases/decreases in prices). Examples of such cognitive science advances in behavioral pricing in business-to-business contexts include the studies by Morgenroth (1964), Howard and Morgenroth (1968), Joskow (1973), Woodside and Wilson (2000), and Woodside (2003). These B2B studies and additional studies in business-to-consumer contexts (e.g., Woodside, Schpektor, & Xia, 2013) support the conclusion that the general theory of behavioral pricing is an insightful and useful blending of cognitive science, complexity theory, economics, marketing, psychology, and implemented practices in explicit contexts. ...
... The development of ethnographic pricing models using the price-collusion original data set awaits the researcher willing to wade into the court records and the FBI (U.S. Federal Bureau of Investigation) files-the multiple decision processes and outcomes in these processes that are available over a five-year period. Such research on decision processes of price setting and changes in B2B contexts rarely is available but the literature does include example studies (e.g., Howard & Morgenroth, 1968;Morgenroth, 1964;Woodside & Wilson, 2000). ...
... In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy" (Gigerenzer & Brighton, 2009, p. 107). Morgenroth (1964) and Howard and Morgenroth (1968) describe the use of holdout samples for testing for predictive validity and the achievement of high predictive validity for parsimonious algorithms in B2B pricing decisions. Gigerenzer and Brighton (2009) describe how, "In the 1970s, the term "heuristic" acquired a different connotation, undergoing a shift from being regarded as a method that makes computers smart to one that explains why people are not smart. ...
... M ANY marketing decisions are made in complex environments where numerous variables affect decision outcomes; market response to these variables is frequently nonlinear and incorporates carryover effects. Since decision making under these conditions is a difficult cognitive task, managers often rely on simple heuristics such as setting advertising budgets as a fixed percentage of sales instead of structuring the problem to analyze and evaluate alternative courses of action (e.g., Howard andMorgenroth 1968, March andSimon 1958). ...
... Since decision making under these conditions is a difficult cognitive task, managers often rely on simple heuristics such as setting advertising budgets as a fixed percentage of sales instead of structuring the problem to analyze and evaluate alternative courses of action (e.g., Howard andMorgenroth 1968, March andSimon 1958). ...
This paper presents a comparative analysis of the findings of two field studies and three recent laboratory experiments that assessed the efficacy of judgment based models in aiding marketing decision making. This analysis indicates factors that may affect the effectiveness of these models. The implications of the findings for users of judgment based marketing decision models as well as model builders are discussed, and suggestions are made for future research to improve the models’ effectiveness.
... The study of wholesale price changes by Howard and Morgenroth (1968) (hereafter Howard & Morgenroth, 1968) and Morgenroth (1964) is an early example of asymmetric modeling with testing for predictive validity.Both articles report in-depth on the same study.The study included a triangulation of data collection: multiple face-to-face interviews of the same persons participating in pricing decisions in a specific context, direct observation, document analysis, and confirmatory interviews with additional executives in the same firm who Morgenroth (1964, p. 19). Table 1 Cross-tabulation of quintiles of cases for core self-evaluations (summed CSE averages) and job satisfaction. ...
... In addition, 130 other decisions in other divisions of the Company were used to test the model (Morgenroth, 1964: 21). Table 1 appearing in both Morgenroth (1964) and in Howard and Morgenroth (1968) reports perfect agreement for the predicted and observed outputs."Hence, the hypothesis that the model represents the executive's decision process is confirmed by the output test" (Howard & Morgenroth, 1968: 424). ...
LaPlaca alone and with colleagues has contributed > 100 insightful studies focusing in advancing science in industrial marketing management (IMM).His body of work constitutes a remarkable scholarly legacy in identifying the milestones in advances in the pursuit of scientific contributions in IMM and in informing scholars of what the field must tackle successfully to achieve scientific legitimacy-that is, engagement in a true paradigm shift, one that advances discovery in this area from sheer descriptive analysis and reporting to the development of explanatory schemata and theoretical frameworks of a kind that allow for accurate prediction of underlying B2B phenomena.The present article is a tribute offering to LaPlaca for this central insight and LaPlaca's body of work generally.The essay here identifies research advances in theory and analytics that contribute successfully to the primary need LaPlaca and colleagues identify for IMM to achieve scientific legitimacy.An explosion in case-based predictive model-building occurred in the teen years of the 21st century that responds to the challenge of achieving accurate prediction of B2B phenomena.A few scholars became renowned through their scholarly contributions, LaPlaca is one of these few because of his mapping of IMM paradigm shifts taking the field closer to the overarching objective of scientific legitimacy.
... Sociology was one of the theoretical units believed to contribute to AI study (Musés, 1962), and sociological scientists began to respond to technological developments through sociological studies (Apter, 1970). Organizations as a field of sociological study have begun to be perceived as one aspect of AI study (Howard & Morgenroth, 1968;Mallen, 1970). The specialized sociological study of artificial intelligence (AI) began in the early 1980s. ...
This research aims to identify potential areas for future sociological research related to artificial intelligence (AI). The study used bibliometric analysis methods and the VosViewer pro- gram to process data. The data analyzed included 31 articles related to "sociology" and "artificial intelligence," and 1,277 articles pertinent to "social" and "artificial intelligence," all published on ScienceDirect between 2003 and 2023. Network visualization, overlay, and density analysis were used to process the data. This revealed that current sociological re- search on AI only covers five topics - artificial intelligence, sociology, technology, affects, and artificial intelligence. However, social research on AI has identified 100 topics across five datasets, with almost all research being conducted within the past decade. It is noteworthy that "sociology" is not among these 100 topics. However, these 100 topics have the potential to become sociological research subjects by applying sociological principles. The research findings suggest that sociologists can publish their scientific documents in 3,800 journals and books published by Elsevier, indicating a high probability of acceptance. Furthermore, the topics can be framed from a sociological perspective, thus providing greater insight on the subjects and potentially opening up the door to more publications by the sociologists.
... Process or Practice: Strategy as a process or practice (Paroutis, Heracleous, and Angwin, 2013) has a strong root in behavioral sciences. For example, "behavioral strategy" concept merges cognitive and social psychology with strategic management theory and practice (Powell et al. 2011(Powell et al. , p. 1371, and cognitive science theory (Howard and Morgenroth, 1968;Morgenroth, 1964;Woodside, 2003;Woodside and Wilson, 2000) in that the executives process information into knowledge by the use of heuristics in selecting choices. ...
This is a book about strategic management, which is based on the research works of the author. The book has more than 1,000 cited references. The readers i.e. students, research scholars, business decision makers, government policy makers, will be introduced to a unique “MOST” framework of strategic management that exploits the different views and ideologies of strategies, as organized inventories of cognition, in the strategic analysis or research processes, in order to arrive at strategic choices and a robust business model for winning in the marketplace. Four empirical research examples are illustrated. Both the undergraduate and post-graduate students of business majors would benefit significantly from this comprehensive compendium of strategy formation and management knowledge.
The book is arranged in sixteen chapters. Chapter 1 is the introduction. Chapter 2 presents various nature and characteristics of strategy and introduces the “MOST” model to strategic management. Chapter 3 provides some theoretical positions of the “MOST” model. Chapter 4 discusses the various “Ps” nature of strategy, including introducing a P-based strategic management framework. The detailed skeleton of the “MOST” is covered in Chapter 5. Chapter 6 overviews some important strategy tools. In Chapter 7, different nature of strategic choice is presented. Chapter 8 provides the evaluation criteria for assessing the strategic choice. Chapter 9 presents various elements of strategy implementation. Chapter 10 covers business model, including a subsection of CBT (Community-based Tourism) which is the research of the author and colleague Dr. Busaba Sitikarn. Chapter 12 overviews the research method for strategy formulation. Chapters 13-16 present four empirical research reports in various aspects of strategic management. In essence, The “MOST” model is an example of the “systemic” approach to strategic management. The word “systemic” is linked to “systems”, which is premised on a philosophy that no idea can be fully understood until it is incorporated into an organized field of knowledge. Thus, the “MOST” model is an effort made to arrive at an organized body of knowledge, which has been vastly neglected in the extant literature. The overall systemic configuration of the “MOST” strategic management model is shown in the following conceptual framework.
Operatively and tactically, being “systemic” in nature embeds and embodies a learning-centric driving force in play. Operatively, continuous performance improvement – a key success attribute often associated with world class practice and management – has been concluded to be achievable only when an organization monitors its progress constantly, based on measured facts, depicting the “MOST” model in continuous feedback loop system. Otherwise, organizational performance would be created at random. The tactical domain concerns the subtleties of the complex relationships of relevant variables embedded in the system of strategic measurement, and the roles the variables could play in ensuring reliability and accountability. This depends on the daily reproduction of performance expectations as depicted in the single-loop learning mechanism, and also depends on the ability to learn beyond the confines of the present knowledge profiles of the organization. Tactical insight, therefore, provides a rich explanation for performance measurement and strategic management systems.
A research-oriented, also known as organizational intelligence development, approach to strategic formulation is emphasized in this book. According to Rumelhart and Norman (1981), real learning involves accretion, or the encoding of new information in terms of existing schemata, restructuring of schema, schema creation and the process whereby new schemata are created, tuning or schema evolution, or the modification and refinement of a schema as a result of using it in different situations (Shuell, p. 91), leading to introducing a research-oriented approach to strategic management in this book.
On similar counts, Johnson (2011) studies how active learners learn better than passive learners when they are subjected to a context that is meaningfulness, which is facilitated by the diversified views and ideologies of strategy discussed in Chapter 2. The “MOST” model is a platform of a meta-communication of firms. The “reflexive” nature of organizations and the social world leads to the concept of treating strategic management as a meta-communication, and thus, to succeed in the meta-communication, the “MOST” model establishes an effective communication process to relate between the organization and the environments. Whether inductive or deductive in the research approach to strategy formations, managers use mental representations that mirror the reality, as perceived through the senses (Barnes, 1984) or some statistical instrument and phenomenological structure of understanding (Tan, 2016), to interpret external environmental information (Feeney and Handley, 2006) as well as internal and socio-psychological information (Tan, 2016). In other words, in the view of cognitive science, strategists’ cognition mediates between the external environmental and their responses to environmental stimuli (Moors and De Houver, 2006; Karakaya and Yannopoulos, 2010), as shown in the “MOST” model of strategic management.
... This DSA method was used in this study to formalize the process of interviewing managers involved in different stages of the decision-making process. Previous studies (Howard and Morgenroth, 1968;Kaynak and Ghauri, 1994;and Ronkainen, 1985) have emphasized that the purpose of DSA is to describe the flowchart of the decision-making process. The steps of the decision-making process provide an opportunity to consider and evaluate changing needs, conditions, and alternatives. ...
This study was conducted with the objective of evaluating the influence of knowledge management on the research capacity of university lecturers in Hanoi. First, the decision system analysis method was used for the decision-making process of the knowledge management model through three rounds of interviews with 15 experts from universities, research institutes, and scientific journals. Five factors, namely knowledge creation, knowledge collection, knowledge sharing, knowledge application, and knowledge management with big data, were then identified. A survey was conducted with 388 participants, and the collected data were analyzed through partial least squares structural equation modeling. Accordingly, the knowledge management model with five factors that affect the motivation of research and the research capacity of lecturers at universities in Hanoi was evaluated, and the significant values of the reliability and suitability of the model were accepted, showing that the proposed model is suitable. The test results showed that knowledge creation, knowledge collection, knowledge sharing, and knowledge management with big data had a significant relationship with the faculty's research capacity. The analysis revealed that knowledge creation, knowledge application, and knowledge management with big data had a significant impact on the motivation of lecturers for research. The results also showed the direct impact of research motivation on lecturers' research capacity. Furthermore, the indirect relationships between knowledge creation, knowledge sharing, knowledge collection, and knowledge management with big data and research capacity through research motivation is a noteworthy finding of this study.
... According to the upper echelons theory, younger executives are more willing to take risks toward novel practices whereas older executives might prefer being risk averse by embracing conservative managerial approaches (Hambrick and Mason, 1984). From another supportive perspective, which is the information-processing theory (Howard and Morgenroth, 1968), younger executives can better integrate and process critical information so that they make more effective decisions with confidence compared to those of older executives. Thus, we can suggest that the CEO age has important implications on the overall firm performance. ...
Corporate spin-offs have been a major “preferred” restructuring technique in the previous couple decades in the U.S. This corporate transaction aims to create value for both divesting firm and its subsidiary. This study examines an understudied interaction of CEO external directorships and age (as well as their direct effects) in the strategy literature on the change in market valuation of spun-off subsidiaries. By drawing our cases from the SDC Platinum database, we identified 138 completed corporate U.S. spin-offs that took place between 2000 and 2014. Our empirical analysis indicates that the number of CEO external directorships as well as having a younger CEO positively and significantly affect the change in market valuation. In addition, our interaction effect shows significant results. Grounded in the upper echelons and resource dependence theories, this study contributes to the corporate governance literature in terms of understanding whether two particular CEO characteristics and their interactions hold a great deal of importance for spun-off subsidiaries’ market performance. From the perspective of managerial implications, this study suggests that having a younger CEO along with holding many external directorships will help these spun-off subsidiaries much better perform in the market.
... The maps are not direct personal representations of cognitive processes but ''intermediate tools that facilitate the discussion of cognitive processes that can never be directly observed'' (Eden, Jones, Sims, & Smithin, 1979, in Huff & Jenkins, 2002; mental processes that include tacit knowledge that is hard to verbalize. Cognitive mapping is a much applied research method in organizational contexts, encouraging organizational members to share their idiosyncratic sensemaking of organizational reality, giving insights into actual values, beliefs, and behaviors guiding behaviors in their immediate social context (e.g., Howard & Morgenroth, 1968;Huff & Jenkins, 2002;Woodside & Samuel, 1981). ...
... Both sets of researchers could advance their contributions to science by adding "computing with words" (CWW) and asymmetric modeling with predictive validity testing to their toolboxes. The contributions to asymmetric theory and data analysis by Gladwin (1989aGladwin ( ), (1989b; Gladwin, Peterson, and Uttaro;Gladwin, Peterson, and Uttaro, (2002) and Morgenroth (1964), Howard and Morgenroth, (1968) are relevant examples of CWW that provide knowledge structures in causal comprehensions of events and behavior permitting formal forecasting model construction and testing. ...
This article describes why and how to end bad science practices and shallow information-from-data reporting by embracing a seemingly minor but quite a radically different research paradigm that includes asymmetric, configurational-focused case-outcome theory construction and somewhat precise outcome testing. This radically different paradigm provides accurate point or interval estimates of case-outcomes. As an illustration of this paradigm, the exposition in this article includes an elaboration based on additional analyses of data in research appearing in a recent issue of this journal.
... Both sets of researchers could advance their contributions to science by adding "computing with words" (CWW) and asymmetric modeling with predictive validity testing to their toolboxes. The contributions to asymmetric theory and data analysis by Gladwin (1989aGladwin ( ), (1989b; Gladwin, Peterson, and Uttaro;Gladwin, Peterson, and Uttaro, (2002) and Morgenroth (1964), Howard and Morgenroth, (1968) are relevant examples of CWW that provide knowledge structures in causal comprehensions of events and behavior permitting formal forecasting model construction and testing. ...
... Both sets of researchers could advance their contributions to science by adding "computing with words" (CWW) and asymmetric modeling with predictive validity testing to their toolboxes. The contributions to asymmetric theory and data analysis by Gladwin (1989aGladwin ( ), (1989b; Gladwin, Peterson, and Uttaro;Gladwin, Peterson, and Uttaro, (2002) and Morgenroth (1964), Howard and Morgenroth, (1968) are relevant examples of CWW that provide knowledge structures in causal comprehensions of events and behavior permitting formal forecasting model construction and testing. ...
... Perhaps a good starting point could be laboratory experiments with simple paradigms such as those presented by Rapoport, Guyer, and Gordon (1976), but where the decision making is in a marketing context. The work by Cyert, Feigenbaum, and March (1959), Howard and Morgenroth (1968), and Hogarth and Makridakis (1981) on information processing under competitive conditions, as well as the experimental work in economics (Plott 1982;Smith 1982), is also very relevant in this regard. ...
The authors review a representative cross-section of analytical models of competition using a framework for competitive analysis developed from a marketing perspective. Models are classified by the substantive issues they address and by the underlying modeling strategy. The implications of the various models are presented and discussed, and a set of empirically testable propositions is given. Some substantive areas that have been neglected by analytical modelers are identified. Other issues that warrant research attention by marketing behavioral scientists and econometricians are suggested as opportunities for future research.
... The first two requirements might be approached through study of a company's existing information system. Any such study must recognize major organizational issues [5] and decision processes [3,6,7], from which the method to be described in this article is derived. ...
... Funds were also provided by the Division of Research of the Graduate School of Business Administration at UCLA. product shopping decisions. Information processing models have been successfully applied to other areas of decision making in economics [4,8], although such models of the behavior of particular individual consumers have received little research effort (see [1] for a model of shopping for women's clothing, and [9] for a model framework, however). ...
Using the Newell, Shaw, and Simon postulates for an information processing theory of human problem solving, decision net models were constructed for two individual consumers’ choices of grocery products. The models were tested against actual data, and the resulting predictions were highly accurate.
... The core focus of the present essay is to advocate formulating parsimonious theories-descriptions, explanations, and predictions of patterns in phenomena-and to show research method fundamentals for testing such theories. Implemented decision rules by firms are parsimonious patterns which are operational algorithms (e.g., Howard & Morgenroth, 1968;Morgenroth, 1964); related to consumer research profiles of buyers are examples of parsimonious patterns. ...
Currently, most of the empirical management, marketing, and psychology articles in the leading journals in these disciplines are examples of bad science practice. Bad science practice includes mismatching case (actor) focused theory and variable-data analysis with null hypothesis significance tests (NHST) of directional predictions (i.e., symmetric models proposing increases in each of several independent X’s associates with increases in a dependent Y). Good science includes matching case-focused theory with case-focused data analytic tools and using somewhat precise outcome tests (SPOT) of asymmetric models. Good science practice achieves requisite variety necessary for deep explanation, description, and accurate prediction. Based on a thorough review of relevant literature, Hubbard (2016) concludes that reporting NHST results (e.g., an observed standardized partial regression betas for X’s differ from zero or that two means differ from zero) are examples of corrupt research. Hubbard (2017) expresses disappointment over the tepid response to his book. The pervasive teaching and use of NHST is one ingredient explaining the indifference, “I can’t change just because it’s [NHST] wrong.” The fear of submission rejection is another reason for rejecting asymmetric modeling and SPOT. Reporting findings from both bad and good science practices may be necessary until asymmetric modeling and SPOT receive wider acceptance than held presently.
... The study of wholesale price changes by Howard andMorgenroth (1968) (hereafter H&M, 1968) and Morgenroth (1964) is an early example of asymmetric modeling with testing for predictive validity. Both articles report in-depth on the same study. ...
This chapter identifies research advances in theory and analytics that contribute successfully to the primary need to be filled to achieve scientific legitimacy: configurations that include accurate explanation, description, and prediction – prediction here refers to predicting future outcomes and outcomes of cases in samples separate from the samples of cases used to construct models. The MAJOR PARADOX: can the researcher construct models that achieve accurate prediction of outcomes for individual cases that also are generalizable across all the cases in the sample? This chapter presents a way forward for solving the major paradox. The solution here includes philosophical, theoretical, and operational shifts away from variable-based modeling and null hypothesis statistical testing (NHST) to case-based modeling and somewhat precise outcome testing (SPOT). These shifts are now occurring in the scholarly business-to-business literature.
... For this reason, knowledge on the topic is either scarce or requires further validation. For example, Howard and Morgenroth (1968) developed a model of a price-decision process based on the decisions of just one executive. A more recent conceptual framework proposed by Ailawadi et al. (2009) indicates a potential difference in the priorities that manufacturers and retailers might pursue: manufacturers aim to maximize profits for their brands and company, whereas retailers aim to maximize store, category, and private-label profits as well as shopper satisfaction. ...
Consumer price promotions account for more than half of many manufacturers' marketing budgets, and require a significant time investment to manage. Amidst the considerable research on price promotions, little academic attention has been paid to how manufacturers and retailers make price-promotion decisions. Based on in-depth interviews with a broad range of managers, this study investigates factors that influence price-promotion decisions in durable and consumer goods industries. Findings suggest that (1) intuition and untested assumptions are the main inputs into these decisions; (2) practitioners lack solid empirical evidence to guide their actions, and their beliefs are often in stark contrast with academic knowledge about the effectiveness of price promotions; and (3) price promotions are typically not evaluated against the objectives according to which they were justified, impeding appropriate feedback for future decisions. Research priorities are outlined to advance evidence-based decision-making in this area.
... The core focus of the present essay is to advocate formulating parsimonious theories-descriptions, explanations, and predictions of patterns in phenomena-and to show research method fundamentals for testing such theories. Implemented decision rules by firms are parsimonious patterns which are operational algorithms (e.g., Howard & Morgenroth, 1968;Morgenroth, 1964); related to consumer research profiles of buyers are examples of parsimonious patterns. ...
This chapter describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e., 2–7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g., high A, high B, and low C) – and low X is an indicator of high Y as well when low X combines in other recipes (e.g., high A, low R, and high S), where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities – configural analysis – is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.
This chapter describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e., 2–7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g., high A, high B, and low C) – and low X is an indicator of high Y as well when low X combines in other recipes (e.g., high A, low R, and high S), where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities – configural analysis – is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.
... For this reason, knowledge on the topic is either scarce or requires further validation. For example, Howard and Morgenroth (1968) developed a model of a price-decision process based on the decisions of just one executive. A more recent conceptual framework proposed by Ailawadi et al. (2009) indicates a potential difference in the priorities that manufacturers and retailers might pursue: manufacturers aim to maximize profits for their brands and company, whereas retailers aim to maximize store, category, and private-label profits as well as shopper satisfaction. ...
... Especially as the B2B-world is dominated by pricing processes rather than price optimization in the microeconomic sense.'' The brilliant study of pricing processes in gasoline markets by Howard and Morgenroth (1968) is illustrative of modeling with testing of the predictive validity of such pricing processes. In VP Kent Monroe applies Nimer's perspective in reviewing a substantial number of pricing process studies. ...
A rare and valuable combination of practical advice, theoretical modeling, and descriptive explanation of pricing decisions is the summary thought that best captures the overall contribution of Visionary Pricing (VP). VP builds principally on Dan Nimer's great foresight that pricing cannot be the sole domain of finance or any singe functional area and in offering a paradigm shift from cost-plus pricing to customer-value-based pricing.
... major competitors (neither i or x) Q expected physical volume of salesFigure 7.3 An inductive pricing decision model for firms in a distribution channel Source: Adapted fromHoward and Morgenroth (1968). ...
... The maps are not direct personal representations of cognitive processes but ''intermediate tools that facilitate the discussion of cognitive processes that can never be directly observed'' (Eden, Jones, Sims, & Smithin, 1979, in Huff & Jenkins, 2002; mental processes that include tacit knowledge that is hard to verbalize. Cognitive mapping is a much applied research method in organizational contexts, encouraging organizational members to share their idiosyncratic sensemaking of organizational reality, giving insights into actual values and beliefs guiding behaviors in their immediate social context (e.g., Howard & Morgenroth, 1968;Huff & Jenkins, 2002;Woodside & Samuel, 1981). ...
This chapter examines the topic of internal branding from an organizational/behavioral science perspective, theoretically and empirically investigating how organizational members actually enact corporate brands. A mixed-method research procedure serves to surface conscious (i.e., deliberate) and unconscious (i.e., tacit) internal brand meaning enactments in an internationally operating Austrian corporate business-to-business (B2B) brand. The results are an evidence of the potential complexity of real-life internal branding processes that limit the possibility of achieving a cohesive intended internal implementation of corporate brands. The chapter concludes with the managerial implication that purposeful managerial interventions necessitate an understanding of the social system that is the target of the internal branding initiative.
... Perhaps a good starting point could be laboratory experiments with simple paradigms such as those presented by Rapoport, Guyer, and Gordon (1976), but where the decision making is in a marketing context. The work by Cyert, Feigenbaum, and March (1959), Howard and Morgenroth (1968), and Hogarth and Makridakis (1981) on information processing under competitive conditions, as well as the experimental work in economics (Plott 1982;Smith 1982), is also very relevant in this regard. ...
The authors review a representative cross-section of analytical models of competition using a framework for competitive analysis developed from a marketing perspective. Models are classified by the substantive issues they address and by the underlying modeling strategy. The implications of the various models are presented and discussed, and a set of empirically testable propositions is given. Some substantive areas that have been neglected by analytical modelers are identified. Other issues that warrant research attention by marketing behavioral scientists and econometricians are suggested as opportunities for future research.
Several authors have suggested evaluation of marketing research information by Bayesian analysis. This suggestion is based on two assumptions: (1) the marketing manager is able to supply valid inputs for the analysis and (2) the essence of the evaluation problem can be captured in the Bayesian analysis framework. This note is a review of recent studies into behavioral decisionmaking that challenge these two assumptions.
Methodologies for analysis of industrial marketing decision systems are extended to allow systematic comparisons over decisions and over firms. Three such systems used by two firms to price and plan volumes for the French market are compared.
A concept to be considered initially is that of Taylor in the twentieth century, where the scientific basis of administration is technological in nature. It is believed that the best way to increase production is by improving the techniques or methods used by employees. With this theory, Taylor treated employees as machines that could be manipulated by their leaders. Another theory of scientific management is related to the development of an organization in the most rational way possible, to create efficiency in management and increase production. Reformulation and restructuring of functions have been carried out to achieve greater efficiency, satisfying the economic interests of the workers through various incentive plans. Clearly, here, the role of the leader is to establish and enforce performance criteria to meet the objectives of the organization. The role of the leader is to pay attention to the organization as a whole and not to the people who were there. In 1925, the tendency was born in relation to the theory of Mayo that initiates the substitution of those standards by the movement of human relations. Greater attention is beginning to be paid to human relations, as well as to seek, through the best technological methods, to increase production. It begins to support the idea that the real centres of power in an organization were interpersonal relationships, which develop within the unit of work. Thus, the theory that the role of the leader was to facilitate the cooperative achievement of the objects among the leaders, giving opportunities for their personal growth and development and personal needs becomes the focus. In short, the scientific management movement emphasizes concern with "task" (production), while the human relations movement highlights concern about "human relations" (people). With the combination of these two concerns were structured the new theories on Leadership. Some aspects of these theories begin to be studied, as is the case of Tannenbaum's theory (1970), which speaks of the styles adopted by the leaders. This and other authors of the time spoke of autocratic and democratic leaders, claiming that before these studies they were defined as autocratic because they were much more concerned with production. They
Purpose
The purpose of this paper is to investigate the underlying knowledge structure and evolution of industrial-buying research published between 1965 and 2015.
Design/methodology/approach
Bibliometric analysis is performed on 357 relevant papers (using principal component analysis and natural language processing, using VantagePoint® tools, used to generate bubble maps, auto-correlation maps and Aduna cluster maps), demonstrating how various factors involved in industrial buying have evolved, their degree of correlation with each other and the interrelationships of multiple factors concerning their co-occurrences.
Findings
The systematic mapping of industrial-buying research would illustrate the development of the significant factors in industrial-buying research. This paper provides both a global perspective on the leading countries and journals in the field and a robust roadmap for further investigation in this field.
Research limitations/implications
This paper is limited to the data considered for analysis and may, therefore, overlook or underestimate some work that has not been captured while filtering databases related to industrial buying.
Practical implications
This paper facilitates near-future projection and trend analysis in industrial-buying research.
Originality/value
The methodology used is unique to the field of business-to-business marketing.
Eine zweite Richtung innerhalb der modernen Organisationstheorien ist durch den systemtheoretischen Bezug ihrer Organisationsaussagen geprägt. Die systemtheoretische Orientierung geht auf das Instrumentarium oder die Sprache der Allgemeinen Systemtheorie zurück. Diese liefert einen formalmethodologischen Ansatz zur einheitlichen Erfassung materiell unterschiedlicher Systeme. Mit der Entwicklung einer Allgemeinen Systemtheorie waren und sind zwei Erkenntnisinteressen verbunden1). Erstens: die zu Anfang der Entwicklung verfolgte Absicht einer Integration und Vereinheitlichung der Wissenschaften und Einzeldisziplinen über gemeinsame Merkmale unterschiedlicher Systeme2). Zweitens: eine spezifische Erforschung eines Wissensgebietes unter Anwendung des systemtheoretischen Instrumentariums. Der Wandel des Forschungsinteresses zugunsten der zweiten Entwicklungsrichtung ermöglichte der Betriebswirtschaftslehre im allgemeinen und der betriebswirtschaftlichen Organisationsieb re im speziellen eine Betrachtung ihres Objektes unter systemtheoretischem Aspekt3).
Chapter 2 describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e., 2–7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g., high A, high B, and low C)—and low X is an indicator of high Y as well when low X combines in other recipes (e.g., high A, low R, and high S), where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities—configural analysis—is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.
This chapter points out that the use of a wide range of theoretical paradigms in marketing research requires researchers to use a broad range of methodologies. As an aid in doing so, the chapter argues for the use of case study research (CSR), defines CSR, and describes several CSR theories and methods that are useful for describing, explaining, and forecasting processes occurring in business-to-business (B2B) contexts. The discussion includes summaries of six B2B case studies spanning more than 60 years of research. This chapter advocates embracing the view that learning and reporting objective realities of B2B processes is possible using CSR methods. CSR methods in the chapter include using multiple interviews (2 + ) separately of multiple persons participating in B2B processes, direct research and participant observation, decision systems analysis, degrees-of-freedom analysis, ethnographic-decision-tree-modeling, content analysis, and fuzzy-set qualitative comparative analysis (fs/QCA.com). The discussion advocates rejecting the dominant logic of attempting to describe and explain B2B processes by arms-length fixed-point surveys that usually involve responses from one executive per firm with no data-matching of firms in specific B2B relationships – such surveys lack details and accuracy necessary for understanding, describing, and forecasting B2B processes.
Dem Preis wurde bisher von allen Absatzinstrumenten wissenschaftlich die meiste Beachtung geschenkt. Vor allem die Volkswirtschaftslehre hat sich intensiv mit der Frage des Zustandekommens von Preisen und mit dem (gewinn-)optimalen unternehmerischen Preisverhalten bei verschiedenen Marktformen auseinandergesetzt. Die betriebswirtschaftliche Preistheorie, wie sie etwa von Gutenberg formuliert wurde, entspricht im wesentlichen den preistheoretischen Aussagen der Mikroökonomie. Die Grundannahmen der mikroökonomischen Preistheorie1) sind jedoch so einschränkend, daß daraus keine für die praktische betriebliche Preisfestsetzung brauchbaren Entscheidungsregeln abgeleitet werden können.2) Es ist daher sicherlich nicht übertrieben festzustellen, daß eine geschlossene entscheidungstheoretische Konzeption einer Theorie der unternehmerischen Angebotspreisfestsetzung, also einer “normativen Theorie der betrieblichen Preispolitik” heute noch fehlt. Eine solche betriebliche Preispolitik müßte, wenn sie leistungsfähig sein soll, unseres Erachtens von einer Typologie praktisch relevanter Probleme der (Angebots-) Preisfestsetzung ausgehen und für diese Problemtypen Modellstrukturen entwickeln, die flexibel genug sind, für den Einzelfall modifiziert werden zu können.
Überblickt man die ungewöhnlich reiche Literatur zu dem im Thema angesprochenen Problemkreis, so mag es scheinen, daß die Gelegenheit eines Übersichtsvortrags Anlaß für einen Sermon zum Erntedankfest böte, wie dies zudem der Kalender nahelegt. Als bekannte Tatsache darf wohl gelten, daß auch die Wissenschaften nicht frei von Saisonschwankungen bzw. Konjunkturzyklen sind. Ich bedauere daher, mögliche Erwartungen zum Inhalt meines Referats enttauschen zu müssen. Es wird - aus gutem Grund - eher einer Fastenpredigt (allerdings nicht einer Gardinenpredigt) gleichen müssen. Antizyklisches Verhalten zum wissenschaftlichen Trend tut meines Erachtens not, wie noch zu zeigen sein wird, nicht euphorische Schönfärberei und selbstzufriedene Beflissenheit.
In einem Übersichtsvortrag anläßlich der Jahrestagung der Schweizerischen und der Deutschen Gesellschaft für Operations Research vom September 1975 in Interlaken hatte ich Klage über die verhältnismäßig geringe Zahl von Operations-Research-Studien in meinem Arbeitsgebiet geführt1). Zwar hat es seither und auch früher nicht an matheatisch-theoretischen Beiträgen von hohem Rang zu absatzpolitischen Themen gemangelt, aber sie verfehlen zumeist im Kern sowohl das, was nach allgemeiner Auffassung “Marketing“ ausmacht, als auch den zentralen Ansatzpunkt von Operations Research: die Lösung komplexer empirischer Probleme. Die Klage wäre somit im Grunde erneut zu führen. Es damit bewenden zu lassen, lohnt indessen nicht. Im Talmud steht geschrieben, daß der ein Held sei, der seinen Trieb beherrsche. Daran möchte ich mich halten. Im folgenden soll daher weniger den subjektiven als den objektiven Gründen für eine Entwicklung nachgespürt werden, die offenbar in eine Sackgasse geführt hat. Der Stand wissenschaftlicher Erkenntnis, vorhandene Lücken und mögliche Weiterentwicklungen lassen sich (daran anschließend) umso genauer kennzeichnen. Nicht zuletzt soll dadurch auch ein konstruktiver Beitrag geleistet werden.
In this book, we have emphasized models that can be and have been used. This implies that at some point in time somebody in the organization has decided whether or not to go ahead with a model building project. That is, the project has been, or should have been, evaluated on a cost-benefit basis and the expected benefits sufficiently exceeded the expected costs, a justification for going ahead with the project. Of course some companies might decide on model building simply because competitors use models, or just because model building is the in thing to do. Here we will be more concerned with the rational decision maker who wants to explore whether using a model as decision support for solving real problems can be justified on economic grounds.
Here, an "icon" refers to a study widely and critically admired, a study symbolizing a movement or field of activity; iconic marketing studies offer exceptional contributions to marketing theory and/or data collection, and/or data analysis, and/or interpretations/implications of qualitative and/or quantitative empirical findings. This article identifies antecedent conditions associated with achieving iconic status in research in marketing and proposes 10 tenets useful for identifying and planning iconic studies in marketing. The study reviews examples of iconic studies in the marketing literature. The study also addresses a few telling mistakes that researchers in the marketing discipline frequently make. The study of iconic research is helpful for crafting high-quality theory and planning high-quality research designs, as well as increasing vigilance and skill in identifying truly exceptionally high-quality studies and studies that are plainly just bad. The essay briefly reviews 10 JGSMS articles appearing also in this virtual issue as possible candidates for the achievement of iconic status in marketing.
An ethnographic approach was used to develop flow diagrams of the information processes and decision making stages of corporate and plant purchasing personnel in developing corporate purchasing agreements with suppliers. Participant observations of the processes used to develop corporate purchasing agreements were conducted along with extensive personal interviews of plant buyers at seven plant locations of Epsilon Corporation-a multinational electronics firm with headquarter offices in New York City. The results indicate that valid and useful descriptions can be provided of the information processes and decisions actually used to produce corporate purchasing agreements. Several diagnostic comments are provided to each of the four phases in the processes used to develop corporate purchasing agreements.
Purpose
– The purpose of this paper is to help interfirm-collaborating cluster (ICC) executives examine the relevance of alternative decision rules in practical business contexts. Multi-party-implemented strategies and establishing multi-lateral collaborations are necessary actions for achieving success in new product development by small and medium enterprises (SMEs). This study explores interfirm decision-making heuristics relating to industrial ICCs.
Design/methodology/approach
– The study examines the relevancy to decision making in ICCs of heurstics such as “fast-and-frugal decision trees” (FFDTs) and “take-the-best” (TTB) to processing possibly influential decision-making cues. The study also examines simple heuristics versus the value of a “fully rational” approach to making decisions – calculating cue values, importance weights, multiplying values by weights, summing and selecting the option having the highest summed score. This study included interviewing executives of the pivotal firm in an ICC.
Findings
– This study reveals a decision-making solution for shortening the time and processes required in seeking new business collaboration partners in an ICC. This study not only develops a FFDT for six decision-making modules to quickly identify potential collaboration partners, but it also constructs a decision systems analysis (DSA) flowchart to effectively shorten the decision-making process.
Research limitations/implications
– This study is in accordance with the general type of industrial interfirm collaboration in Taiwan. The industrial interfirm collaboration could be further divided into the types of formal, semi-formal and informal industrial interfirm collaborations.
Practical implications
– This study argues that firms usually find it difficult to develop their own technology because of the high costs of research and development for SMEs. Therefore, firms need to collaborate with partners to maintain their competitive advantage. However, to collaborate, firms must learn to trust their collaboration partners, and the degree of collaboration also strongly depends on the degree to which they trust their collaboration partners.
Originality/value
– This study provides the efficient models of FFDT and DSA to quickly identify potential collaboration partners and to effectively shorten decision-making processes.
Business-to-business pricing research has often focused on developing rational and normative frameworks and models for pricing issues, strategies and tactics. However, there has been less attention given to behavioral models that help us understand the realities of pricing and how managers actually set prices. Specifically, there has been less attention given to the various individual and group influences on the price setting process. We apply insights from a steadily increasing body of literature on behavioral decision making to identify some relevant behavioral issues that may affect managerial price setting processes in business-to-business contexts. We conclude with some implications for theory building and practice and an agenda for future research.
This study presents a theory of causally complex configurations of antecedent conditions influencing the adoption versus non-adoption of international supplier ethical certification-standards. Using objective measures of antecedents and outcomes, a large-scale study of exporting firms in the cut-flower industry in two South American countries (Colombia and Ecuador) supports the theory. The theory includes the following and additional propositions. No single (simple)-antecedent condition is sufficient for accurately predicting a high membership score in outcome conditions; the outcome conditions include a firm’s adoption or rejection of a product certification. No single (simple)-antecedent condition is necessary for accurately predicting high scores in the outcome condition. A few complex antecedent conditions (configurations) are sufficient but the occurrence of each is not necessary for accurately predicting high scores (e.g., adoption) in an outcome condition. Causal asymmetry of antecedent conditions indicating adoption versus non-adoption of specific ethical standards occurs—that is, causal conditions leading to rejection are not the mirror opposites of causal conditions leading to adoption.
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