Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is moderate to high heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (a) Are the correlation matrices homogeneous? (b) Do the proposed models fit the data? (c) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.
181-200 of 282 Results
Article
Meta-Analytic Structural Equation Modeling
Mike W.-L. Cheung
Article
Micro-Foundations of Institutional Logic Shifts: Entrepreneurial Action in Response to Crises
Trenton Alma Williams
Institutional logics shape how actors interpret and organize their environment. Institutional logics include society’s structural, normative, and symbolic influences that provide organizations and individuals with norms, values, assumptions, and rules that guide decision-making and action. While institutional logics influence individuals and organizations, they do not exist/form in a vacuum, but rather are instantiated in the practices and patterned behaviors of actors who act as carriers of logics in specific contexts. Given the dynamic interaction between institutional logics and individual/organizational actors, research has begun to explore the micro-level processes that influence institutional logic changes to help explain how and why those who are shaped by an institution enact changes in the very context in which they are embedded. Thus, the formation and changing of institutional logics involve precipitating action—which can include entrepreneurial action. Entrepreneurial action—especially in moments of crisis (e.g., when there is a disruptive event, natural disaster, or external feature that disturbs the status quo), can function as a micro-foundation for institutional logic shifts. An entrepreneurship model of dominant logic shifts therefore reveals how crises induce sensemaking activities that can influence shifts in how actor’s see the world, which in turn motivates the pursuit of entrepreneurial opportunities. Significant disruptions, such as environmental jolts, have a triggering effect in enabling individuals to problematize previously held beliefs and logics, allowing them to temporarily “step out of” status quo institutional logics. With prior beliefs and logics problematized, individual decision-makers become open to seeing new interpretations of their surroundings as it is and as it could be. Therefore, actors shift their dominant way of seeing the world (e.g., dominant logic) and then enact this new logic through ventures that, ultimately, can shift and alter institutional-level logics. Therefore, an entrepreneurship model of dominant logic shifts serves as an explanation for how broader institutional logics may shift as a result of the interaction between entrepreneurial action and the environment following a major disruption.
Article
Minority Employees’ Ethnic Identity in the Workplace
Nasima M. H. Carrim
With an increase in the number of diverse groups of individuals (including ethnic minorities) entering organizations, managing diversity in the 21st-century workplace has become imperative. The workplace provides employees with opportunities to work interactively with others in diverse situations and to express their identities, including ethnic identity. Despite Western-based organizations’ adoption of strategies such as affirmative action in an effort to integrate diverse employees into their workplaces, members of ethnic minority groups may still experience great difficulties in obtaining instrumental and social support in these organizations. While some minorities may not outwardly manifest their ethnicity, in the majority of cases, ethnic identity forms a core identity of many individuals and employees do not leave this identity at the doorstep of the organization. In some countries, ethnic minorities have refused to assimilate into the majority workplace culture, and have maintained strong ethnic identities. By outwardly expressing their identities, ethnic minority employees face discrimination, stereotyping and micro-aggressive behaviors within the workplace, and in the majority of cases are relegated to dead-end lower level posts and face barriers to their career advancement. Also, having strong ethnic identities results in a conflict between minorities ethnic identities and the workplace culture. This is especially apparent in terms of religious beliefs and values. Embracing ethnic identity of migrants into organizational cultures is especially challenging for organizations these days, as many immigrants are highly skilled professionals that enter western corporations. They experience discrimination and not receiving support in order to advance their careers.
Article
Missing Data in Research
Hettie A. Richardson and Marcia J. Simmering
Nonresponse and the missing data that it produces are ubiquitous in survey research, but they are also present in archival and other forms of research. Nonresponse and missing data can be especially problematic in organizational contexts where the risks of providing personal or organizational data might be perceived as (or actually) greater than in public opinion contexts. Moreover, nonresponse and missing data are presenting new challenges with the advent of online and mobile survey technology. When observational units (e.g., individuals, teams, organizations) do not provide some or all of the information sought by a researcher and the reasons for nonresponse are systematically related to the survey topic, nonresponse bias can result and the research community may draw faulty conclusions. Due to concerns about nonresponse bias, scholars have spent several decades seeking to understand why participants choose not to respond to certain items and entire surveys, and how best to avoid nonresponse through actions such as improved study design, the use of incentives, and follow-up initiatives. At the same time, researchers recognize that it is virtually impossible to avoid nonresponse and missing data altogether, and as such, in any given study there will likely be a need to diagnose patterns of missingness and their potential for bias. There will likewise be a need to statistically deal with missing data by employing post hoc mechanisms that maximize the sample available for hypothesis testing and minimize the extent to which missing data obscures the underlying true characteristics of the dataset. In this connection, a large body of programmatic research supports maximum likelihood (ML) and multiple imputation (MI) as useful data replacement procedures; although in some situations, it might be reasonable to use simpler procedures instead. Despite strong support for these statistical techniques, organizational scholars have yet to embrace them. Instead they tend to rely on approaches such as listwise deletion that do not preserve underlying data characteristics, reduce the sample available for statistical analysis, and in some cases, actually exacerbate the potential problems associated with missing data. Although there are certainly remaining questions that can be addressed about missing data techniques, these techniques are also well understood and validated. There remains, however, a strong need for exploration into the nature, causes, and extent of nonresponse in various organizational contexts, such when using online and mobile surveys. Such research could play a useful role in helping researchers avoid nonresponse in organizational settings, as well as extend insight about how best and when to apply validated missing data techniques.
Article
Moral Disengagement and Organizations
Catherine Hessick
One does not need to look extensively to find examples of organizations behaving unethically in today’s society. With the passage of whistleblower laws and the increased attention to ethical behavior in recent years, many businesses focus on training in order to reduce unwanted behavior. Despite organizations transitioning to more engaging, substantial ethical training programs for their employees, unethical behavior still remains. Moral disengagement, in part, could be the reason. Moral disengagement is when an individual deliberately deactivates their moral self-regulations, allowing the individual to commit unethical acts without shame or guilt.
Moral disengagement has eight mechanisms: moral justification, euphemistic labeling, advantageous comparison, displacement of responsibility, diffusion of responsibility, distortion of the consequences, dehumanization, and attribution of blame. Each of these mechanisms offers insight into why and how moral disengagement operates within individuals. Because an individual’s reasoning can fall into either a single mechanism or a combination of them, measurement tools commonly place each mechanism as a dimension of moral disengagement. Doing so allows the researcher to examine the construct and its relationships more accurately.
The research investigating unethical behavior in organizations is substantial. However, moral disengagement is an antecedent to unethical behavior and not necessarily an unethical act itself. Previous research on moral disengagement often lies within psychology, military science, sociology, and other nonbusiness fields. With the depths of moral disengagement in the workplace still unexplored, scholars have opportunities to contribute research that can help organizations understand moral disengagement, improve ethical training, and potentially curtail employees’ unethical behavior.
Article
Moral Emotion and Intuition in Organizations
Armin Pircher Verdorfer, Martin Fladerer, and Clarissa Zwarg
While traditional approaches have described ethical decision-making in organizations mainly as being the result of rational deliberative thought, a steadily growing body of research indicates that moral decision-making is strongly influenced by moral intuitions and emotions. The moral intuition approach typically has two aspects: the process through which moral intuitions emerge and their content. With regard to the process, moral intuitions represent fast, automatic, evaluative reactions that are emotionally charged. An important tenet of moral intuition research refers to the primacy of intuition—the notion that moral intuitions generally drive moral decision-making. Accordingly, moral intuitions are described as starting points for rational reflection processes that follow later. On this basis, it has also been argued that the interplay of moral intuition and deliberation is malleable. Specifically, the well-formed moral intuitions of experts are thought to differ from the naive moral intuitions of novices. With increasing experience and reflection about the moral issues in one’s experiences, deliberation increasingly enables individuals to shift between intuitions and reasoning and to monitor, test, weigh, and reject both intuitions and reasons. The content of moral intuition refers to the foundations of morality, which are the underlying moral domain, specifying what individuals view as morally right or wrong. The most commonly referenced account in this field, Moral Foundations Theory (MFT), argues that moral intuitions are a function of evolutionarily developed, innate predispositions to master multiple social problems that interact with social and cultural influences. These predispositions, or moral foundations, include care, fairness, loyalty, authority, and sanctity. While empirical work on the role of moral intuition in organizations is still at an early stage, several areas have been identified that may particularly benefit from integrating moral intuition process and content. For instance, the moral intuition perspective can aid the understanding and prevention of processes through which unethical behaviors and practices, such as corruption, may be justified and normalized in organizations. Furthermore, the moral intuition perspective is increasingly used to study the moral leadership process, most notably the link between leader moral foundations and moral leader behaviors, as well as the role of (mis)fit between leader and follower moral foundations. Moral emotions are an inherent element of the moral intuition process and refer to the welfare of others and the promotion of a functioning society. It is thought that individuals experience moral emotions when they or others have violated moral standards. These emotions build the motivational force for moral action and are often placed in five clusters: other‐praising (e.g., gratitude), other‐suffering (e.g., sympathy), other‐condemning (e.g., contempt), self‐condemning (e.g., guilt), and self-approving (e.g., moral pride) moral emotions.
Article
(Multi)Collinearity in Behavioral Sciences Research
Dev K. Dalal
A statistical challenge many researchers face is collinearity (also known as multicollinearity). Collinearity refers to a situation in which predictors - independent variables, covariates, etc. - are linearly related to each other and typically are related strongly enough as to negatively impact one’s statistical analyses, results, and/or substantive interpretations. Collinearity can impact the results of general linear models (e.g., ordinary least squares regression, structural equation modeling) or generalized linear models (e.g., binary logistic regression; Poisson regression). Collinearity can cause (a) estimation/convergence challenges (particularly with iterative estimation methods), (b) inflated standard errors, as well as (c) biased, unstable, and/or uninterpretable parameter estimates. Due to the issues in the results, substantive interpretation of models with collinearity can be inaccurate, sometimes in significant ways (e.g., nonsignificant predictors that are in fact significantly related to the outcome).
In standard linear models, researchers can make use of variance inflation factor (VIF) or tolerance (Tol) indices to detect potential collinearity. Although zero-order correlations may be useful for detecting collinearity in rare instances, most researchers will want to use VIF or Tol to capture the potential for collinearity resulting from linear combinations of predictors. For statistical models that use iterative estimation (e.g., generalized linear models), researchers can turn to condition indices.
Researchers can address collinearity issues in a myriad of ways. This includes basing models on well-developed a priori theoretical propositions to avoid including empirically or conceptually redundant variables in one’s model—this includes the careful and theoretically appropriate consideration of control variables. In addition, researchers can use data reduction techniques to aggregate correlated covariates (e.g., principal components analysis or exploratory factor analysis), and/or use well-constructed and well-validated measurements so as to ensure that measurement of key variables are not related due to construct overlaps.
Article
Multicultural Identities at Work
Yih-Teen Lee and Nana Yaa Gyamfi
Cultural identity, a specific form of social identity that refers to a person’s degree of identification and sense of belonging to a specific cultural group, has been extensively examined as a kind of social identity over the past decades, especially in the fields of migration, cross-cultural psychology, and applied international management. Meanwhile, exposure to settings with different cultures typically triggers a process of acculturation, enabling individuals to develop multicultural identities, whereby people see things from multiple cultural groups’ perspectives, feel at one with the cultural groups, and act according to the norms of those cultural groups. Individual organizational members serve as the conduit by which culture influences and is influenced by organizational life. There exist various forms of multicultural identities with different psychological and behavioral implications on individuals. In terms of plurality, to date, extant studies accumulated extensive knowledge on biculturalism, which focuses on individuals having two distinct cultural identities and how these identities intersect and influence the individual. Beyond biculturalism obtained through birth, ancestry, or immersive foreign experience, individuals may become multicultural by being simultaneously immersed in more than two cultures: a situation common among children of immigrants (i.e., second-generation immigrants), children raised in multicultural households, third culture individuals who spend their formative years outside their passport country, and individuals living within multicultural societies.
A key to understanding multicultural identities is how these multiple identities are structured within individuals. Scholars largely agree that the structural pattern of identities affects the outcomes and degree of synergy among multiple identities. Widely accepted modes of structuring multiple identities include relative strength of identities involved and how multiple identities relate to each other. Scholars have built on these lines of thinking to examine specific forms of multicultural identities and their outcomes. Furthermore, research indicates that multiculturals possess unique identity resources relevant to organizational life, including cognitive strengths, relational capital and belonging, and leadership-related competencies.
Although there is evidence for responsiveness of multicultural identity to situational cues, there are also strong arguments made in favor of the agency of individuals over their multiple identities. The foregoing notwithstanding, individuals with multicultural identities must balance their agentic enactments of identity with societal requirements of legitimacy. In particular, business organizations play a vital role in providing identity workspaces and other enabling factors which legitimize multicultural identities. Additionally, business organizations play the role of balancing power, status and other dynamics between multicultural and non-multicultural members.
Article
Multilevel Theory, Methods, and Analyses in Management
Michael T. Braun, Steve W. J. Kozlowski, and Goran Kuljanin
Multilevel theory (MLT) details how organizational constructs and processes operate and interact within and across levels. MLT focuses on two different inter-level relationships: bottom-up emergence and top-down effects. Emergence is when individuals’ thoughts, feelings, and/or behaviors are shaped by interactions and come to manifest themselves as collective, higher-level phenomena. The resulting higher-level phenomena can be either common, shared states across all individuals (i.e., compositional emergence) or stable, unique, patterned individual-level states (i.e., compilational emergence). Top-down effects are those representing influences from higher levels on the thoughts, feelings, and/or behaviors of individuals or other lower-level units. To date, most theoretical and empirical research has studied the top-down effects of either contextual variables or compositional emerged states. Using predominantly self-report survey methodologies collected at a single time point, this research commonly aggregates lower-level responses to form higher-level representations of variables. Then, a regression-based technique (e.g., random coefficient modeling, structural equation modeling) is used to statistically evaluate the direction and magnitude of the hypothesized effects. The current state of the literature as well as the traditional statistical and methodological approaches used to study MLT create three important knowledge gaps: a lack of understanding of the process of emergence; how top-down and bottom-up relationships change over time; and how inter-individual relationships within collectives form, dissolve, and change. These gaps make designing interventions to fix or improve the functioning of organizational systems incredibly difficult. As such, it is necessary to broaden the theoretical, methodological, and statistical approaches used to study multilevel phenomena in organizations. For example, computational modeling can be used to generate precise, dynamic theory to better understand the short- and long-term implications of multilevel relationships. Behavioral trace data, wearable sensor data, and other novel data collection techniques can be leveraged to capture constructs and processes over time without the drawbacks of survey fatigue or researcher interference. These data can then be analyzed using cutting-edge social network and longitudinal analyses to capture phenomena not readily apparent in hierarchically nested cross-sectional research.
Article
National Systems of Innovation
Erik E. Lehmann and Julian Schenkenhofer
The pursuit of economic growth stands out as one of the main imperatives within modern economies. Nevertheless, economies differ considerably in their competitiveness. Theories on the endogeneity of growth agree on the value of knowledge creation and innovativeness to determine a country’s capability to achieve a sustained performance and to adapt to the dynamics of changing environments and faster information flows. To this effect, national institutional regimes shape nation-specific contexts and embed individuals and firms. The resulting incentive structures shape the attitudes and behavior of individuals and firms alike, whose interactions contribute to the accumulation and flow of knowledge among the nodes of their networks. National systems of innovation (NSIs) therefore embody a concept that aims to analyze the national innovation performance of economies. It rests its rationale in the variation of national institutions that shape the diffusion of technologies through the process of shared knowledge creation and the development of learning routines. Both public and private institutions are thought to interact in a given nation-specific institutional context that essentially affects incentive schemes and resource allocation of the involved economic agents in creating, sharing, distributing, absorbing, and commercializing knowledge. To this effect, public policy plays a key role in the NSI through building bridges between these actors, reducing information asymmetries, and providing them with resources from others within the system. The different actors contributing to the creation and diffusion of knowledge within the system are needed to exchange information and provide the engine for sustained economic growth. Universities, research institutes, companies and the individual entrepreneur are in charge of shaping their economic system in a way that resource and skill complementarities are exploited to the mutual benefit.
Article
Natural Experiments in Business Research Methods
Michael C. Withers and Chi Hon Li
Causal identification is an important consideration for organizational researchers as they attempt to develop a theoretical understanding of the causes and effects of organizational phenomena. Without valid causal identification, insights regarding organizational phenomena are challenging given their inherent complexity. In other words, organizational research will be limited in its scientific progression. Randomized controlled experiments are often suggested to provide the ideal study design necessary to address potential confounding effects and isolate true causal relationships. Nevertheless, only a few research questions lend themselves to this study design. In particular, the full randomization of subjects in the treatment and control group may not be possible due to the empirical constraints. Within the strategic management area, for example, scholars often use secondary data to examine research questions related to competitive advantage and firm performance. Natural experiments are increasingly recognized as a viable approach to identify causal relationships without true random assignment. Natural experiments leverage external sources of variation to isolate causal effects and avoid potentially confounding influences that often arise in observational data. Natural experiments require two key assumptions—the as-if random assignment assumption and the stable unit treatment value assumption. When these assumptions are met, natural experiments can be an important methodological approach for advancing causal understanding of organizational phenomena.
Article
Necessary Condition Analysis (NCA) and Its Diffusion
Jan Dul
Necessary condition analysis (NCA) understands cause–effect relations in terms of “necessary but not sufficient.” This means that without the right level of the cause, a certain effect cannot occur. This is independent of other causes; thus, the necessary condition can become a single bottleneck, critical factor, constraint, disqualifier, or so on that blocks the outcome when it is absent. NCA can be used as a stand-alone method or in multimethod research to complement regression-based methods such as multiple linear regression (MLR) and structural equation modeling (SEM), as well as methods like fuzzy set qualitative comparative analysis (fsQCA).
The NCA method consists of four stages: formulation of necessary condition hypotheses, collection of data, analysis of data, and reporting of results. Based on existing methodological publications about NCA, guidelines for good NCA practice are summarized. These guidelines show how to conduct NCA with the NCA software and how to report the results. The guidelines support (potential) users, readers, and reviewers of NCA to become more familiar with the method and to understand how NCA should be applied, as well as how results should be reported.
NCA’s rapid diffusion and broad applicability in the social, technical, and medical sciences is illustrated by the growth of the number of article publications with NCA, the diversity of disciplines where NCA is applied, and the geographical spread of researchers who apply NCA.
Article
The New Public Management and Public Management Studies
Ewan Ferlie
The New Public Management (NPM) is a major and sustained development in the management of public services that is evident in some major countries. Its rise is often linked to broader changes in the underlying political economy, apparent since the 1980s, associated with the rise of the New Right as both a political and an intellectual movement. The NPM reform narrative includes the growth of markets and quasi-markets within public services, empowerment of management, and active performance measurement and management. NPM draws its intellectual inspiration from public choice theory and agency theory.
NPM’s impact varies internationally, and not all countries have converged on the NPM model. The United Kingdom is often taken as an extreme case, but New Zealand and Sweden have also been highlighted as “high-impact” NPM states, while the United States has been assessed as a “medium impact” state. There has been a lively debate over whether NPM reforms have had beneficial effects or not. NPM’s claimed advantages include greater value for money and restoring governability to an overextended public sector. Its claimed disadvantages include an excessive concern for efficiency (rather than democratic accountability) and an entrenchment of agency-specific “silo thinking.”
Much academic writing on the NPM has been political science based. However, different traditions of management scholarship have also usefully contributed in four distinct areas: (a) assessing and explaining performance levels in public agencies, (b) exploring their strategic management, (c) managing public services professionals, and (d) developing a more critical perspective on the resistance by staff to NPM reforms.
While NPM scholarship is now a mature field, further work is needed in three areas to assess: (a) whether public agencies have moved to a post-NPM paradigm or whether NPM principles are still embedded even if dysfunctionally so, (b) the pattern of the international diffusion of NPM reforms and the characterization of the management knowledge system involved, and (c) NPM’s effects on professional staff working in public agencies and whether such staff incorporate, adapt, or resist NPM reforms.
Article
New Venture Legitimacy
Greg Fisher
Starting an entrepreneurial endeavor is an uncertain and ambiguous project. This uncertainty and ambiguity make it difficult for entrepreneurs to generate much needed resources and support. In order to address this difficulty, a new venture needs to establish legitimacy, which entails being perceived as desirable, proper, or appropriate within the socially constructed system of norms, values, beliefs, and definitions within which it operates. New venture legitimacy is generated from various sources and hence has three broad dimensions—a cognitive, a moral, and a pragmatic dimension. The cognitive dimension accounts for the extent to which the activities and purpose of a venture are understood by key audiences and how knowledge about that venture spreads. The moral dimension reflects the extent to which a venture is perceived to be doing the right thing. The pragmatic dimension accounts for the extent to which a new venture serves the interests of critical constituents. All three of these dimensions factor into a legitimacy assessment of a new venture. Legitimacy is important for new ventures because it helps them overcome their liabilities of newness, allowing them to mobilize resources and engage in transactions, thereby increasing their chances of survival and success.
Although legitimacy matters for almost all new ventures, it is most critical if an entrepreneur engages in activities that are new and novel, such as establishing a new industry or market or creating a new product or technology. In these circumstances, it is most important for entrepreneurs to strategically establish and manage a new venture’s legitimacy. The strategic establishment and management of new venture legitimacy may entail arranging venture elements to conform with the existing environment, selecting key environments in which to operate, manipulating elements of the external environment to align with venture activities, or creating a whole new social context to accommodate a new venture. Enacting each of these new venture legitimation strategies may necessitate employing identity, associative, and organizational mechanisms. Identity mechanisms include cultural tools and identity claims such as images, symbols, and language by entrepreneurs to enhance new venture legitimacy. Associative mechanisms reflect the formation of relationships and connections with other individuals and entities to establish new venture legitimacy. Organizational mechanisms account for manipulating the organization and structure of a new venture and the achievement of success measures by that venture to attain legitimacy. Ultimately all of this is done so that various external parties, with different logics and perspectives, will evaluate a new venture as legitimate and be prepared to provide that venture with resources and support.
Article
Online Communities and Knowledge Collaborations
Samer Faraj and Takumi Shimizu
Online communities (OCs) are emerging as effective spaces for knowledge collaboration and innovation. As a new form of organizing, they offer possibilities for collaboration that extend beyond what is feasible in the traditional hierarchy. OC participants generate new ideas, talk about knowledge, and remix and build on each other’s contributions on a massive scale. OCs are characterized by fluidity in the resources that they draw upon, and they need to manage these tensions in order to sustain knowledge collaboration generatively. OCs sustain knowledge collaboration by facilitating both tacit and explicit knowledge flows. Further, OCs play a key role in supporting and sustaining the knowledge collaboration process that is necessary for open and user innovation. As collective spaces of knowledge flows, OCs are mutually constituted by digital technologies and participants. The future is bright for OC research adopting the knowledge perspective and focusing on how to sustain their knowledge flow.
Article
Open Innovation
Jennifer Kuan
Open Innovation, published in 2003, was a ground-breaking work by Henry Chesbrough that placed technology and innovation at the center of attention for managers of large firms. The term open innovation refers to the ways in which firms can generate and commercialize innovation by engaging outside entities. The ideas have attracted the notice of scholars, spawning annual world conferences and a large literature in technology and innovation management (including numerous journal special issues) that documents diverse examples of innovations and the often novel business models needed to make the most of those innovations. The role of business models in open innovation is the focus of Open Business Models, Chesbrough’s 2006 follow-up to Open Innovation. Managers have likewise flocked to Chesbrough’s approach, as the hundreds of thousands of hits from an online search using the term open innovation can attest. Surveys show that the majority of large firms were engaging in open innovation practices in 2017, compared to only 20% in 2003 when Open Innovation was published.
Article
Optimal Distinctiveness
Karl Taeuscher
Optimal distinctiveness research is a rapidly growing area of scholarship that integrates key theoretical insights from strategic management and institutional theory. Strategic management research highlights differentiation as a key driver of competitive advantage and superior performance, while institutional theory emphasizes conformity as a central driver of organizational legitimacy and resource acquisition. Optimal distinctiveness research synthesizes these two perspectives and explores the tension that arises from conflicting pressures for differentiation (distinctiveness) and conformity (similarity). This emerging body of research departs from traditional positioning research in strategic management—which primarily explored corporations’ strategic positions within mature industries—by attending to a variety of competitive settings (e.g., newly emerging market categories, online marketplaces), forms of differentiation (e.g., based on product features, narratives, or category affiliations), levels of analysis (e.g., business level, product level), and performance outcomes (e.g., customer demand, resource acquisition, audience evaluations). By advancing understanding about a broad array of phenomena, optimal distinctiveness research has profound implications for strategic management, entrepreneurship, and organization studies.
A central concern in this rapidly growing body of research is to understand how positions on the continuum between similarity and distinctiveness affect performance outcomes. Early optimal distinctiveness research showed that organizations often benefit from positioning themselves near the middle of this continuum, where the relationship between their distinctiveness and performance resembles an inverted U-shape. Over time, however, scholars spotlighted various contingencies that shape the distinctiveness–performance relationship, pointing to important boundary conditions under which organizations derive the most desirable (i.e., optimal) outcomes through low or high levels of distinctiveness. Extant research also shows that organizations can strategically alleviate the tension between differentiation and conformity by, for instance, buffering legitimacy in alternative ways or by differentiating on dimensions that do not impose any related conformity pressures. Scholarship further explores the sources of heterogeneity in organizations’ distinctiveness, including the conditions and strategic considerations that lead organizations to pursue distinctiveness. Toward this aim, extant research particularly emphasizes organizations’ strategic efforts to attain optimal distinctiveness through storytelling and other symbolic forms of differentiation and conformity. Collectively, these explorations help to understand why, when, and how organizations pursue distinctiveness and how distinctiveness shapes varied performance outcomes.
Article
Organizational Happiness
Howard Harris
Organizational happiness is an intuitively attractive idea, notwithstanding the difficulty of defining happiness. A preference for unhappiness rather than happiness in an organization would be out of tune with community expectations in most societies, as would an organization that promoted unhappiness. Some argue that organizational happiness is a misconception, that happiness is a personality trait and organizations cannot have personality. Others suggest that organizational happiness is derived from, or at least dependent on, the happiness of the individuals in the organization. A third approach involves virtue ethics, linking organizational happiness to virtuous organizations. Some discussion of the nature of happiness is needed before consideration of these three approaches to the concept of organizational happiness. If one leaves aside the notion of happiness as a psychological state, there remain three main views as to the nature of happiness: one based on a hedonistic view, which grounds happiness in pleasure, one based on the extent to which desire is satisfied, and one where happiness is linked to a life of virtuous activity and the fulfillment of human potential. Some would see no distinction between all three senses of happiness and what is called well-being.
Whether or not organizations can experience happiness is to some extent determined by whether happiness is considered subjective well-being, fulfilled desire, or virtue and to some extent by one’s view of the moral nature of corporations. There are dangers in the unfettered pursuit of happiness. Empirical research is impacted by questions of definition, by changes over time for both individuals and society, and by the difficulty that arises from reliance on self-reported data. Recent decades have seen the publication of quantitative assessments of organizational happiness, despite the difficulty of constructing scales and manipulating data, and the problems of effectively taking into account cultural, organizational, and individual differences in concepts of happiness. Potential research questions fall into two groups, those that seek a better understanding of what happiness is and those that seek to collect data about happiness in pursuit of answers to questions about the benefits of happiness.
Article
Organizational Innovation
Fariborz Damanpour
Innovation is a complex construct and overlaps with a few other prevalent concepts such as technology, creativity, and change. Research on innovation spans many fields of inquiry including business, economics, engineering, and public administration. Scholars have studied innovation at different levels of analysis such as individual, group, organization, industry, and economy. The term organizational innovation refers to the studies of innovation in business and public organizations.
Studies of innovations in organizations are multidimensional, multilevel, and context-dependent. They investigate what external and internal conditions induce innovation, how organizations manage innovation process, and in what ways innovation changes organizational conduct and outcome. Indiscreet application of findings from one discipline or context to another, lack of distinction between generating (creating) and adopting (using) innovations, and likening organizational innovation with technological innovation have clouded the understanding of this important concept, hampering its advancement. This article organizes studies of organizational innovation to make them more accessible to interested scholars and combines insights from various strands of innovation research to help them design and conduct new studies to advance the field.
The perspectives of organizational competition and performance and organizational adaptation and progression are introduced to serve as platforms to position organizational innovation in the midst of innovation concepts, elaborate differences between innovating and innovativeness, and decipher key typologies, primary sets of antecedents, and performance consequences of generating and adopting innovations. The antecedents of organizational innovation are organized into three dimensions of environmental (external, contextual), organizational (structure, culture), and managerial (leadership, human capital). A five-step heuristic based on innovation type and process is proposed to ease understanding of the existing studies and select suitable dimensions and factors for conducting new studies. The rationale for the innovation–performance relationship in strands of organizational innovation research, and the employment of types of innovation and performance indicators, is articulated by first-mover advantage and performance gap theory, in conjunction with the perspectives of competition and performance and of adaptation and progression. Differences between effects of technological and nontechnological innovation and stand-alone and synchronous innovations are discussed to articulate how and to what extent patterns of the introduction of different types of innovation could contribute to organizational performance or effectiveness. In conclusion, ideas are proposed to demystify organizational innovation to allure new researches, facilitate their learning, and provide opportunities for the development of new studies to advance the state of knowledge on organizational innovation.
Article
Organizational Learning and Adaptation
Henrich R. Greve
Organizational learning theory is motivated by the observation that organizations learn by encoding inferences from experience into their behavior. It seeks to answer the questions of what kinds of experiences influence behaviors, how and under what circumstances behaviors change, and how new behaviors are stabilized and have consequences for organizations’ adaptation to their environment. Organizational learning research has as key mechanisms innovations and other triggering events that lead to major behavioral change, knowledge accumulation and experimentation that encourage incremental change, and interpretations that guide each of these processes. Organizational learning research has gained a central position in organizational theory because it has implications for organizational behaviors that also affect other theoretical perspectives such as institutional theory, organizational ecology, and resource dependence.
Key research topics in organizational learning and adaptation are (a) organizational routines and their stability and change, (b) performance feedback and its consequences for organizational search and change, (c) managerial goal formation and coalition building, (d) managerial attention to goals and organizational activities, and (e) adaptive consequences of learning procedures. Each of these topics has seen significant research, but they are far from completing their empirical agenda. Recently, organizational learning research has been very active, especially on the topics of routines, performance feedback, and attention, resulting in a strong increase in learning and adaptation research in management journals.
