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This paper aims to propose an integrated methodology for evaluating academic spin-offs (ASOs) for supporting both the development phase and performance evaluation. The ASOs have peculiar characteristics compared to other start-up companies and the debate on their evaluation is still open.

The proposed methodology, adopting a lean approach, faces the typical problems that characterize the growth of an ASO: the excessive attention to the technological aspects with respect to the commercial and managerial ones; and the need for evaluation systems that try to evaluate all risk areas and to highlight any misalignment. The methodology was built also starting from the results of an Erasmus + research project, co-funded by the European Commission, called spin-off lean acceleration.

The methodology proposes to monitor the main risk areas (market, technological, implementation, governance and financial). For each of these areas, at first, a framework and a checklist are proposed for supporting the qualitative assessment of the potential of each areas. In the second part, a set of metrics for monitoring the performances and to understand if the spinoff is developing in the right direction is proposed. Moreover, the methodology was applied to the spin-offs at the University of Calabria (Italy), and the paper reports the first results obtained.

A new canvas model (lean acceleration canvas), more specific and suited to the context of ASOs, was developed and tested. A lean approach has been adopted also for understanding the weakness of traditional methods. The proposed methodology could be used by the technology transfer offices in their institutional activity of supporting ASOs.

Given the increased significance and rapid growth of an ageing population, this review paper aims to define the mature consumer segment chronologically to resolve definitional inconsistencies found in prior marketing communications literature, identify the current state of the marketing communications field in terms of mature consumer research and highlight future research directions on mature consumers for marketing communications academics and practitioners.

A synthesis of existing marketing communication research on mature consumers (those aged 50+), published in top-tier journals since 1972, is provided. In total, 106 papers were identified in 21 marketing journals.

Three existing research themes were identified: market segmentation of mature consumers (we ground this theme in three inter-related facets: chronological age, health [physical and neurological] and self-perception of age [also referred to as cognitive age]); attitudes and behaviours of mature consumers; and marketing to mature consumers. This paper also proposes several future research themes: further definition of mature consumers and widening the scope of examination; segmenting mature consumers to account for heterogeneity; information processing of mature consumers cannot use a one-size-fits-all approach; the influence of marketing mix elements on mature consumers; and alternative methodologies to better understand mature consumers.

Recognising the heterogeneity within the chronologically based mature consumer segment, this paper proposes an extended mature consumer definition which includes biological, psychological and social dimensions, as well as life events and life circumstances, rather than biological age alone.

In practical terms, understanding information processing of mature consumers cannot use a one-size-fits-all approach and marketing mix elements may affect behaviour differently within this segment. This will require alternative methodologies to understand these processes fully.

This synthesis of mature consumers research within the marketing communications field provides key research questions for future research to better understand this market segment and its implications for marketing communications, theory development and practice.

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.

Deepfakes are fabricated content created by replacing an original image or video with someone else. Deepfakes have recently become commonplace in politics, posing serious challenges to democratic integrity. The advancement of AI-enabled technology and machine learning has made creating synthetic videos relatively easy. This study explores the role of political brand hate and individual moral consciousness in influencing electorates' intention to share political deepfake content.

The study creates and uses a fictional deepfake video to test the proposed model. Data are collected from N = 310 respondents in India and tested using partial least square–structural equation modelling (PLS-SEM) with SmartPLS v3.

The findings support that ideological incompatibility with the political party leads to political brand hate, positively affecting the electorates' intention to share political deepfake videos. This effect is partially mediated by users' reduced intention to verify political deepfake videos. In addition, it is observed that individual moral consciousness positively moderates the effect of political brand hate on the intention to share political deepfake videos. Intention to share political deepfake videos thus becomes a motive to seek revenge on the hated party, an expression of an individual's ideological hate and a means to preserve one's moral self-concept and strengthen their ideologies and moral beliefs.

The study expands the growing discussion about disseminating political deepfake videos using the theoretical lens of the negative consumer-brand relationship. It validates the effect of political brand hate on irrational behavior that is intended to cause harm to the hated party. Further, it provides a novel perspective that individual moral consciousness may fuel the haters' desire to engage in anti-branding behavior. Political ideological incompatibility reflects ethical reasons for brand hate. Therefore, hate among individuals with high moral consciousness serves to preserve their moral self.

The purpose of this paper is to address the issue of knowledge visualization and its connection with performance measurement from an epistemological point of view, considering quantification and measurement not just as technical questions but showing their relevant implications on the management decision-making of knowledge-based organizations.

This study proposes a theoretical contribution that combines two lines of research for identifying the three main meta-choices problems that arise in the multidimensional benchmarking of knowledge-based organizations. The first is the meta-choice problem related to the choice of the algorithm used (Iazzolino et al., 2012; Laise et al., 2015; Daraio, 2017a). The second refers to the choice of the variables to be included in the model (Daraio, 2017a). The third concerns the choice of the data on which the analyses are carried out (Daraio, 2017a).

The authors show the interplay existing among the three meta-choices in multidimensional benchmarking, considering as key performance indicators intellectual capital, including Human Capital, Structural Capital and Relational Capital, and performances, evaluated in financial and non-financial terms. This study provides an empirical analysis on Italian Universities, comparing the ranking distributions obtained by several efficiency and multi-criteria methods.

This study demonstrates the difficulties of the “implementation problem” in performance measurement, related to the subjectivity of results of the evaluation process when there are many evaluation criteria, and proposes the adoption of the technologies of humility related to the awareness that we can only achieve “satisficing” results.

Resurgent interest in the life and work of the Italian Cambridge economist Piero Sraffa is leading to New Directions in Sraffa Scholarship. This chapter introduces readers to some of these developments. First and perhaps foremost is the fact that as of September 2016 Sraffa’s archival material has been uploaded onto the website of the Wren Library, Trinity College, Cambridge University, as digital colour images; this chapter introduces readers to the history of these events. This history provides sharp relief on the extant debates over the role of the archival material in leading to the final publication of Production of Commodities by Means of Commodities, and readers are provided a brief sketch of these matters. The varied nature of Sraffa scholarship is demonstrated by the different aspects of Sraffa’s intellectual legacy which are developed and discussed in the various entries of our Symposium. The conclusion is reached that we are on the cusp of an exciting phase change of tremendous potential in Sraffa scholarship.

In a previous paper the authors emphasized the advantages of multicriteria methodologies to evaluate business performance. The purpose of this paper is to highlight the metachoice problem that always arises in a benchmark multicriteria analysis that can be synthesized as follows: “how to choose an algorithm to choose?”

In order to perform a benchmark analysis, a set of criteria must be chosen. In the Balanced Scorecard approach, for example, key performance indicators (KPIs) are grouped in four different perspectives: financial, customer, internal processes and learning and growth. In this paper, the authors focus on multicriteria benchmark analysis applied to KPIs of the financial perspective. The paper considers a set of criteria used in financial statement analysis based on balance sheet, income statement and cash flow statement. A case study is described.

The main findings of the paper are when the evaluation of a firm is based on different genuine criteria, a metachoice problem arises: multicriteria ranking algorithms cannot be selected using a multicriteria algorithm; the choice of an algorithm ultimately depends on the subjective preference of the policy maker; and the authors metachoice solution to the benchmarking problem is in accordance with Simon’s satisfacing solution, describing a non-maximizing performance measurement methodology.

The paper provides several practical implications in all cases in which a ranking has to be assigned to a group of firms based on financial performances. More in general the problem is very relevant when a ranking has to be carried out with respect to a set of projects, a set of strategies, a set of organizational units, etc.

The adoption of a set of criteria is certainly an advantage to avoid uni-criterial myopic evaluation. However, this also creates some methodological problems. The paper demonstrates the “relativity” (subjectivity) of results of the evaluation process when there are many evaluation criteria, as in a benchmark context. This is a metachoice problem that cannot be solved by using another multicriteria algorithm.

The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.

This study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.

The resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.

Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.

At a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

The purpose of this paper is to apply PROMETHEE II, a multicriteria decision-making method, to evaluate wineries, aiming to support the tourist’s decision regarding an online recommendation system on which wineries to prioritise visiting.

This study was divided into four phases: literature review, which aimed to identify a list of criteria related to the quality of services offered by wineries in specialised literature; validation of the categories and criteria with specialists in the field of wine tourism using a questionnaire with questions based on the stage of the research; multicriteria evaluation of wineries using PROMETHEE II method which was adjusted to the model through a pre-test with ten specialist professionals from different states (Rio Grande do Sul, Santa Catarina, Paraná, São Paulo and Pernambuco); and validation of the model with one decision-maker representing each winery in Rio Grande do Sul, Brazil, and a decision-making expert who is familiar with all the wineries.

These findings reveal various scenarios based on different criteria that wine tourists may consider, with emphasis on the terroir of the region, extensive tasting, picnics in the vineyards and improvement in the development of wine tourism with a night harvest. The core of wine tourism services is based on 12 dimensions: waiting for the service, ease of purchase, opening hours, price, winery landscape, variety of products, local cuisine, quality of tasted products, access to the winery, tourist facilities, sustainable products and hygiene installation.

This research contributes to the literature by demonstrating the applicability of multicriteria tools to solve wine tourism services based on the tourist perspective of the service. Still, this paper proposes that it be applied to other case studies.